Beyond E. coli and Yeast: Harnessing Non-Model Organisms as Next-Generation Microbial Cell Factories

Abstract

This article explores the paradigm shift from traditional model microbial cell factories to non-model organisms, which offer a treasure trove of unique metabolic capabilities for sustainable biomanufacturing. Aimed at researchers and drug development professionals, it provides a comprehensive framework covering the foundational rationale, advanced engineering methodologies, critical optimization strategies, and rigorous validation techniques required to develop these promising hosts. By integrating insights from synthetic biology, systems metabolic engineering, and techno-economic analysis, this resource serves as a guide for unlocking the potential of non-model microbes to produce high-value chemicals, pharmaceuticals, and materials, thereby advancing the bioeconomy and supporting drug discovery pipelines.

Why Look Beyond Model Organisms? The Untapped Potential of Non-Model Microbes

Defining Non-Model Microbial Cell Factories and the Industrial Chassis Concept

The transition from a fossil-fuel-based economy to a sustainable, bio-based circular economy represents one of the most critical challenges of the 21st century. This shift requires fundamentally rethinking industrial production processes, with microbial cell factories emerging as key enabling technologies. While traditional biotechnology has relied heavily on a handful of model microorganisms, recent advances are driving a paradigm shift toward non-model microbes – organisms with unique, advantageous traits that make them superior candidates for specific industrial applications. These non-model microbes, when systematically engineered into specialized microbial chassis, offer unprecedented opportunities to overcome the limitations of conventional production strains and meet the demanding requirements of industrial bioprocesses [1] [2].

The concept of a microbial chassis refers to an "engineerable and reusable biological platform with a genome encoding several basic functions for stable self-maintenance, growth, and optimal operation but with the tasks and signal processing components growingly edited for strengthening performance under pre-specified environmental conditions" [1] [2]. This technical guide explores the fundamental principles, engineering methodologies, and practical applications of non-model microbial cell factories, providing researchers and industrial scientists with a comprehensive framework for developing next-generation bioproduction platforms.

Definition and Rationale: Non-Model vs. Model Microbial Systems

Conceptual Framework and Definitions

Model microorganisms such as Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, and Corynebacterium glutamicum are characterized by their well-annotated genomes, extensive molecular toolkits, and deep understanding of their metabolic and regulatory networks. These organisms have served as workhorses for fundamental research and commercial production for decades. However, their widespread use has revealed significant limitations, including suboptimal growth characteristics, limited substrate ranges, sensitivity to harsh process conditions, and insufficient tolerance to high substrate and product concentrations [1] [3].

In contrast, non-model microorganisms are defined by their relative undercharacterization and the limited availability of genetic tools, despite often possessing exceptional physiological and metabolic capabilities. The term "non-model model organisms" has emerged to describe systems that are "models in the original sense (convenient for the study of a biological process) but not in the newer sense (possessing infrastructure and resources)" [4]. These organisms represent the overwhelming majority of microbial biodiversity and constitute a vast reservoir of untapped biocatalytic potential [4] [5].

Comparative Advantages of Non-Model Systems

Table 1: Comparative Analysis of Model vs. Non-Model Microbial Platforms

Characteristic	Model Microorganisms	Non-Model Microorganisms
Genetic Tools	Extensive toolkit available	Limited, often requires development
Metabolic Understanding	Well-characterized networks	Limited characterization
Database Resources	Comprehensive omics databases	Sparse data availability
Industrial Robustness	Often limited	Frequently inherent (e.g., stress tolerance)
Substrate Range	Typically narrow	Often broad or specialized
Metabolic Diversity	Limited	Extensive, novel pathways
Engineering Timeline	Rapid	Longer development cycle
Regulatory Status	Often established	May require new approvals

Non-model microbes offer several compelling advantages as industrial chassis cells. Many possess innate resilience to extreme conditions such as high temperature, pH extremes, solvent toxicity, and osmotic stress – characteristics that are difficult to engineer into model systems [3]. Furthermore, non-model organisms often harbor unique metabolic pathways capable of producing specialized compounds or utilizing inexpensive, non-food feedstocks such as lignocellulose, glycerol, and C1 compounds (CO2, CO, methane, methanol) [6] [3].

Examples of promising non-model chassis include:

• Zymomonas mobilis: An ethanologenic bacterium with high sugar uptake rates, ethanol yield, and tolerance that utilizes the Entner-Doudoroff pathway anaerobically [3]
• Pseudomonas putida: A soil bacterium with remarkable metabolic versatility and tolerance to toxic compounds, useful for lignin valorization [6] [5]
• Halomonas bluephagenesis: Engineered for polyhydroxyalkanoate (PHA) production [3]
• Vibrio natriegens: Noted for extremely fast growth rates [3]
• Acinetobacter baylyi: Characterized by high natural transformation efficiency [3]

Quantitative Assessment of Genome-Reduced Chassis

Genome Reduction as a Chassis Optimization Strategy

Genome reduction has emerged as a powerful strategy for refining non-model microorganisms into efficient industrial chassis. This process involves the systematic removal of "unnecessary" genes and genomic regions to streamline cellular metabolism, improve genetic stability, and enhance predictability and controllability [1] [2]. Two primary approaches dominate the field: the bottom-up approach entails designing and building an artificially synthesized genome (e.g., the JCVI-syn3.0 minimal cell with only 473 genes), while the top-down approach starts from an intact genome and proceeds with targeted deletions [1].

Table 2: Notable Examples of Genome-Reduced Microbial Chassis

Parental Strain	Genome-Reduced Strain	Deletion Targets	Deletion Size	Resulting Characteristics
Bacillus subtilis 168	MGB874	Prophages, secondary metabolic genes, non-essential genes	814 kb (20.7%)	Decreased growth rate, 1.7-fold increase in cellulase and 2.5-fold protease production, no sporulation [2]
Bacillus subtilis 168	PG10	Sporulation, motility, secondary metabolism, prophages, proteases	1.46 Mb (34.6%)	Decreased growth rate, reduced glycolytic flux, improved production of difficult-to-express proteins [2]
Bacillus amyloquefaciens LL3	GR167	Genomic islands, extracellular polysaccharide genes, prophages	168 kb (4.2%)	Faster growth, higher transformation efficiency, increased heterologous gene expression [2]
Streptomyces albus	Δ15 clusters	Native antibiotic gene clusters	15 clusters deleted	2-fold higher production of heterologous biosynthetic gene clusters [1]
E. coli	IS-free strain	Insertion sequences	Variable	25% and 20% increased production of TRAIL and BMP2 recombinant proteins [1]

Benefits of Genome Reduction

Substantial evidence demonstrates that strategic genome reduction can yield multiple beneficial effects on chassis performance:

• Enhanced Genetic Stability: Removal of mobile genetic elements (prophages, insertion sequences) and error-prone DNA polymerases reduces spontaneous mutation rates and prevents product inactivation [1]. For instance, deletion of error-prone DNA polymerases in E. coli resulted in a 50% decrease in spontaneous mutation rate [1].

• Improved Product Yields: Eliminating competitive pathways and simplifying metabolic backgrounds can significantly increase target product formation. In Streptomyces lividans, deletion of 10 endogenous antibiotic clusters led to a 4.5-fold increase in production of the heterologously expressed compound deoxycinnamycin [1].

• Increased Substrate Conversion Efficiency: Reducing metabolic "burden" by deleting non-essential genes can redirect cellular resources toward product synthesis, potentially lowering operating costs for DNA, RNA, and protein synthesis [1].

• Higher Transformation Efficiency: Removal of restriction-modification systems and other DNA defense mechanisms can facilitate genetic engineering [2].

Engineering Workflows for Chassis Development

Comprehensive Chassis Development Pipeline

The transformation of a promising non-model microbe into an industrial-grade chassis requires a systematic, multi-stage approach. The following diagram illustrates the integrated workflow encompassing key stages from discovery to application:

Critical Stages in Chassis Development

Host Selection Criteria

Selecting appropriate non-model hosts requires careful consideration of multiple factors:

• Inherent Physiological Traits: Stress tolerance, growth characteristics, oxygen requirements, and nutritional needs [6]
• Metabolic Capabilities: Native substrate utilization range, existing biosynthetic pathways, and precursor availability [3]
• Genetic Tractability: Natural competence, existing transformation methods, and CRISPR-Cas systems [3] [4]
• Industrial Relevance: Scalability, regulatory status, and safety profile [1] [7]
• Bioprocess Compatibility: Compatibility with fermentation systems and downstream processing requirements [6]

Genomic Characterization and Tool Development

Comprehensive genome sequencing and annotation provide the foundational knowledge for chassis engineering. Essential components include:

• High-Quality Genome Assembly: Resolving repetitive regions and secondary metabolite gene clusters [3]
• Functional Annotation: Identifying metabolic pathways, regulatory elements, and non-essential regions [1]
• Mobile Genetic Element Mapping: Locating prophages, insertion sequences, and genomic islands for targeted deletion [1] [2]

Concurrently, genetic tool development must establish:

• Transformation Methods: Electroporation, conjugation, or other DNA delivery techniques [4]
• Expression Systems: Constitutive and inducible promoters, ribosome binding sites, and plasmid vectors [3]
• Gene Editing Tools: CRISPR-Cas systems, recombinase systems, and counter-selection markers [3] [4]

Metabolic Model Construction and Refinement

Genome-scale metabolic models (GEMs) are indispensable computational tools for guiding chassis design. The iterative process of model construction and validation has been demonstrated in organisms like Zymomonas mobilis, where enzyme-constrained models (e.g., eciZM547) provided superior predictions of metabolic flux distributions compared to traditional stoichiometric models [3]. Key steps include:

• Draft Model Reconstruction: Using automated tools based on genome annotation
• Manual Curation: Incorporating organism-specific biochemical knowledge
• Constraint Integration: Adding enzyme kinetics, thermodynamics, and regulatory constraints
• Experimental Validation: Using 13C-flux analysis and chemostat cultivation data [3]

Pathway Engineering Strategies for Product Diversification

Pathway Design and Implementation Frameworks

Engineering metabolic pathways in non-model chassis involves distinct strategic approaches based on the relationship between the target product and the host's native metabolism:

Case Study: Overcoming Dominant Metabolism in Zymomonas mobilis

The development of Z. mobilis as a platform for non-ethanol products illustrates the innovative strategies required to overcome dominant native metabolism. Researchers implemented a Dominant-Metabolism Compromised Intermediate-Chassis (DMCI) strategy, which involved:

• Pathway Introduction: Introducing a low-toxicity but cofactor-imbalanced 2,3-butanediol pathway to deliberately create metabolic conflict with the native ethanol production pathway [3]

• Adaptive Evolution: Allowing the strain to adapt to this metabolic burden and rewire its regulatory networks [3]

• Product Switching: Subsequently engineering the adapted intermediate chassis for high-yield D-lactate production [3]

This approach yielded remarkable results, with recombinant producers achieving:

• >140.92 g/L D-lactate from glucose
• >104.6 g/L D-lactate from corncob residue hydrolysate
• Yield >0.97 g/g glucose [3]

Techno-economic analysis and life cycle assessment confirmed the commercial feasibility and greenhouse gas reduction capability of this lignocellulosic D-lactate production process [3].

Essential Research Reagents and Methodologies

Critical Research Reagent Solutions

Table 3: Essential Research Reagents for Non-Model Chassis Development

Reagent Category	Specific Examples	Function/Application
Genetic Editing Tools	CRISPR-Cas12a, endogenous Type I-F CRISPR-Cas, MMEJ repair systems [3]	Precise genome editing in non-model systems
Bioinformatics Databases	KEGG, MetaCyc, BRENDA, ModelSeed, BiGG [3] [8]	Pathway discovery, enzyme kinetics, metabolic reconstruction
Metabolic Modeling Software	ECMpy, AutoPACMEN, FBA, MDF analysis tools [3] [6]	Constraint-based modeling, enzyme constraint integration, flux prediction
Expression Components	Native constitutive and inducible promoters, RBS libraries, plasmid vectors [3]	Heterologous gene expression, pathway optimization
Analytical Standards	13C-labeled metabolites for flux analysis [3]	Experimental validation of metabolic models
Cultivation Media	Defined media for omics analysis, stress tolerance assays [1] [3]	Physiological characterization, industrial condition simulation

Experimental Protocol: Genome Reduction via Sequential Deletion

A standardized protocol for top-down genome reduction in non-model bacteria:

• Target Identification Phase:

  • Analyze genome sequence for mobile genetic elements (prophages, insertion sequences)
  • Identify secondary metabolite clusters and non-essential genomic regions through comparative genomics
  • Predict essential genes using transposon mutagenesis or bioinformatics tools

• Deletion Strategy Design:

  • Design 1-2 kb flanking homology arms for each target region
  • Incorporate selectable markers (antibiotic resistance) and/or counter-selection markers (sacB, rpsL)
  • Plan sequential deletion order from largest to smallest regions

• Implementation Phase:

  • Construct deletion cassettes via PCR or synthesis
  • Transform using established methods (electroporation, conjugation)
  • Verify deletions via colony PCR and sequencing
  • Remove selection markers when using marker-recycling systems

• Phenotypic Validation:

  • Assess growth characteristics in minimal and complex media
  • Evaluate genetic stability through serial passage
  • Measure transformation efficiency and productivity metrics [1] [2]

The strategic development of non-model microbial cell factories represents a frontier in industrial biotechnology with transformative potential. By leveraging natural biodiversity and applying systematic engineering principles, researchers can create specialized chassis cells optimized for specific production requirements. The integration of genome reduction, systems biology, synthetic biology, and automated strain engineering approaches will continue to accelerate the development timeline for these platforms.

Future advances will likely focus on several key areas: (1) AI-driven prediction of gene essentiality and metabolic pathway design; (2) high-throughput genome editing and screening methodologies; (3) integration of techno-economic analysis and life cycle assessment at early development stages; and (4) expansion of non-model chassis to utilize C1 feedstocks and complex waste streams [6] [9]. As these technologies mature, non-model microbial cell factories will play an increasingly central role in establishing a sustainable, bio-based economy.

Microbial cell factories are a cornerstone of industrial biotechnology, enabling the sustainable production of chemicals, pharmaceuticals, and materials. For decades, traditional model organisms—Escherichia coli, Saccharomyces cerevisiae, and Corynebacterium glutamicum—have served as the primary workhorses in both academic research and industrial biomanufacturing [10]. Their established genetic tools, well-annotated genomes, and extensive experimental knowledge have made them the default choices for metabolic engineering. However, as the field advances toward more complex and specialized production demands, the innate limitations of these chassis strains become increasingly apparent. These constraints often necessitate extensive engineering efforts to achieve competitive production metrics for non-native compounds.

Framed within the burgeoning context of non-model organisms as microbial cell factories, this review critically examines the specific technical limitations of these traditional workhorses. We move beyond a superficial comparison to provide a detailed analysis of their metabolic, genetic, and physiological constraints, supported by quantitative data and experimental evidence. Understanding these limitations is crucial for rational host selection and drives the development of next-generation chassis with native advantageous traits for specific bioprocesses.

Metabolic Limitations and Network Complexity

Constrained Metabolic Capacity and Yield Inefficiencies

The metabolic network of an organism fundamentally determines its capacity to produce a target compound. While traditional workhorses possess versatile core metabolisms, their innate pathways are often suboptimal for producing many high-value chemicals, leading to inherent yield limitations and redox imbalances.

Table 1: Maximum Achievable Yields (YA) of Selected Chemicals in Traditional Workhorses Calculated under aerobic conditions with D-glucose as the carbon source [10]

Target Chemical	Host Strain	Maximum Achievable Yield (mol/mol Glucose)	Key Limiting Factor
L-Lysine	S. cerevisiae	0.8571	Different pathway (L-2-aminoadipate) vs. bacteria
	B. subtilis	0.8214	Diaminopimelate pathway efficiency
	C. glutamicum	0.8098	Diaminopimelate pathway efficiency
	E. coli	0.7985	Diaminopimelate pathway efficiency
	P. putida	0.7680	Diaminopimelate pathway efficiency
L-Glutamate	C. glutamicum	Industrial Producer	Specialized secretion trigger required
Shikimate (SA)	C. glutamicum	141 g/L (resting cells) [11]	Precursor (PEP) availability, feedback regulation

A comprehensive evaluation of metabolic capacities revealed that for more than 80% of 235 bio-based chemicals analyzed, fewer than five heterologous reactions were needed to construct functional biosynthetic pathways in traditional hosts [10]. However, a weak but significant negative correlation exists between the length of a biosynthetic pathway and its maximum theoretical yield, underscoring the systemic burden of introducing complex heterologous routes [10]. Furthermore, central carbon metabolism precursors like phosphoenolpyruvate (PEP) and erythrose-4-phosphate (E4P) are often limiting in strains like E. coli, requiring extensive engineering of substrate uptake and glycolytic pathways to overcome this bottleneck, as demonstrated in the high-yield production of shikimate in C. glutamicum [11].

Unrealistic Predictions from Genome-Scale Models

Genome-scale metabolic models (GEMs) are indispensable tools for predicting metabolic behavior. However, when applied to large-scale models of traditional workhorses, they frequently generate biologically unrealistic predictions. A key limitation is the prediction of unphysiological metabolic bypasses that function in silico but not in living cells due to undefined thermodynamic, kinetic, or regulatory constraints [12] [13]. This often occurs during in silico gene knockout design, where GEMs may suggest non-functional solutions that must be manually filtered out [12].

The size and complexity of genome-scale models (e.g., iML1515 for E. coli contains 1,877 metabolites and 2,712 reactions) make them difficult to visualize and interpret, limiting the application of more advanced modeling frameworks like kinetic modeling or thermodynamics-based flux analysis [12] [13]. To address this, compact, manually curated models like iCH360 for E. coli have been developed. These "Goldilocks-sized" models strike a balance by focusing on central energy and biosynthetic metabolism, enabling more reliable analysis and simulation while retaining biological relevance [12].

Figure 1: GEM limitations and the compact model solution. Genome-scale models can predict unrealistic metabolism; smaller, curated models address this [12] [13].

Host-Pathway Compatibility and Metabolic Burden

Hierarchical Compatibility Issues

Introducing synthetic pathways into a host chassis often creates significant conflicts, categorized into four hierarchical levels of incompatibility: genetic, expression, flux, and microenvironment [14]. These mismatches arise because biological systems have robust regulatory mechanisms to maintain homeostasis, which are disrupted by heterologous pathways.

• Genetic Instability: Heterologous genes can be unstable due to mutation or deletion, especially if they impose a burden. Plasmid-based expression requires continuous antibiotic selection, which is undesirable industrially [14].
• Expression Incompatibility: Differences in codon usage, GC content, and promoter strength between donor and host can lead to poor expression, misfolded proteins, and low enzyme activity [14].
• Flux Imbalance: Synthetic pathways often lack the native regulatory circuits of the host, leading to toxic intermediate accumulation, resource competition (e.g., for ATP, cofactors), and metabolic burden that retards cell growth [14]. This is a major hurdle in E. coli and S. cerevisiae engineering.
• Microenvironment Mismatch: The absence of specialized organelles or compartments in bacteria like E. coli can hinder pathways requiring specific pH, redox conditions, or enzyme proximity, which eukaryotes like S. cerevisiae naturally provide [14].

The Challenge of Growth-Production Coupling

A fundamental challenge is the inherent trade-off between cell growth and product synthesis. High-yield production often requires diverting massive resources from biomass formation, creating a strong selective pressure for non-producing mutants that outgrow the producers, undermining long-term process stability [14].

Growth-coupled selection strategies in E. coli, where cell survival is linked to the function of the engineered pathway, help address this [15]. While effective, designing and validating such strains is labor-intensive, requiring careful growth phenotyping across conditions [15]. Conversely, decoupling strategies aim to separate production from growth, but they often rely on complex, multi-layer genetic circuits that can be difficult to implement robustly [14].

Figure 2: Host-pathway mismatches and solutions. Introducing synthetic pathways causes compatibility issues across multiple levels, addressed by compatibility engineering [14].

Physiological and Process-Related Limitations

Substrate Utilization and Stress Tolerance

Industrial bioprocesses often require the utilization of complex, low-cost feedstocks like lignocellulosic hydrolysates or waste gases, and operation under harsh conditions. Traditional workhorses often lack the native capacity to thrive in these settings.

• Narrow Substrate Range: E. coli and C. glutamicum are primarily geared toward simple sugars. While engineered strains can co-utilize sugar mixtures, they generally cannot natively metabolize methane, methanol, or complex polymers without extensive engineering [10] [11].
• Product and Substrate Toxicity: The production of aromatic compounds like hydroxybenzoic acids or phenylpropanoids is challenging in most hosts due to cytotoxicity. C. glutamicum exhibits a comparative advantage here, with its mycolic acid-containing outer membrane acting as a permeability barrier, granting it high tolerance to such compounds [11]. E. coli often requires efflux pump engineering to mitigate product toxicity.
• Osmotic and Thermal Stress: Fermentation conditions can involve high solute concentrations or temperatures. While S. cerevisiae is relatively robust, many E. coli and C. glutamicum strains require stabilization for industrial-scale bioreactors.

Genomic Instability and Unwanted Byproduct Formation

The genomic plasticity of traditional workhorses can be a double-edged sword. E. coli's genome contains mobile genetic elements and error-prone DNA polymerases that can lead to genomic instability and mutations that inactivate production pathways [1]. Deleting these elements in E. coli has been shown to enhance recombinant protein production by 20-25% and reduce spontaneous mutation rates by 50% [1].

Furthermore, native metabolic networks often compete for precursors, leading to byproduct formation. For example, in S. cerevisiae, ethanol production under aerobic conditions (the Crabtree effect) can divert carbon away from target products. Eliminating such byproducts requires multiple gene knockouts, which can be tedious and sometimes impair the host's fitness.

Experimental Protocols for Characterizing Limitations

Protocol: Quantifying Metabolic Burden and Growth Decoupling

Objective: To measure the impact of a heterologous pathway on host cell growth and quantify the metabolic burden [14].

• Strain Preparation: Construct the production strain harboring the heterologous pathway (on a plasmid or integrated into the genome). Create a control strain with a null or "empty" construct.
• Cultivation: Inoculate triplicate cultures of both production and control strains in minimal medium with the primary carbon source. Use baffled shake flasks in a controlled incubator.
• Growth Kinetics Monitoring: Measure the optical density at 600 nm (OD600) every hour for 12-24 hours. For a more precise assessment of live cell biomass, use flow cytometry (FCM) with a DNA-specific stain at key time points (e.g., mid-exponential phase) [16].
• Data Analysis:
  • Calculate the maximum specific growth rate (μmax) for both strains from the linear region of the ln(OD600) vs. time plot.
  • Determine the maximum biomass yield (g biomass/mol substrate) at stationary phase.
  • Quantify burden as the percentage reduction in μmax or biomass yield of the production strain compared to the control.

Protocol: Determining Maximum Theoretical and Achievable Yields

Objective: To computationally predict the metabolic capacity of a host for a target chemical using a genome-scale model (GEM) [10].

• Model Selection and Curation: Obtain a high-quality GEM for the host organism (e.g., iML1515 for E. coli). Manually curate or add heterologous reactions to form a functional pathway to the target chemical.
• Simulation Setup: Use a constraint-based modeling tool like COBRApy.
  • Set the carbon source uptake rate (e.g., glucose = 10 mmol/gDW/h).
  • Set the oxygen uptake rate according to the condition (aerobic, anaerobic).
  • Define the non-growth-associated maintenance (NGAM) value.
• Yield Calculations:
  • Maximum Theoretical Yield (YT): Perform Flux Balance Analysis (FBA) with the biomass objective function set to zero. The production flux is maximized. YT is the calculated production flux divided by the substrate uptake flux.
  • Maximum Achievable Yield (YA): Perform FBA with the biomass objective function set to a lower bound (e.g., 10% of its maximum value). This ensures minimum growth requirements are met. YA is the resulting production flux divided by the substrate uptake flux.
• Validation: Compare in silico predictions with yields obtained from controlled bioreactor experiments to validate model accuracy.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Key Reagents and Tools for Analyzing and Overcoming Workhorse Limitations

Reagent/Tool	Function/Application	Specific Example / Note
Genome-Scale Model (GEM)	In silico prediction of metabolic flux, yield, and gene knockout targets.	iML1515 (E. coli), iMK735 (S. cerevisiae), iCGB21FR (C. glutamicum) [12] [10].
Compact Metabolic Model	Simplified, curated model for advanced analysis (e.g., EFM, thermodynamics).	iCH360 for E. coli core and biosynthetic metabolism [12] [13].
CRISPR-Cas Tools	Precision genome editing for gene knockouts, repression, and activation.	Enables rapid multiplexed engineering in E. coli, S. cerevisiae, and C. glutamicum [17] [14].
Cellular Internal Standards	Absolute quantification of microbial cells in complex samples via sequencing.	Used with flow cytometry to accurately measure total microbial load and absolute abundance of specific taxa [16].
Enzyme Kinetics Database	Source of kinetic constants (kcat, KM) for constraint-based modeling.	Data used to enrich models like iCH360 for enzyme-constrained FBA [12].
Heterologous Pathway Library	Pre-assembled genetic modules for expressing non-native metabolic pathways.	Accelerates the construction of cell factories for compounds like cannabinoids or opiates [17].
Growth-Coupled Selection Strain	Engineered host whose survival depends on the function of a target pathway.	E. coli strains with essential genes deleted and linked to production pathways [15].
Aldox-D6	Aldox-D6, MF:C19H40N2O, MW:318.6 g/mol	Chemical Reagent
DiBAC4(5)	DiBAC4(5), MF:C29H42N4O6, MW:542.7 g/mol	Chemical Reagent

The limitations of E. coli, S. cerevisiae, and C. glutamicum—spanning metabolic capacity, host-pathway compatibility, and physiological robustness—present significant barriers to the efficient bioproduction of many complex molecules. While advanced metabolic engineering and synthetic biology tools can mitigate some constraints, the extensive "debugging" required is often resource-intensive and host-specific.

This reality underscores the strategic value of exploring non-model microorganisms [1]. These organisms often possess native advantageous traits, such as the innate tolerance to aromatic compounds found in Pseudomonas putida, or the ability to consume C1 substrates (methane, methanol) found in methanotrophs. By developing these natural hosts into platform chassis through genome reduction and tool development, the field can bypass many of the intrinsic limitations of traditional workhorses [1]. The future of microbial cell factories lies in a diverse portfolio of specialized chassis, each optimized for specific feedstocks and target products, ultimately enabling a more efficient and sustainable bio-based economy.

In the pursuit of sustainable biomanufacturing, the development of efficient microbial cell factories is paramount. While traditional model organisms like Escherichia coli and Saccharomyces cerevisiae have been workhorses for decades, they often lack the specialized capabilities required for specific industrial processes [1]. Non-model microorganisms represent a largely untapped resource, possessing unique metabolic repertoires and inherent robustness that make them ideal chassis for the bio-based production of chemicals, materials, and pharmaceuticals [3]. This inherent robustness—encompassing tolerance to harsh process conditions, toxic substrates and products, and genetic stability—is a critical determinant for successful scale-up and commercial viability [18]. This review explores the native advantages of non-model microbes, detailing their unique metabolisms, the molecular basis of their resilience, and the experimental methodologies for harnessing these traits within the context of a circular bioeconomy.

Unique Metabolic Capabilities of Non-Model Organisms

Non-model microbes often possess specialized metabolic pathways that are absent in conventional hosts. These native capabilities can be directly harnessed for biotechnological applications, often requiring fewer genetic modifications and providing higher yields compared to engineered model systems.

Table 1: Notable Non-Model Microorganisms and Their Native Metabolic Capabilities

Organism	Native Metabolic Advantage	Potential Biotechnological Application	Key Feature
Zymomonas mobilis	Entner-Doudoroff (ED) pathway under anaerobic conditions [3]	High-yield bioethanol production [3]	High sugar uptake rate; high ethanol yield and tolerance [3]
Escherichia coli W	Enhanced flavonoid tolerance and efficient sucrose metabolism [19]	Glycosylation of flavonoids to improve solubility and bioavailability [19]	Robustness under high-stress conditions; versatile carbon source utilization [19]
Purple Non-Sulfur Bacteria (PNSB)	Remarkable metabolic versatility (photo-organo-heterotrophy, photo-litho-autotrophy, dark fermentation) [20]	Valorization of agri-food waste into high-value protein, pigments, and vitamins [20]	Near-perfect substrate-to-biomass conversion yield under photoheterotrophy [20]
Streptomyces albus	Native repertoire of antibiotic biosynthetic gene clusters [1]	Production of diverse antibiotics and heterologous natural products [1]	Simplified metabolic background after genome reduction [1]

The Entner-Doudoroff (ED) pathway in Z. mobilis is a prime example of a unique central metabolism. This pathway allows for a higher theoretical yield of ATP and NAD(P)H per glucose molecule compared to the traditional glycolytic pathway found in most model organisms, contributing to its exceptionally high ethanol production rate and yield [3]. Another key advantage is metabolic versatility, as seen in PNSB, which can switch between different metabolic modes (e.g., phototrophy and chemotrophy) to utilize a wide array of inexpensive feedstocks, including volatile fatty acids and sugars from agri-food waste [20]. Furthermore, innate tolerance to toxic compounds, such as E. coli W's resistance to flavonoids, provides a direct advantage for producing molecules that are typically inhibitory to microbial growth, thereby simplifying bioprocess optimization and improving final product titers [19].

Mechanisms of Inherent Robustness

The industrial utility of non-model microbes is not solely dependent on their product formation capacity but is equally defined by their robustness. This resilience manifests as tolerance to various stresses and is underpinned by specific physiological and genetic traits.

Tolerance to Inhibitors and Toxic Products

Many non-model organisms are isolated from extreme environments, having evolved mechanisms to cope with high concentrations of inhibitory compounds. E. coli W demonstrates a natural tolerance to flavonoids, which are often toxic to microbes, allowing for their efficient bioconversion into more soluble glycosylated derivatives without significant growth impairment [19]. This inherent tolerance reduces the metabolic burden associated with engineering defense mechanisms in more sensitive model hosts [21].

Genetic and Phenotypic Stability

Stability over many generations is crucial for large-scale fermentation. Non-model organisms like Z. mobilis can possess stable genome structures, reducing the risk of productivity loss during prolonged cultivation [3]. Genome reduction is a top-down engineering approach that systematically removes non-essential genes, including mobile genetic elements and prophages, to enhance genomic stability. For instance, developing an IS-free E. coli strain reduced random mutations and improved recombinant protein production by 20-25% [1]. This simplification of the genome also lowers the cellular burden of replicating and maintaining DNA, potentially freeing up resources for growth and production [1].

Resistance to Harsh Process Conditions

Industrial bioprocesses often involve fluctuating pH, temperature, and osmolarity. The robustness of non-model hosts like E. coli W under high-stress conditions makes them suitable for diverse bioreactor configurations and complex feedstocks, such as lignocellulosic hydrolysates that contain multiple inhibitors [19] [3].

Table 2: Engineering Strategies to Enhance Robustness in Microbial Chassis

Strategy	Mechanism	Example
Genome Reduction [1]	Deletion of non-essential genes, mobile elements, and pathogenicity islands to improve genetic stability and reduce metabolic burden.	Creation of an IS-free E. coli strain with 25% higher TRAIL production [1].
Dynamic Metabolic Control [18]	Use of biosensors and quorum sensing to autonomously regulate metabolic fluxes, preventing toxic intermediate accumulation.	Dynamic control of FPP in isoprenoid production doubled amorphadiene titer to 1.6 g/L [18].
Decoupling Growth & Production [18]	Separating biomass formation from product synthesis phases to alleviate resource competition.	A nutrient sensor in E. coli delayed vanillic acid production, lowering metabolic burden 2.4-fold [18].
Product Addiction [18]	Coupling essential gene expression to product synthesis to ensure long-term strain stability.	A synthetic system maintained mevalonate production stability over 95 generations [18].

Diagram 1: Pillars of microbial robustness, illustrating how native traits and engineering strategies converge to create a robust cell factory.

Experimental Protocols for Harnessing Native Advantages

Protocol: Adaptive Laboratory Evolution (ALE) for Enhanced Substrate Utilization

ALE is a powerful method for improving specific microbial traits, such as the ability to consume non-native carbon sources more efficiently [19].

• Strain and Medium Preparation: Begin with a wild-type or genetically engineered strain. Prepare a minimal medium where the target substrate (e.g., sucrose for E. coli W) is the sole or primary carbon source.
• Evolution Setup: Inoculate the strain into multiple independent flasks or a serial transfer setup. Maintain the cultures in the exponential growth phase by periodically transferring a small aliquot to fresh medium.
• Monitoring: Regularly monitor optical density (OD600) to track improvements in growth rate and maximum cell density. The experiment should be continued for several tens to hundreds of generations.
• Isolation and Screening: Plate evolved cultures to isolate single clones. Screen these clones for improved performance in the target phenotype (e.g., specific growth rate on sucrose).
• Genomic Analysis: Sequence the genomes of superior-evolved clones to identify causative mutations. This can reveal novel genes or regulatory elements involved in substrate utilization and stress tolerance.

Protocol: Dominant-Metabolism Compromised Intermediate-Chassis (DMCI) Strategy

This strategy is used for organisms with a dominant, competing native pathway that limits flux to a desired new product, as demonstrated in Zymomonas mobilis [3].

• Pathway Identification: Identify the dominant native pathway (e.g., the ethanol pathway from pyruvate in Z. mobilis).
• Introduction of a "Distractor" Pathway: Introduce a heterologous pathway for a product that has low toxicity but creates a cofactor imbalance. For example, introducing the 2,3-butanediol (2,3-BDO) pathway in Z. mobilis consumes NADH, creating an internal imbalance that helps weaken the dominant ethanol pathway.
• Construction of Intermediate Chassis: Genetically engineer the host to express the distractor pathway. This creates an intermediate chassis (DMCI) with a compromised, less rigid metabolism.
• Engineering for Target Production: In the DMCI strain, introduce the pathway for the target biochemical (e.g., D-lactate). The relaxed metabolic control allows for higher carbon flux toward the new product.
• Validation: Ferment the final engineered strain and quantify the titer, yield, and productivity of the target product, comparing it to a strain engineered directly without the DMCI step.

Protocol: Genome-Scale Metabolic Modeling (GEM) Guided Engineering

GEMs are in silico representations of metabolism used to predict genetic modifications that optimize production [3].

• Model Reconstruction: Develop a high-quality, genome-scale metabolic model for the target non-model organism by integrating genomic, transcriptomic, and proteomic data. An example is the eciZM547 model for Z. mobilis [3].
• Integration of Enzyme Constraints: Enhance the model by incorporating enzyme kinetic data (kcat values) to create an enzyme-constrained model (ecModel). This improves prediction accuracy by accounting for proteome limitations.
• In Silico Simulation: Use the ecModel to simulate growth and product synthesis under different genetic and environmental conditions. Perform flux balance analysis (FBA) to identify gene knockout or overexpression targets that maximize the product formation rate.
• Experimental Implementation: Construct the engineered strains based on the model predictions.
• Model Refinement: Use experimental results from the engineered strains, such as fermentation data and 13C-metabolic flux analysis (MFA), to refine and validate the model, creating an iterative Design-Build-Test-Learn (DBTL) cycle.

Diagram 2: The iterative Design-Build-Test-Learn (DBTL) cycle for engineering robust non-model microorganisms, enabled by tools like genome-scale metabolic models.

The Scientist's Toolkit: Key Research Reagent Solutions

The effective engineering of non-model microbes relies on a suite of specialized reagents and tools.

Table 3: Essential Research Reagents and Tools for Engineering Non-Model Microbes

Reagent/Tool Category	Specific Example	Function in Research
Gene Editing Tools [22]	CRISPR-Cas12a, endogenous Type I-F CRISPR-Cas, MEJ repair systems [3]	Enables precise genomic modifications (knockouts, knock-ins) in genetically recalcitrant non-model hosts.
Biosensors [18]	Metabolite-responsive transcriptional regulators (e.g., for myo-inositol) [18]	Allows dynamic monitoring and control of intracellular metabolite levels, enabling autonomous pathway regulation.
Specialized Vectors & Promoters [1] [3]	Plasmid systems with toxin-antitoxin (TA) modules; native constitutive/inducible promoters [18] [3]	Ensures stable plasmid maintenance without antibiotics; provides predictable, tunable gene expression.
Enzyme Kits for Assays	UDP-Glucosyltransferase (UGT) kits [19]	Used for in vitro validation of enzymatic activity, such as flavonoid glycosylation, before implementing in vivo.
Metabolic Model Software [3]	ECMpy, AutoPACMEN for kcat prediction [3]	Facilitates the construction and refinement of enzyme-constrained metabolic models for predictive strain design.
Bodipy FL-C16	Bodipy FL-C16, MF:C27H41BF2N2O2, MW:474.4 g/mol	Chemical Reagent
Dihydroergotamine-d3	Dihydroergotamine-d3, MF:C33H37N5O5, MW:586.7 g/mol	Chemical Reagent

Non-model microorganisms are invaluable assets for advancing the circular bioeconomy. Their native metabolic capabilities and inherent robustness, stemming from unique pathways and resilient physiologies, provide a foundational advantage over traditional model systems for specific industrial applications. By combining a deep understanding of these native traits with advanced engineering strategies—such as genome reduction, dynamic control, and model-guided DBTL cycles—researchers can transform these microbes into highly efficient and robust cell factories. Future research will undoubtedly focus on expanding the toolkit for non-model organisms, further unlocking their potential to produce a wider range of bio-based products sustainably and economically.

The transition from a fossil-fuel-based economy to a sustainable, bio-based circular economy necessitates the development of highly efficient microbial cell factories. While traditional model organisms like Escherichia coli and Saccharomyces cerevisiae have been widely exploited, they often lack the specialized traits required for diverse industrial bioprocesses. This has driven research toward non-model microorganisms that possess innate, advantageous physiological and metabolic characteristics. Through advanced genetic engineering and synthetic biology, these organisms are being refined into robust industrial chassis. This whitepaper examines two exemplary cases: the ethanologenic bacterium Zymomonas mobilis for biofuel production and the prolific actinobacterium Streptomyces for natural drug discovery. Framed within the context of microbial chassis development, this review highlights the unique properties of each organism, the engineering strategies employed to enhance their capabilities, and the experimental protocols that enable their manipulation.

Microbial Chassis Development: A Framework for Engineering

A microbial chassis is defined as an engineerable and reusable biological platform. Its genome encodes basic functions for stable self-maintenance and growth, but is systematically edited to strengthen performance under specified industrial conditions [1]. The general workflow for chassis development involves a detailed genomic and physiological characterization of a selected strain, followed by the development of a molecular toolbox for its genetic manipulation. A crucial step in this process is genome reduction, a top-down approach that systematically removes "unnecessary" genes to reduce cellular complexity and improve desirable traits [1]. The benefits of this approach, as demonstrated in various prokaryotes, include enhanced genomic stability, improved growth and production rates, higher transformation efficiency, and simplification of the metabolic background for easier analysis and engineering [1].

Case Study 1:Zymomonas mobilisas a Biofuel Chassis

Zymomonas mobilis is a facultative anaerobic, Gram-negative bacterium with a naturally streamlined genome of approximately 2,000 genes [23] [24]. It is a natural ethanologen, renowned for its high ethanol yield (up to 98% of theoretical maximum) and productivity, surpassing traditional yeast [23] [25]. Its key metabolic advantage lies in its use of the Entner-Doudoroff (ED) pathway anaerobically, which generates only one net ATP per glucose molecule. This leads to a phenomenon known as "uncoupled growth," where less carbon is diverted to biomass production (only 3-5%) and more is channeled toward ethanol [23]. Furthermore, Z. mobilis exhibits high tolerance to sugar concentrations (up to 400 g L⁻¹) and ethanol (up to 160 g L⁻¹), making it inherently robust for industrial fermentation [23].

Genetic Diversity and Strain Selection

Comparative genomic studies have classified Z. mobilis strains into distinct clusters based on Average Nucleotide Identity (ANI). Phenotypic characterization of these strains reveals significant variation in traits critical for industrial application, such as growth in lignocellulosic hydrolysate and tolerance to inhibitors [26]. Among available strains, ZM4 has been identified as a superior chassis due to its robust growth, high tolerance, and relatively efficient genetic accessibility [26]. The table below summarizes key quantitative data for representative Z. mobilis strains.

Table 1: Physiological and Metabolic Characteristics of Z. mobilis

Feature	Description / Value	Significance / Implication
Ethanol Yield	Up to 98% of theoretical maximum [23]	Superior to yeast, minimizes carbon loss.
Ethanol Productivity	Up to 63.7 g L⁻¹ h⁻¹ (with immobilized cells) [23]	Very high production rate.
Sugar Tolerance	Up to 400 g L⁻¹ [23]	Enables very high gravity fermentation.
Ethanol Tolerance	Up to 160 g L⁻¹ [23]	Allows accumulation of high product titers.
Sugar Utilization	Glucose, fructose, sucrose (native) [23]	Limited substrate range requires expansion.
ATP Yield (ED Pathway)	1 mol ATP / mol glucose [23]	Low biomass yield, high carbon flux to product.
Genome Size	~2,000 protein-coding genes [24]	Naturally streamlined, easier to study and engineer.

Expanding Substrate Utilization and Product Spectrum

Wild-type Z. mobilis is limited to fermenting glucose, fructose, and sucrose. Extensive metabolic engineering has been undertaken to expand its substrate range to include the pentose sugars (xylose and arabinose) derived from lignocellulosic biomass [23]. Concurrently, research has focused on rerouting its metabolism to produce compounds beyond ethanol, such as lactate, succinate, isobutanol, and 2,3-butanediol [23] [25]. The diagram below illustrates the native metabolic pathway of Z. mobilis and key engineering targets.

Diagram 1: Native metabolism and byproducts of Z. mobilis (GLK: glucokinase; FRK: fructokinase; PGI: phosphoglucose isomerase; PDC: pyruvate decarboxylase; ADH: alcohol dehydrogenase; GFOR: glucose-fructose oxidoreductase; GL: gluconolactonase; Sac: sucrase genes).

Essential Experimental Protocols

Protocol: CRISPRi-Mediated Gene Knockdown for Essential Gene Analysis

Purpose: To identify genes essential for survival or growth under specific conditions (e.g., anaerobiosis, toxin tolerance) [24]. Principle: A catalytically "dead" Cas9 (dCas9) binds to target DNA sequences under guide RNA (gRNA) direction, blocking transcription (CRISPR interference). Procedure:

• Library Construction: Design and clone a library of gRNAs targeting all 1,915 protein-coding genes of Z. mobilis into a CRISPRi plasmid vector containing dCas9.
• Transformation: Introduce the gRNA library into a Z. mobilis strain expressing dCas9 via electroporation.
• Selection and Growth: Plate the transformed cells on selective media and allow them to grow under the condition of interest (e.g., anaerobic vs. aerobic, with/without hydrolysate toxins).
• Sequencing and Analysis: Harvest cells after growth, extract genomic DNA, and sequence the gRNA inserts via high-throughput sequencing. Depletion of specific gRNAs from the population after growth indicates that the targeted gene is essential for survival under the tested condition [24].

Protocol: Metabolic Engineering for Xylose Utilization

Purpose: To enable Z. mobilis to ferment xylose, a major pentose sugar in lignocellulosic biomass. Principle: Heterologous expression of xylose isomerase (XI) and xylulokinase (XK) to convert xylose to xylulose-5-phosphate, which can enter the non-oxidative pentose phosphate pathway. Procedure:

• Gene Assembly: Clone genes encoding XI (e.g., xylA) and XK (e.g., xylB) from a suitable donor organism (e.g., E. coli) into an expression vector with strong constitutive promoters.
• Transformation: Introduce the construct into Z. mobilis ZM4 via electroporation or conjugation.
• Validation and Screening: Screen for successful transformants on selective plates. Validate gene expression via RT-PCR and enzyme activity assays.
• Fermentation Analysis: Evaluate the engineered strain in controlled bioreactors with xylose as the sole carbon source to measure growth, sugar consumption, and ethanol production [23].

Case Study 2:Streptomycesas a Natural Products Chassis

Streptomyces are Gram-positive, filamentous bacteria belonging to the Actinobacteria phylum. They are characterized by a complex life cycle involving the formation of aerial mycelium and spores [27]. They possess large, linear genomes (8-10 Mb) with a high G+C content (>70%), which are exceptionally rich in Biosynthetic Gene Clusters (BGCs) [28] [27]. Each BGC encodes the enzymatic machinery for producing a specific secondary metabolite. It is estimated that Streptomyces produce over 100,000 bioactive compounds, accounting for approximately 70-80% of medically useful antibiotics, as well as antifungals, antivirals, anticancer agents, and immunosuppressants [27].

The Imperative for New Natural Products

The relentless spread of Antimicrobial Resistance (AMR) and the emergence of "superbugs" underscore the critical need for novel antibiotics. Furthermore, diseases like cancer, Alzheimer's, and emerging viral infections demand new therapeutic agents. Streptomyces, with their vast untapped reservoir of BGCs (many of which are "cryptic" under laboratory conditions), represent the most promising source for these new drugs [27].

Engineering Strategies for Natural Product Exploitation

The development of Streptomyces as a chassis involves three key engineering aspects: advanced genetic tools, BGC-specific engineering, and host chassis modification [28].

Table 2: Prominent Bioactive Natural Products from Streptomyces

Natural Product	Producing Species	Biological Activity	Clinical/Commercial Use
Streptomycin	S. griseus	Antibacterial	Treatment of Tuberculosis [27]
Tetracycline	S. aureofaciens	Antibacterial	Broad-spectrum antibiotic [27]
Daptomycin	S. roseosporus	Antibacterial	FDA-approved for skin infections (2003) [27]
Doxorubicin	S. peucetius	Antitumoral	Chemotherapy drug [27]
Rapamycin	S. hygroscopicus	Immunosuppressant	Prevents organ transplant rejection [27]
Avermectin	S. avermitilis	Antiparasitic	Treatment of river blindness [27]

Genetic Toolbox forStreptomyces

The field has been revolutionized by CRISPR-based systems.

• CRISPR-Cas9: Used for efficient and multiplex gene knock-outs (deletions from 20 bp to 30 kb) and precise knock-ins (e.g., inserting strong promoters to activate BGCs) [28].
• CRISPR-Cpf1 (Cas12a): An alternative to Cas9, developed to overcome toxicity issues in some industrial strains, used for both genome editing and transcriptional repression [28].
• CRISPRi: Utilizing dCas9 for targeted repression of specific genes without altering the DNA sequence [28].

Genome Reduction and Chassis Development

To create cleaner and more efficient hosts for heterologous expression of BGCs, genome reduction is employed. This involves deleting endogenous BGCs to minimize background metabolite interference and free up metabolic resources.

• Example: In Streptomyces albus, 15 native antibiotic BGCs were deleted. This mutant showed a 2-fold higher production of heterologously expressed BGCs compared to the parent strain [1].
• Example: In Streptomyces lividans, deletion of 10 endogenous antibiotic clusters resulted in a higher growth rate and a 4.5-fold increase in the production of the heterologous antibiotic deoxycinnamycin [1].

Essential Experimental Protocols

Protocol: CRISPR-Cas9 Mediated Promoter Knock-in for BGC Activation

Purpose: To activate the expression of a cryptic BGC by inserting a strong constitutive promoter upstream of its core biosynthetic operon [28]. Principle: The CRISPR-Cas9 system introduces a double-strand break (DSB) at a specific site near the target BGC. A donor DNA template containing the desired promoter is provided, and the cell's homology-directed repair (HDR) machinery integrates it. Procedure:

• gRNA and Donor Design: Design a gRNA to target a non-essential site immediately upstream of the BGC's first biosynthetic gene. Synthesize a donor DNA fragment containing the strong promoter (e.g., ermEp) flanked by homology arms (500-1000 bp) matching the sequences upstream and downstream of the target site.
• Plasmid Construction: Clone the gRNA expression cassette into a Streptomyces CRISPR-Cas9 plasmid.
• Transformation: Co-transform the CRISPR-Cas9 plasmid and the donor DNA fragment into the Streptomyces host via protoplast transformation.
• Screening and Validation: Screen for apramycin-resistant (or other marker) colonies. Validate correct promoter insertion via colony PCR and sequencing.
• Metabolite Analysis: Ferment the validated mutant and analyze the metabolite profile using HPLC or LC-MS to detect newly produced compounds.

Protocol: Direct Cloning of Large BGCs using CATCH

Purpose: To clone large biosynthetic gene clusters ( > 30 kb) directly from genomic DNA for heterologous expression. Principle: CATCH (Cas9-Assisted Targeting of CHromosome segments) uses Cas9 to excise a specific large DNA fragment from the genome, which is then ligated into a vector via Gibson assembly [28]. Procedure:

• In Vitro Cas9 Digestion: Design gRNAs to flanks the target BGC. Incubate genomic DNA with Cas9 protein and the specific gRNAs to liberate the linear BGC fragment.
• Vector Preparation: Linearize the BAC (Bacterial Artificial Chromosome) vector using Cas9 with gRNAs targeting its multiple cloning site.
• Gibson Assembly: Mix the purified linear BGC fragment and the linearized vector with Gibson assembly master mix. This enzyme mix chews back the DNA ends to create complementary overhangs and ligates them together.
• Transformation: Transform the assembled product into E. coli for propagation.
• Verification: Isolve the BAC DNA and verify the correct insert by restriction digest and pulse-field gel electrophoresis (PFGE) before transferring it into a Streptomyces chassis for expression [28].

The workflow for developing a Streptomyces chassis and exploiting its natural products is summarized below.

Diagram 2: Integrated workflow for developing Streptomyces as a natural product cell factory.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagent Solutions for Non-Model Organism Engineering

Reagent / Tool Category	Specific Examples	Function and Application
Genetic Engineering Tools	CRISPR-Cas9/Cpf1 systems [23] [28]	Enables precise gene knock-out, knock-in, and repression.
	Recombineering (RecET/Redαβ) [23] [28]	Facilitates homologous recombination for genetic modifications.
Vector Systems	Bacterial Artificial Chromosomes (BACs) [28]	Stably maintains large DNA inserts (e.g., entire BGCs) for heterologous expression.
	Shuttle Vectors (E. coli-Zymomonas/Streptomyces) [23] [26]	Allows plasmid construction in E. coli before transfer to the target host.
DNA Assembly Methods	Gibson Assembly [28]	Seamlessly assembles multiple DNA fragments in vitro.
	Transformation-associated recombination (TAR) in yeast [28]	Captures and assembles large DNA pathways in yeast for subsequent transfer.
Specialized Reagents	Polyvinyl Alcohol (PVA) [23]	Used for cell immobilization to achieve very high ethanol productivity in bioreactors.
	Protoplast Transformation Mix [28]	Essential for introducing DNA into the thick cell wall of Streptomyces.
Pimobendan-d3	Pimobendan-d3, MF:C19H18N4O2, MW:337.4 g/mol	Chemical Reagent
MitoPerOx	MitoPerOx, MF:C42H38BBrF2N3OP, MW:760.5 g/mol	Chemical Reagent

Zymomonas mobilis and Streptomyces exemplify the power of leveraging non-model organisms as microbial cell factories. Z. mobilis provides a naturally streamlined chassis whose exceptional native capabilities for ethanol production are being refined and expanded through metabolic engineering and synthetic biology. In contrast, Streptomyces offers a vast and complex metabolic landscape, which is being systematically mined, understood, and streamlined using cutting-edge genetic tools to unlock novel pharmaceuticals. The continued development of both organisms underscores a central theme in industrial biotechnology: the strategic selection of a native host with advantageous innate traits provides a more efficient starting point for engineering than attempting to instill these complex traits de novo into traditional models. The ongoing integration of systems biology, sophisticated genetic toolkits, and genome reduction strategies will undoubtedly solidify the position of these and other non-model organisms as pillars of the emerging bio-based economy.

Microbial biodiversity represents an immense and largely untapped reservoir of enzymatic and metabolic potential for biotechnology and drug discovery. Despite the existence of millions of microbial species, current industrial biotechnology primarily utilizes a limited set of model organisms, leaving the vast majority of nature's genetic and metabolic diversity unexplored [7]. Environmental biodiversity analyses reveal that approximately 99% of microorganisms exist in consortia form in habitats ranging from wastewater and soil to animal gastrointestinal tracts [29]. This microbial dark matter represents a treasure trove of novel biosynthetic pathways waiting to be discovered and harnessed.

The numbers underscore this potential: of the approximately 1 million known natural products, only about 25% are biologically active compounds, with 60% derived from plants and the remainder from microbial sources [7]. Fungi and bacteria alone have yielded approximately 23,000 bioactive natural products with therapeutic applications, including antivirals, antimicrobials, anti-inflammatory agents, and cytotoxic compounds [29]. Notably, 42% of these valuable compounds originate from fungi (particularly Basidiomycota and Ascomycota), while 32% are produced by filamentous bacteria (actinomycetes) [29]. Despite this proven potential, less than 5% of fungal and 1% of bacterial species are currently characterized, indicating that the majority of natural product-synthesizing microbial species remain undiscovered [29]. This gap highlights the critical opportunity for systematic exploration of microbial biodiversity to identify novel candidates for development as microbial cell factories in the bioeconomy era.

Biodiversity Screening and Candidate Prioritization

Key Taxonomic Groups with Industrial Potential

Systematic biodiversity screening requires focusing on taxonomic groups with demonstrated industrial potential while maintaining openness to novel lineages. Several bacterial and fungal families have shown exceptional capabilities in producing valuable compounds and withstanding industrial process conditions.

Table 1: Promising Microbial Groups for Bioprospecting

Microbial Group	Key Genera/Species	Industrial Applications	Notable Characteristics
Lactic Acid Bacteria	Lactobacillus, Lactococcus, Streptococcus, Pediococcus [7]	Lactic acid, amines, antibacterial peptides, vitamins, organic acids [7]	GRAS status, food fermentation, diverse metabolic output
Actinomycetes	Streptomyces [29] [1]	Antibiotics, anticancer agents, immunosuppressants [29]	>75% of commercially used antibiotics; extensive secondary metabolism
White Rot Fungi	Phanerochaete, Trametes, Pleurotus [30]	Lignin-modifying enzymes, biomass conversion [30]	Complex enzyme systems for lignin breakdown
Ascomycete Fungi	Aspergillus, Penicillium, Fusarium [29]	Bioactive compounds, organic acids, enzymes [29] [7]	Aspergillus alone produces 950+ bioactive compounds
Non-Model Bacteria	Zymomonas mobilis, Halomonas bluephagenesis [3] [31]	Biofuels, bioplastics, specialty chemicals [3]	Unique metabolic pathways, industrial robustness

Prioritization Criteria for Candidate Selection

When evaluating microbial candidates from biodiversity screens, researchers should employ a multi-parameter assessment framework:

• Biosynthetic Potential: Prioritize strains with abundant biosynthetic gene clusters (BGCs). Genomic analyses reveal that many fungi contain numerous silent or barely expressed BGCs under laboratory conditions, representing hidden biosynthetic potential [32]. For instance, the fungus Streptomyces albus was engineered by deleting 15 native antibiotic gene clusters, resulting in a 2-fold increase in production of heterologously expressed biosynthetic gene clusters [1].

• Process-Relevant Phenotypes: Candidates should demonstrate robustness against harsh process conditions, including tolerance to high substrate and product concentrations, inhibitors present in lignocellulosic hydrolysates, and varying pH/temperature profiles [1] [3]. Zymomonas mobilis exemplifies this with its high sugar uptake rate and ethanol tolerance [3].

• Metabolic Versatility: Strains with broad substrate utilization capabilities are preferred, particularly those capable of converting low-cost non-food materials like lignocellulose, glycerol, and waste streams into valuable products [3].

• Genetic Tractability: While non-model organisms may lack established genetic tools, evidence of transformability or presence of endogenous genetic elements that can be harnessed for engineering should be considered. For example, the endogenous Type I-F CRISPR-Cas system in Z. mobilis has been exploited for genome editing [3].

Experimental Workflows for Characterization and Engineering

Biodiversity Mining and Pathway Identification

The initial discovery phase requires integrated approaches combining traditional microbiology with modern omics technologies:

Figure 1: Biodiversity Mining Workflow

Sample Collection and Strain Isolation: Strategic sampling from diverse ecological niches (marine environments, extreme habitats, plant rhizospheres) increases the probability of discovering novel functions [29]. Advanced culturing techniques, including diffusion chambers and co-culture approaches, help recover previously "uncultivable" species [7].

Multi-Omics Characterization: Genome sequencing provides the foundation for identifying biosynthetic gene clusters through tools like antiSMASH. Transcriptomics under various conditions reveals silent clusters, while metabolomics links chemical products to their genetic basis [32]. For white rot fungi, this approach has identified numerous lignin-modifying enzyme (LME) genes and their expression patterns during lignin degradation [30].

Heterologous Expression Platform Development: For uncultivable or genetically recalcitrant strains, heterologous expression in amenable hosts enables pathway exploration. Key considerations include host selection, BGC assembly methods, promoter selection, and metabolic engineering to support production [32]. Fungal platforms are particularly valuable for expressing complex eukaryotic biosynthetic pathways.

Engineering Non-Model Organisms as Microbial Chassis

Engineering non-model microorganisms requires specialized approaches that address their unique genetic and metabolic characteristics:

Figure 2: Non-Model Organism Engineering Pipeline

Genetic Tool Development: Establishing efficient transformation protocols is foundational. This includes adapting CRISPR systems, developing shuttle vectors, and characterizing native promoters and ribosomal binding sites [1] [3]. For Z. mobilis, tools based on heterologous CRISPR-Cas12a and endogenous Type I-F CRISPR-Cas systems have been developed [3].

Genome Reduction for Chassis Development: Removing non-essential genes, mobile genetic elements, and native biosynthetic pathways streamlines metabolism and improves genetic stability [1]. In Streptomyces lividans, deletion of 10 endogenous antibiotic encoding clusters resulted in higher growth rates and a 4.5-fold production increase of the heterologously expressed compound deoxycinnamycin [1].

Metabolic Engineering Strategies: For organisms with dominant native pathways, sophisticated rerouting approaches are needed. In Z. mobilis, researchers developed a "dominant-metabolism compromised intermediate-chassis" (DMCI) strategy that introduces a low-toxicity but cofactor-imbalanced pathway to divert flux from the native ethanol pathway, enabling high-yield production of alternative compounds like D-lactate (140.92 g/L from glucose) [3].

Systems Metabolic Engineering: Integrating metabolic engineering with evolutionary engineering, synthetic biology, and systems biology enables comprehensive strain optimization. This includes balancing redox cofactors, optimizing precursor supply, and deleting competing pathways [7].

Research Reagents and Methodologies

Table 2: Essential Research Reagents and Platforms

Reagent/Platform Type	Specific Examples	Function/Application
Genetic Engineering Tools	CRISPR-Cas12a [3], Endogenous Type I-F CRISPR-Cas [3], MMEJ repair systems [3]	Precise genome editing in non-model organisms
Heterologous Expression Hosts	Aspergillus niger [7], Saccharomyces cerevisiae [7], Escherichia coli [1]	Expression of BGCs from uncultivable or recalcitrant species
Bioinformatics Tools	antiSMASH [32], AutoPACMEN [3], GEM reconstruction tools [3]	BGC identification, enzyme constraint modeling, metabolic flux prediction
Metabolic Models	eciZM547 [3], iZM516 [3]	Genome-scale metabolic modeling with enzyme constraints
Cultivation Platforms	High-throughput microbioreactors [33], Co-culture systems [29]	Scalable screening and production optimization

Case Studies: From Biodiversity to Industrial Application

White Rot Fungi for Lignin Valorization

White rot fungi (WRF) possess sophisticated enzymatic systems highly effective in breaking down lignocellulosic biomass, particularly lignin [30]. Their enzyme systems include lignin-modifying enzymes (LMEs) such as laccases (Lac), lignin peroxidases (LiP), manganese peroxidases (MnP), and versatile peroxidases (VP), along with lignin-degrading auxiliary enzymes (LDAEs) [30]. Research has focused on:

• Enzyme Engineering: Improving catalytic properties and stability through rational design and directed evolution. For example, the hydrogen peroxide stability of Pleurotus eryngii versatile ligninolytic peroxidase was enhanced through rational protein engineering [30].

• Transcriptional Regulation Engineering: Identifying and manipulating transcription factors that regulate LME composition and expression. This approach shifts focus from individual enzymes to integrative regulation of entire enzyme systems [30].

• Fungal Cell Factory Development: Constructing specialized chassis strains for controlled production of tailored enzyme cocktails. This involves synthetic biology and genome editing to create strains with optimized LME profiles for specific biomass feedstocks [30].

Non-Model Bacteria as Biorefinery Chassis

Zymomonas mobilis demonstrates how non-model organisms with unique metabolic capabilities can be developed into industrial platforms. This bacterium possesses several advantageous traits, including:

• High Sugar Uptake Rate: Utilizes the Entner-Doudoroff pathway anaerobically with faster glucose consumption than many traditional hosts [3].

• Native Ethanol Production: Efficient pyruvate decarboxylase (PDC) and alcohol dehydrogenases (ADHs) enable high ethanol yield and tolerance [3].

• Genetic Tool Development: Implementation of CRISPR systems and characterization of repair pathways enable precise genome engineering [3].

To overcome the challenge of its dominant ethanol pathway, researchers developed a sophisticated metabolic strategy. Rather than directly engineering target biochemical pathways, they first constructed an intermediate chassis with compromised dominant metabolism by introducing a low-toxicity but cofactor-imbalanced 2,3-butanediol pathway. This approach successfully reduced ethanol flux and enabled construction of a D-lactate producer achieving over 140 g/L from glucose and >100 g/L from corncob residue hydrolysate with yields exceeding 0.97 g/g [3]. Techno-economic analysis and life cycle assessment demonstrated the commercial feasibility and greenhouse gas reduction capability of this lignocellulosic D-lactate production process [3].

The systematic exploration of microbial biodiversity for identifying novel microbial cell factories represents a paradigm shift in industrial biotechnology. While traditional approaches have focused on a handful of model organisms, the expanding toolkit for characterizing and engineering non-model microbes now enables researchers to tap into nature's vast arsenal of metabolic diversity. Success in this endeavor requires integrated approaches combining advanced biodiscovery methods with sophisticated engineering strategies tailored to the unique characteristics of non-model systems.

Future progress will be accelerated by several emerging technologies. The integration of automation and artificial intelligence with biotechnology will facilitate the development of customized artificial synthetic microbial cell factories [31]. High-throughput experimentation combined with deep learning enables more efficient exploration of biodiversity and rapid optimization of strains [33]. Additionally, the continued development of enzyme-constrained genome-scale metabolic models will enhance our ability to predictively engineer metabolic fluxes in non-model chassis [3].

As these technologies mature, the bioeconomy will increasingly rely on specialized microbial chassis derived from biodiversity exploration, enabling sustainable production of chemicals, materials, and pharmaceuticals from renewable feedstocks. This transition from a fossil-based economy to a circular bioeconomy represents both a profound challenge and an unprecedented opportunity for biotechnology innovation.

Building the Factory: Synthetic Biology Tools and Pathway Engineering Strategies

The advancement of non-model organisms as microbial cell factories (MCFs) represents a frontier in biomanufacturing, enabling the sustainable production of biofuels, pharmaceuticals, and chemicals from renewable feedstocks [9] [34]. Unlike conventional model organisms, non-model microbes often possess innate physiological and metabolic advantages—such as substrate utilization range, stress tolerance, and unique biosynthetic capabilities—that make them ideal industrial workhorses. However, their genetic intractability has historically hindered metabolic engineering efforts. The emergence of sophisticated genetic toolkits, particularly CRISPR-based systems and TALENs, has revolutionized our capacity to design, construct, and optimize these complex biological systems [22] [35]. These technologies enable precise genome editing, transcriptional regulation, and metabolic pathway engineering, thereby accelerating the transformation of non-model microorganisms into high-performance cell factories for the bioeconomy era [9] [36].

Technology Deep Dive: Mechanisms and Components

CRISPR-Cas Systems: From DNA Cleavage to Multiplexed Control

The CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated proteins) system functions as an adaptive immune system in prokaryotes, and has been repurposed as a highly programmable genome editing tool. Its activity is mediated through three key stages: adaptation, expression, and interference [37]. During adaptation, fragments of foreign DNA (protospacers) are integrated into the CRISPR array as new spacers. In the expression stage, the CRISPR array is transcribed and processed into mature CRISPR RNA (crRNA). Finally, in the interference stage, crRNA guides Cas proteins to recognize and cleave complementary foreign DNA sequences [38] [37].

Core Components and System Diversity:

• Class 2 Systems (Type II, V, VI): Utilize single effector proteins (Cas9, Cas12a, etc.) and are most widely adopted for genetic engineering [38].
• Cas9 (Type II): Requires both crRNA and trans-activating crRNA (tracrRNA), often fused into a single guide RNA (sgRNA). It recognizes a 5'-NGG-3' Protospacer Adjacent Motif (PAM) and generates blunt-ended double-strand breaks (DSBs) via its HNH and RuvC nuclease domains [38] [37].
• Cas12a (Type V): Requires only a crRNA, recognizes a T-rich PAM, and produces staggered DSBs. It is particularly valuable for multiplexed genome editing due to its simpler guide RNA structure [38] [34].

The CRISPR toolbox has expanded beyond simple nucleases to include advanced derivatives:

• CRISPR Interference (CRISPRi): Catalytically dead Cas9 (dCas9) binds DNA without cutting, blocking transcription initiation or elongation when targeted to promoter or coding regions [38].
• Base Editing: Fusion of dCas9 or nCas9 (nickase Cas9) with deaminase enzymes enables direct conversion of C•G to T•A or A•T to G•C base pairs without requiring DSBs [34] [39].
• Prime Editing: A more precise editing system that uses a Cas9-reverse transcriptase fusion and a prime editing guide RNA (pegRNA) to directly write new genetic information into a target DNA site [34] [39].

TALENs: Protein-Based Genome Editing

Transcription Activator-Like Effector Nucleases (TALENs) represent an earlier generation of programmable nucleases that remain valuable for specific applications. A TALEN pair consists of two custom-designed proteins, each containing a DNA-binding domain fused to the FokI nuclease domain [40] [41].

Mechanism and Design:

• DNA Recognition: The DNA-binding domain comprises highly conserved repeats of 33-35 amino acids. The repeat variable diresidues (RVDs) at positions 12 and 13 within each repeat specify nucleotide recognition (NI for A, HD for C, NG for T, NN for G/A) [41].
• DSB Formation: A pair of TALENs binds to opposing DNA strands with a defined spacer (typically 14-20 bp) between them. The FokI domains dimerize to create a DSB within the spacer region [40] [41].

Promoter Systems: Fine-Tuning Gene Expression

Precise metabolic engineering in non-model organisms requires fine control over the expression of heterologous pathways and endogenous genes. A diverse toolkit of promoter systems is essential for this purpose.

• Constitutive Promoters: Provide constant expression levels and are characterized as strong, medium, or weak. They are vital for driving essential pathway genes and selection markers.
• Inducible Promoters: Enable temporal control over gene expression in response to specific chemical or environmental cues (e.g., tetracycline, arabinose, light). This is crucial for expressing toxic genes or optimizing flux through biosynthetic pathways.
• Synthetic Promoters: Engineered promoters that combine core promoter elements with tailored operator sequences for customized expression strength and regulation.

Table 1: Core Components of Major Genome Editing Platforms

Feature	CRISPR-Cas9	TALENs
Molecular Machinery	Cas9 nuclease + sgRNA (crRNA + tracrRNA) [38] [37]	Pair of TAL effector-FokI nuclease fusions [41]
Target Recognition	RNA-DNA complementarity (20 nt guide sequence) [38]	Protein-DNA code (RVD-nucleotide specificity) [41]
PAM/Restriction	Requires 5'-NGG-3' PAM sequence adjacent to target [38] [37]	No PAM; target site must be flanked by two TALEN binding sites [41]
Cleavage Mechanism	Blunt-ended DSB via single Cas9 protein (HNH & RuvC domains) [38]	Staggered DSB via FokI dimerization [40] [41]
Efficiency	Typically high (can exceed 70% indel formation) [41]	High (e.g., ~33% indel formation reported) [41]
Multiplexing	Highly amenable via multiple sgRNAs [38] [34]	Challenging, requires multiple protein pairs

Comparative Analysis: Selecting the Right Tool for the Application

Specificity and Off-Target Effects

• CRISPR-Cas9: Initial concerns regarding off-target cleavage exist because sgRNAs can tolerate up to 5 base mismatches, particularly in the 5' end of the guide sequence [41] [37]. Strategies to mitigate this include:
  • Truncated sgRNAs: Using 17-18 nucleotide guides instead of 20 to reduce off-target affinity [41].
  • Paired Nickases: Using two Cas9 nickase mutants (D10A) with offset sgRNAs to create single-strand breaks only on opposite strands, significantly reducing off-target mutagenesis [41].
  • High-Fidelity Cas Variants: Engineered Cas9 proteins with altered amino acids to strengthen binding specificity [37].
• TALENs: Generally exhibit high specificity with minimal evidence of off-target activity, attributed to the requirement for dimerization of FokI and the long (∼36 bp) combined target sequence, which is statistically unique in most genomes [40] [41].

Practical Considerations: Design, Efficiency, and Limitations

• Ease of Design and Construction:

  • CRISPR: Design is straightforward, requiring only the synthesis of a 20 nt sgRNA sequence complementary to the target. Plasmid construction is simple and highly modular, facilitating rapid library generation and multiplexing [41].
  • TALENs: Construction is more labor-intensive, as it requires protein engineering—assembling a new TALE array for each target site—though modular cloning systems have streamlined this process [40] [41].

• Efficiency and Limitations:

  • CRISPR: Demonstrates high editing efficiency across diverse systems. A key limitation is the constraint imposed by the PAM sequence, which can restrict targetable sites [38] [41].
  • TALENs: Also achieve high efficiency but can be sensitive to cytosine methylation, especially at CpG islands, which can inhibit DNA binding and abolish cleavage activity [41].

Table 2: Strategic Selection Guide for Genome Editing Tools

Application	Recommended Tool	Rationale & Technical Notes
Rapid Gene Knockout	CRISPR-Cas9 (1st gen)	Fast design, high efficiency; ideal for initial functional gene studies [41] [36].
High-Fidelity Editing	TALENs or CRISPR High-Fidelity variants (e.g., eSpCas9)	TALEN's long target site and FokI dimerization minimize off-targets; high-fidelity Cas9 mutants also suitable [40] [41].
Multiplexed Editing	CRISPR-Cas9 or Cas12a	Co-expression of multiple sgRNAs enables simultaneous modification of several genomic loci [38] [34].
Methylated DNA Targets	CRISPR-Cas9	Cas9 activity is not hindered by DNA methylation, unlike TALENs [41].
DSB-Free Base Editing	CRISPR Base Editors (CBE, ABE)	Converts C•G to T•A or A•T to G•C base pairs without DSBs; crucial in organisms with inefficient HDR [34] [39].
Precise Insertion/Replacement	CRISPR HDR or TALEN HDR	Both can mediate precise edits with a donor template; choice depends on target sequence constraints and specificity requirements [40] [34].

Experimental Protocols for Non-Model Organisms

Protocol 1: Implementing a CRISPR-Cas9 System for Gene Knockout

This protocol outlines the steps for targeted gene disruption in a non-model microbe via non-homologous end joining (NHEJ) [38] [34] [37].

• Target Selection and sgRNA Design:

  • Identify a 20-nucleotide target sequence within the coding region of the gene of interest. The sequence must be directly followed by a 5'-NGG-3' PAM on the genomic DNA.
  • Use bioinformatics tools (e.g., CRISPRon/off) to assess on-target efficiency and potential off-target sites across the genome [36].
  • Reagent: Synthesize an oligonucleotide encoding the sgRNA scaffold and target sequence for cloning.

• Vector Construction:

  • Clone the sgRNA sequence into an expression vector under a strong, species-appropriate RNA Polymerase III promoter (e.g., U6, SCR1) or a T7 polymerase system if the host lacks native RNAP III machinery [35].
  • Ensure the plasmid also expresses a codon-optimized Cas9 nuclease, driven by a constitutive or inducible promoter functional in the host.

• Transformation and Selection:

  • Introduce the CRISPR plasmid into the microbial host using an optimized transformation method (e.g., electroporation, conjugation).
  • Plate cells on selective media and incubate to allow for plasmid maintenance, Cas9 expression, and target cleavage.

• Screening and Validation:

  • After 48-72 hours, pick individual colonies and perform colony PCR to amplify the targeted genomic region.
  • Use the Surveyor nuclease assay (Cel-1) or T7 Endonuclease I to detect mismatches in heteroduplex DNA, indicating successful mutagenesis [40].
  • Confirm the genotype by Sanger sequencing of the PCR products to identify the exact indel sequences.

Protocol 2: TALEN-Mediated Gene Replacement via HDR

This protocol describes using TALENs to create a DSB that is repaired via homology-directed repair (HDR) for precise gene insertion or replacement [40] [41].

• TALEN Design and Donor Template Construction:

  • Design a TALEN pair to target a site as close as possible to the intended modification. The binding sites should be on opposite strands, separated by a 14-20 bp spacer.
  • Reagent: Assemble the TALEN repeats using the Golden Gate cloning method with intermediary arrays (pFUSA, pFUSB, pLR) [40].
  • Reagent: Construct a donor DNA template containing the desired modification (e.g., a gene cassette) flanked by homology arms (≥500 bp each) that are homologous to the sequences upstream and downstream of the TALEN cut site.

• Co-delivery and Editing:

  • Co-transform the microbial host with the two TALEN plasmids and the linear donor DNA fragment.
  • Alternatively, a single plasmid expressing both TALENs can be used.

• Screening and Isolation of Edited Clones:

  • Screen transformants via PCR using primers outside the homology arms and inside the inserted cassette to identify correct recombinants.
  • Functional screens, such as fluorescence-activated cell sorting (FACS) if a fluorescent reporter is inserted, can also be employed for efficient enrichment [40].

Essential Research Reagent Solutions

A successful genetic engineering campaign in non-model organisms relies on a core set of reagents and materials.

Table 3: Key Research Reagent Solutions for Genetic Manipulation

Reagent/Material	Function/Description	Example Use Case
Codon-Optimized Cas9	Cas9 nuclease gene sequence optimized for the host's tRNA pool to maximize translation efficiency.	Essential for functional expression of CRISPR machinery in non-model hosts [35] [37].
sgRNA Expression Vector	Plasmid containing a promoter (e.g., SNR52, U6, T7) for driving the synthesis of guide RNA.	pCRISPRyl for Yarrowia lipolytica; pCAS1yl for multiplexed editing [35].
TALEN Repeat Plasmids	Kit of modular plasmids (e.g., pFUSA, pFUSB) for efficient assembly of TAL effector arrays using the Golden Gate method.	Standardized system for constructing custom TALEN proteins [40].
HDR Donor Template	Double-stranded DNA fragment or single-stranded oligonucleotide (ssODN) containing homologous sequences flanking the desired edit.	Used for precise gene insertion or point mutation via HDR with CRISPR or TALENs [40] [34].
λ-Red Recombinase System	A phage-derived protein system (Gam, Exo, Beta) that enhances recombination efficiency in bacteria.	Co-expressed with CRISPR-Cas9 in E. coli to dramatically improve HDR efficiency [38] [34].
Selection Markers	Genes conferring resistance to antibiotics (e.g., kanamycin, ampicillin) or complementing auxotrophies.	Essential for selecting and maintaining editing plasmids and integrated DNA cassettes.

The refined toolkit of CRISPR systems, TALENs, and promoter technologies has fundamentally altered the landscape of metabolic engineering, making the genetic manipulation of non-model organisms not only possible but increasingly routine. The choice between CRISPR and TALENs is no longer a question of which is universally superior, but rather which is best suited for a specific application, considering factors such as target site constraints, required specificity, and the host's native repair machinery [40] [41] [34].

Future advancements will focus on expanding this toolkit further. The discovery of novel Cas proteins with diverse PAM requirements will increase targetable genomic space [38] [37]. The development of more efficient DSB-free editors, such as enhanced prime editors, will enable cleaner and more precise genetic alterations [34] [39]. Furthermore, the integration of artificial intelligence and automation into the design-build-test-learn cycle promises to accelerate the high-throughput engineering of complex phenotypes, ultimately unlocking the full potential of non-model microbial cell factories for sustainable bioproduction [9] [36].

Genome-Scale Metabolic Models (GEMs) for In Silico Design and Flux Prediction

Genome-Scale Metabolic Models (GEMs) are mathematical representations of an organism's metabolism that provide a comprehensive network of biochemical reactions within a cell. Reconstructed from genomic and biochemical data, GEMs represent gene-protein-reaction (GPR) associations through stoichiometric matrices, enabling computational simulation of metabolic capabilities [10]. The primary framework for simulating GEMs is Constraint-Based Reconstruction and Analysis (COBRA), which operates under well-defined mathematical constraints without requiring detailed kinetic parameters [42]. This approach has become indispensable for predicting metabolic behavior, guiding metabolic engineering strategies, and optimizing microbial cell factories—particularly for non-model organisms with unique metabolic capabilities that remain underexplored.

The fundamental principle underlying GEMs is the steady-state mass balance equation: Sv = 0, where S is the stoichiometric matrix containing stoichiometric coefficients of metabolites in each reaction, and v is the flux vector representing reaction rates [42]. This equation is supplemented with physiological constraints that bound reaction fluxes (LBj ≤ vj ≤ UBj), reflecting thermodynamic and regulatory limitations. To predict biologically relevant metabolic states, Flux Balance Analysis (FBA) formulates an optimization problem that maximizes or minimizes an objective function (Z = cᵀv), typically biomass formation or product synthesis, subject to these constraints [42]. This computational framework enables researchers to predict organism behavior under different genetic and environmental conditions, providing invaluable insights for engineering non-model microorganisms as efficient cell factories.

GEM Reconstruction and Simulation Workflows

Model Reconstruction Pipeline

Reconstructing a high-quality GEM begins with genome annotation and proceeds through systematic curation and validation. The standard workflow involves: (1) Genome Annotation identifying genes encoding metabolic enzymes; (2) Reaction Identification assigning biochemical functions based on databases like KEGG and Rhea; (3) Network Assembly compiling metabolic reactions into an interconnected network; (4) GPR Association linking genes to their catalytic functions; (5) Compartmentalization assigning intracellular locations; (6) Gap Filling identifying missing reactions to ensure network connectivity; and (7) Experimental Validation comparing model predictions with empirical data [10] [42].

For non-model organisms, special considerations include accounting for organism-specific metabolic capabilities and potential knowledge gaps. Manual curation is particularly crucial, as automated pipelines often generate incomplete models. The MEMOTE evaluation tool provides standardized quality assessment, with high-quality models typically scoring above 90% [3]. Recent advances have enabled the reconstruction of GEMs for numerous non-model organisms, including Zymomonas mobilis (iZM516 and iZM547) and various human microbiome species, expanding the repertoire of potential microbial chassis [3] [43].

Advanced Simulation Techniques

Basic FBA simulations can be enhanced through several advanced approaches that increase predictive accuracy. parsimonious FBA identifies flux distributions that achieve the objective while minimizing total flux, reflecting evolutionary pressure toward efficiency. Flux Variability Analysis determines the range of possible fluxes for each reaction while maintaining optimal objective value, identifying flexible nodes in the network. Dynamic FBA extends the approach to time-varying conditions by coupling multiple steady-state simulations.

More sophisticated implementations incorporate additional biological constraints. Enzyme-constrained models integrate proteomic limitations by accounting for enzyme turnover numbers and mass constraints, preventing unrealistic proteome allocations [3]. The ec_iZM547 model of Z. mobilis demonstrated superior predictive accuracy compared to stoichiometric models alone by correctly simulating carbon diversion to both acetate and acetoin under aerobic conditions [3]. Regulatory FBA incorporates transcriptional regulation, while Thermodynamic-based FBA ensures flux directions align with energy constraints.

Table 1: Key Computational Tools for GEM Reconstruction and Analysis

Tool Name	Primary Function	Application Context	Reference
COBRA Toolbox	Model simulation & analysis	Constraint-based modeling across organisms	[43]
MEMOTE	Model quality assessment	Standardized evaluation of GEM quality	[3]
AutoPACMEN	kcat prediction for ecModels	Enzyme constraint integration	[3]
Rhea Database	Biochemical reaction data	Mass- and charge-balanced equations	[10]
MicroMap	Network visualization	Human microbiome metabolism	[43]
ModelSEED	Automated model reconstruction	Draft model generation from genomes	[3]

Figure 1: GEM Reconstruction and Simulation Workflow. The process begins with genome annotation and proceeds through network assembly, validation, and simulation phases, integrating data from multiple sources.

GEMs for Engineering Non-Model Organisms

Host Strain Selection and Evaluation

GEMs enable systematic comparison of metabolic capabilities across diverse microorganisms, providing critical insights for selecting optimal chassis strains for bioproduction. A comprehensive evaluation of five representative industrial microorganisms (Bacillus subtilis, Corynebacterium glutamicum, Escherichia coli, Pseudomonas putida, and Saccharomyces cerevisiae) calculated both maximum theoretical yield (YT) and maximum achievable yield (YA) for 235 bio-based chemicals across nine carbon sources under different aeration conditions [10]. This analysis revealed that while S. cerevisiae showed the highest yields for most chemicals, specific compounds exhibited clear host-specific advantages, such as pimelic acid production in B. subtilis [10].

For non-model organisms with desirable native traits, GEMs facilitate the assessment of their potential as alternative microbial cell factories. Zymomonas mobilis, a non-model bacterium with exceptional industrial characteristics including high ethanol yield and tolerance, has been systematically evaluated using the iZM516 and eciZM547 models [3]. These models have illuminated the organism's unique Entner-Doudoroff pathway and enabled the development of strategies to circumvent its dominant ethanol production, expanding its potential as a biorefinery chassis for diverse biochemicals [3]. Similarly, GEMs of non-model organisms like Clostridium species have enabled the exploitation of their native solvent production capabilities for consolidated bioprocessing applications [44].

Table 2: Metabolic Capacity Comparison of Representative Industrial Microorganisms

Microorganism	Metabolic Characteristics	Optimal Production Examples	Maximum Yield Range (mol/mol glucose)
Escherichia coli	Versatile metabolism, extensive genetic tools	Amino acids, organic acids	0.7985 (L-lysine) [10]
Saccharomyces cerevisiae	Eukaryotic system, robust industrial performer	Complex natural products, ethanol	0.8571 (L-lysine) [10]
Corynebacterium glutamicum	Amino acid overproduction, industrial safety	Proteinogenic amino acids	0.8098 (L-lysine), 221.30 g/L (L-lysine with engineering) [10] [22]
Bacillus subtilis	Secretory capability, industrial enzyme production	Pimelic acid, industrial enzymes	0.8214 (L-lysine) [10]
Pseudomonas putida	Diverse substrate utilization, stress tolerance	Aromatics, difficult substrates	0.7680 (L-lysine) [10]
Zymomonas mobilis	High ethanol yield, unique ED pathway	Ethanol, D-lactate (140.92 g/L) [3]	Varies by product and engineering

Metabolic Engineering Strategies

GEMs enable the identification of systematic engineering strategies to enhance production in non-model organisms. For non-model organisms, several key approaches have proven successful. Eliminating competing pathways addresses native metabolic dominance, as demonstrated in Z. mobilis where compromising the dominant ethanol pathway enabled D-lactate production exceeding 140 g/L with yields >0.97 g/g glucose [3]. Cofactor balancing optimizes redox metabolism by identifying and addressing cofactor imbalances that limit production. Pathway expansion introduces heterologous reactions to overcome native metabolic gaps, with analyses showing that >80% of 235 target chemicals required fewer than five heterologous reactions for functional pathway construction across five host strains [10]. Regulatory manipulation identifies gene targets for up- or down-regulation to redirect metabolic fluxes, with GEMs successfully predicting knockout and overexpression targets to enhance product yields.

The Dominant-Metabolism Compromised Intermediate (DMCI) chassis strategy represents a particularly innovative approach for engineering recalcitrant non-model organisms. Rather than directly engineering the target biochemical pathway, this method first introduces a low-toxicity but cofactor-imbalanced pathway (such as 2,3-butanediol) to weaken dominant native metabolism, creating an intermediate chassis more amenable to subsequent engineering for target product synthesis [3]. This strategy has successfully expanded the product range of Z. mobilis beyond its native ethanol specialty, demonstrating the power of model-guided engineering for non-model hosts.

Experimental Protocols and Methodologies

Protocol 1: GEM-Enabled Metabolic Engineering

This protocol outlines the complete workflow for engineering non-model organisms using GEM predictions, based on successful applications in Z. mobilis and other non-model systems [3].

Step 1: Model Reconstruction and Validation

• Obtain genome sequence and annotation for the target non-model organism
• Reconstruct draft GEM using automated tools (ModelSEED, RAVEN)
• Manually curate central carbon and energy metabolism based on literature
• Validate model by comparing simulated growth with experimental data under different conditions
• Refine through gap-filling and ensure biomass composition accuracy

Step 2: In Silico Strain Design

• Define production objective and constraints
• Perform flux balance analysis to identify theoretical maximum yield
• Use optimization algorithms (OptKnock, GDLS) to identify gene knockout targets
• Perform flux variability analysis to identify potential overexpression targets
• Analyze cofactor balances and thermodynamic feasibility

Step 3: Genetic Implementation

• Develop genetic tools specific to the non-model organism (shuttle vectors, selection markers)
• Implement gene knockouts using CRISPR or homologous recombination systems
• Introduce heterologous genes with appropriate expression elements
• Verify genetic modifications through sequencing and functional assays

Step 4: Experimental Validation and Model Refinement

• Cultivate engineered strains in controlled bioreactors
• Measure growth, substrate consumption, and product formation
• Perform metabolomic analysis to validate intracellular flux predictions
• Compare experimental yields with model predictions
• Refine model based on discrepancies to improve predictive capability

Protocol 2: Enzyme-Constrained Model Implementation

This protocol details the development and application of enzyme-constrained models, which enhance flux prediction accuracy by incorporating proteomic limitations [3].

Step 1: kcat Value Collection and Curation

• Extract organism-specific kcat values from BRENDA or SABIO-RK databases
• Use computational tools (AutoPACMEN, DLkcat) to predict missing kcat values
• Manually curate kcat values for central metabolic enzymes from literature
• Apply appropriate correction factors for temperature and pH differences

Step 2: Model Construction

• Integrate kcat values with stoichiometric GEM
• Add enzyme mass balance constraints
• Set total protein content constraint based on experimental measurements
• Implement the metabolic and enzyme capacity coupling

Step 3: Model Simulation and Analysis

• Simulate growth and production under enzyme constraints
• Identify enzyme-limited reactions in metabolic pathways
• Compare predictions with stoichiometric model results
• Validate predictions against experimental flux measurements

Step 4: Model Application

• Identify protein burden limitations in heterologous pathways
• Optimize expression levels using enzyme cost considerations
• Predict trade-offs between metabolic efficiency and enzyme investment
• Guide promoter engineering and ribosomal binding site optimization

Visualization and Analysis of Modeling Results

Effective visualization of GEM simulations and omics data integration is essential for interpreting complex metabolic behaviors and communicating insights. GEM-Vis provides an animated representation of time-course metabolomic data within metabolic network maps, using fill levels of metabolite nodes to intuitively represent concentration changes [45]. This approach enables researchers to identify dynamic metabolic patterns and transient behaviors that might be overlooked in static analyses. For human microbiome research, MicroMap offers a manually curated network visualization capturing the metabolism of over 250,000 microbial reconstructions, containing 5,064 unique reactions and 3,499 unique metabolites, including 98 drugs [43]. This resource enables intuitive exploration of microbiome metabolism and visualization of computational modeling results.

Visualization techniques also enable comparative analysis of metabolic capabilities across different microbes. When comparing the metabolic maps for Bacilli and Verrucomicrobia, it becomes evident that Bacilli possess significantly more drug-metabolizing capabilities [43]. Heatmaps of relative reaction presence further enhance this comparative approach, revealing differences in metabolic capabilities among related species, such as the varying abilities of Pseudomonas species to mediate Fluorouracil metabolism [43]. For flux analysis, visualization of longitudinal timeseries data can create frame-by-frame animations that highlight flux changes in sign and magnitude, helping to identify candidate pathways of interest based on their dynamic behavior [43].

Figure 2: GEM Visualization and Analysis Workflow. The process integrates computational predictions with visualization tools to generate testable biological insights, with iterative refinement based on experimental validation.

Essential Research Toolkit

Table 3: Key Research Reagents and Computational Resources for GEM Development

Resource Category	Specific Tools/Reagents	Function/Purpose	Application Examples
Database Resources	Rhea, KEGG, BiGG Models	Biochemical reaction data, stoichiometry	Mass- and charge-balanced reaction equations [10]
Modeling Software	COBRA Toolbox, CellDesigner	Constraint-based modeling, network visualization	Flux prediction, network mapping [43]
Quality Assessment	MEMOTE	Standardized model testing	Quality evaluation of draft reconstructions [3]
Genetic Engineering	CRISPR-Cas systems, Shuttle vectors	Genome editing, heterologous expression	Implementing model-predicted modifications [3] [44]
Analytical Techniques	LC-MS/MS, GC-MS	Metabolite quantification	Validation of intracellular metabolite levels [45]
Flux Analysis	13C-MFA, Isotopic tracers	Experimental flux determination	Validation of predicted flux distributions [3]
Visualization Platforms	MicroMap, ReconMap	Interactive metabolic maps	Contextualizing modeling results [43]
Rediocide C	Rediocide C, MF:C46H54O13, MW:814.9 g/mol	Chemical Reagent	Bench Chemicals
N-Boc-Ibrutinib-d4	N-Boc-Ibrutinib-d4, MF:C27H30N6O3, MW:490.6 g/mol	Chemical Reagent	Bench Chemicals

The field of GEM development and application continues to evolve rapidly, with several emerging trends shaping future research directions. Machine learning integration is enhancing GEM predictive capabilities, with algorithms increasingly employed for pathway design, enzyme kinetics prediction, and omics data interpretation [46]. Multi-scale modeling approaches are expanding to incorporate regulatory networks, signaling pathways, and multi-cellular systems, moving beyond pure metabolic representations. Automated reconstruction pipelines are accelerating model development for non-model organisms, reducing the manual curation burden. Community standards and shared resources are promoting model reproducibility and interoperability across research groups.

For non-model organisms specifically, several challenges and opportunities deserve emphasis. Genetic tool development remains a significant bottleneck, requiring organism-specific optimization of transformation protocols, selection markers, and expression systems [44]. Knowledge gaps in metabolic capabilities continue to hinder accurate model reconstruction, necessitating integrated experimental and computational approaches for functional annotation. Strain stability and industrial robustness present additional challenges for scaling up laboratory successes to industrial production [3] [44].

In conclusion, GEMs have transformed our approach to engineering microbial cell factories, providing a powerful framework for in silico design and flux prediction. For non-model organisms with attractive native capabilities, GEMs offer a pathway to systematically evaluate, engineer, and optimize metabolic performance. As reconstruction methods become more automated and simulation techniques more sophisticated, the application of GEMs to diverse non-model organisms will continue to expand the repertoire of microbial cell factories for sustainable bioproduction. The integration of GEMs with synthetic biology tools and high-throughput experimentation creates a powerful platform for advancing the bio-based economy, turning non-model microorganisms into efficient producers of valuable chemicals, materials, and pharmaceuticals.

In the burgeoning bioeconomy, the shift from fossil-based resources to sustainable biomanufacturing has placed microbial cell factories (MCFs) at the forefront of industrial biotechnology [7] [9]. While model organisms like Escherichia coli and Saccharomyces cerevisiae have been traditional workhorses, non-model microorganisms are increasingly recognized as superior chassis for many applications due to their innate resilience, diverse metabolic capabilities, and ability to utilize a broader range of feedstocks [1] [3]. The development of these non-model microbes into efficient production platforms hinges on the strategic design and implementation of biosynthetic pathways [3]. Pathway construction—the process of assembling genetic sequences to enable the microbial production of target compounds—fundamentally occurs through three paradigms: leveraging native pathways, introducing heterologous pathways from other organisms, or designing entirely de novo pathways not found in nature [7] [47]. This review provides a technical guide to these pathway construction strategies, framed within the context of advancing non-model organisms as next-generation microbial cell factories.

Native Pathway Engineering

Fundamental Concepts and Applications

Native pathway engineering involves the optimization of pre-existing metabolic routes within a host organism to overproduce a target metabolite. This approach leverages the host's innate enzymatic machinery, minimizing the need for extensive genetic manipulation and reducing metabolic burden [7]. Native pathways are particularly exploited for the production of primary metabolites such as organic acids, amino acids, and enzymes, where the host already possesses the fundamental genetic blueprint [7]. For example, lactic acid bacteria (LAB) like Lactobacillus sp. naturally produce lactic acid, and Corynebacterium glutamicum has been optimized for decades for the industrial production of amino acids like L-glutamate and L-lysine [7] [10].

Key Engineering Strategies

Optimizing native pathways requires enhancing carbon flux toward the desired product while minimizing diversion to competing pathways and byproduct formation. Key strategies include:

• Promoter Engineering: Replacing native promoters with stronger or inducible versions to enhance the expression of rate-limiting enzymes [3].
• Gene Overexpression: Amplifying the copy number of key structural genes within the pathway.
• Feedback Inhibition Relief: Engineering allosteric regulation sites on enzymes to desensitize them from inhibition by end-products [7].
• Competitive Pathway Knockdown: Using CRISPRi or other methods to downregulate genes in competing metabolic branches [10].

A prime example in a non-model host involves engineering the innate Entner-Doudoroff (ED) pathway in Zymomonas mobilis to improve ethanol yield and rate [3]. However, a significant challenge in native engineering, especially in non-model hosts, is the presence of dominant natural pathways that can rigidly channel carbon flux, making redirection difficult [3].

Quantitative Analysis of Native Metabolic Capacities

The innate potential of different microbial chassis to produce chemicals via native metabolism can be evaluated and compared using genome-scale metabolic models (GEMs). The table below summarizes the maximum theoretical yield (YT) for selected valuable chemicals in various industrial hosts, calculated under aerobic conditions with D-glucose as the carbon source [10].

Table 1: Maximum Theoretical Yields (Y_T) of Selected Chemicals in Different Microbial Chassis via Native Metabolism

Target Chemical	E. coli	S. cerevisiae	C. glutamicum	B. subtilis	P. putida
L-Lysine (mol/mol glc)	0.80	0.86	0.81	0.82	0.77
L-Glutamate (mol/mol glc)	0.82	0.91	0.87	0.85	0.79
Sebacic Acid (mol/mol glc)	0.67	0.71	0.65	0.66	0.63
Putrescine (mol/mol glc)	0.75	0.81	0.76	0.77	0.72
Mevalonic Acid (mol/mol glc)	0.69	0.74	0.68	0.67	0.65

Heterologous Pathway Implementation

Rationale and Host Selection

Heterologous biosynthesis involves transferring genetic material encoding a biosynthetic pathway from a native host (which may be slow-growing, uncultivable, or genetically intractable) into a technically superior heterologous host [48]. This strategy is indispensable for accessing the vast therapeutic potential of natural products like polyketides, nonribosomal peptides, and isoprenoids, which are often produced by organisms unsuitable for industrial fermentation [48]. Successful examples include the production of the antimalarial drug precursor artemisinic acid in S. cerevisiae and the synthesis of steviol glycosides (natural sweeteners) in engineered yeast [7] [48].

Selecting an appropriate heterologous host is a critical first step. Heuristics for selection include:

• Phylogenetic Similarity: Pathways from eukaryotic sources often express better in eukaryotic heterologous hosts (e.g., yeast) due to similar cellular environments and protein processing machinery [48].
• Genetic Tool Availability: The ease of genetic manipulation is a primary consideration. Well-characterized hosts like E. coli and S. cerevisiae are often chosen for this reason [48] [1].
• Native Metabolic Precursor Availability: The host must supply sufficient pools of required precursor metabolites (e.g., acetyl-CoA, malonyl-CoA) to feed the heterologous pathway [48] [10].

Experimental Workflow and Methodologies

The generalized workflow for establishing heterologous production involves a multi-step process of pathway identification, genetic construction, and functional screening.

Diagram 1: Heterologous Pathway Workflow

Step 1: Gene Cluster Identification and DNA Acquisition. The biosynthetic gene cluster (BGC) for the target compound must be identified in the native producer through genome mining and sequencing. The genetic material can be obtained by constructing genomic DNA libraries, direct PCR amplification, or, increasingly, by total gene synthesis [48].

Step 2: Host Selection and Vector Construction. The selected heterologous host is transformed with plasmids or chromosomal integrations carrying the foreign genes. For complex pathways, this often requires coordinated expression of multiple genes, which can be achieved using multi-gene expression vectors or through chromosomal integration [48] [3].

Step 3: Functional Expression and Screening. A significant challenge is ensuring the functional expression of all pathway enzymes, which may require codon optimization, selection of appropriate promoters and ribosome binding sites, and co-expression of chaperones. High-throughput screening is then used to identify successful producers [48].

Pathway Refinement and Balancing

Initial pathway construction rarely yields optimal titers. Subsequent refinement is necessary and may involve:

• Enzyme Engineering: Modifying enzymes to improve catalytic efficiency, substrate specificity, or stability in the new host [47].
• Metabolic Flux Optimization: Using tools like CRISPRi/a to fine-tune the expression levels of each pathway gene, preventing the accumulation of toxic intermediates or the wasteful overexpression of non-rate-limiting enzymes [10].
• Cofactor Balancing: Engineering the host's central metabolism to supply the necessary redox cofactors (NADPH/NADH, ATP) in the required stoichiometry [10] [3].

De Novo Pathway Design

Principles and Computational Tools

De novo pathway design represents the frontier of metabolic engineering, moving beyond the reconstruction of natural pathways to the creation of entirely new metabolic routes for both natural and "non-natural" products [47]. This approach employs a retro-biosynthetic analysis, analogous to organic chemistry retrosynthesis, where a target molecule is deconstructed stepwise into simpler, biologically available precursors [47]. The process identifies potential biotransformations for each step, drawing from the vast diversity of known enzyme-catalyzed reactions.

Computational tools are indispensable for navigating the immense theoretical space of possible pathways. Key algorithms and databases include:

• BNICE (Biochemical Network Integrated Computational Explorer): Generates novel biochemical pathways using a set of expert reaction rules derived from the Enzyme Commission (EC) classification system [47].
• ReBiT (Retro-Biosynthetic Tool): A database containing hundreds of unique structures and chemical conversions to aid in pathway design [47].
• Pathway Tools Software: A comprehensive suite that manages pathway/genome data and can be used for metabolic reconstruction and analysis [49] [50]. Its capabilities include predicting metabolic pathways from a genome sequence and performing route searches across metabolic networks [50].

Implementation and Case Studies

De novo pathways are typically assembled from a combination of natural, engineered, and promiscuous enzyme parts [47]. A classic example is the design and construction of a synthetic pathway for 1,3-propanediol in E. coli, which combined genes from S. cerevisiae and Klebsiella pneumoniae [47]. Another is the production of isopropanol in E. coli through a non-native pathway that was computationally designed and experimentally implemented [47].

The successful implementation of a de novo designed pathway for D-lactate production in the non-model bacterium Zymomonas mobilis demonstrates the power of this approach. The innate dominant ethanol pathway was circumvented by first constructing a dominant-metabolism compromised intermediate-chassis (DMCI). This involved introducing a low-toxicity but cofactor-imbalanced 2,3-butanediol pathway to drain central metabolites, after which the high-yield D-lactate pathway was installed, achieving a titer of over 140 g/L from glucose [3].

Diagram 2: De Novo Design Process

The Scientist's Toolkit: Essential Research Reagents and Solutions

The advancement of pathway construction, especially in non-model organisms, relies on a suite of essential reagents and methodologies. The following table details key components of the metabolic engineer's toolkit.

Table 2: Research Reagent Solutions for Pathway Construction

Reagent / Tool Category	Specific Examples	Function & Application
Genome Editing Tools	CRISPR-Cas Systems (Cas9, Cas12a), MAGE, SAGE	Enables precise gene knock-ins, knock-outs, and replacements essential for pathway insertion and host engineering [1] [3].
DNA Assembly & Vector Systems	Gibson Assembly, Golden Gate Shuffle, Yeast Assembly, Plasmid Vectors	Facilitates the construction of multi-gene pathways and expression vectors for heterologous and de novo pathways [48] [47].
Bioinformatics Software	Pathway Tools, BNICE, ReBiT, GEM Construction Tools	Used for in silico pathway prediction, retrosynthetic design, and metabolic flux simulation [49] [47] [50].
Omics Data Analysis Platforms	Pathway Tools Omics Dashboard, Multi-Omics Cellular Overview	Allows visualization and integration of transcriptomics, proteomics, and metabolomics data on pathway diagrams to guide engineering [49] [50].
Genome-Scale Metabolic Models (GEMs)	iZM547 (for Z. mobilis), iML1515 (for E. coli)	Quantitative models for predicting metabolic fluxes, yields, and identifying gene knockout/upregulation targets [10] [3].
RO-09-4609	RO-09-4609, MF:C21H24N2O4, MW:368.4 g/mol	Chemical Reagent
JCP174	JCP174, MF:C12H12ClNO3, MW:253.68 g/mol	Chemical Reagent

The strategic construction of metabolic pathways—through native optimization, heterologous transfer, or de novo design—is the cornerstone of developing performant microbial cell factories from non-model organisms. While each approach presents distinct challenges, the convergence of advanced genome-editing tools, powerful computational algorithms, and systems metabolic engineering principles is progressively overcoming these barriers. The future of the field lies in the deeper integration of automation and artificial intelligence with biotechnology, which will accelerate the design-build-test-learn cycle [9]. This will enable the creation of customized, robust, and highly efficient non-model chassis, ultimately paving the way for a sustainable, bio-based economy.

The Design-Build-Test-Learn (DBTL) Cycle for Systematic Strain Development

The Design-Build-Test-Learn (DBTL) cycle represents a systematic framework in synthetic biology and metabolic engineering for developing efficient microbial cell factories. This iterative process has revolutionized strain development by enabling data-driven optimization of complex biological systems. While traditional microbial engineering has relied on well-characterized model organisms like Escherichia coli and Saccharomyces cerevisiae, the biotechnological potential of non-model microbes remains largely untapped due to limited synthetic biology toolsets and fundamental knowledge [51]. These non-traditional hosts often possess innate physiological and metabolic capabilities that make them ideally suited for specific industrial applications, including utilization of unconventional carbon sources, resistance to process inhibitors, and native production of valuable compounds [1]. The DBTL cycle provides a structured methodology to overcome the challenges associated with engineering these less-characterized organisms, facilitating their development into efficient microbial chassis for bio-based production.

Implementing the DBTL cycle for non-model organisms requires addressing unique challenges throughout each phase. The Design phase faces limited genomic annotation and poorly characterized regulatory elements. The Build phase struggles with underdeveloped genetic tools and low transformation efficiencies. The Test phase encounters unknown metabolic network structures and physiological constraints. Finally, the Learn phase is complicated by incomplete biological context for interpreting multi-omic data. Despite these hurdles, advances in biofoundries, multi-omic analyses, and automation are increasingly making non-model organisms accessible for systematic engineering [51] [52]. This technical guide explores how the DBTL framework can be adapted to harness the unique biotechnological potential of non-model microbes, with particular emphasis on strategies that address their specific limitations.

Core Principles and Process Flow

The DBTL cycle operates as an iterative engineering process where each iteration generates knowledge that informs subsequent cycles, progressively optimizing strain performance. In a fully automated implementation, known as a biofoundry, this process can rapidly generate and evaluate hundreds of microbial strains [51] [52]. The cycle integrates computational design, genetic construction, phenotypic characterization, and data analysis into a continuous feedback loop that systematically expands biological understanding while improving production metrics.

The following diagram illustrates the core structure of the DBTL cycle and the key activities at each stage:

The Knowledge-Driven DBTL Approach for Non-Model Organisms

A significant advancement in DBTL implementation is the knowledge-driven approach, which incorporates upstream investigations to inform the initial design phase. This strategy is particularly valuable for non-model organisms where limited prior knowledge exists. For example, researchers developing a dopamine production strain in E. coli implemented an in vitro cell lysate system to test different relative enzyme expression levels before moving to in vivo engineering [53]. This knowledge-driven DBTL approach enabled mechanistic understanding of pathway limitations and more efficient strain optimization, resulting in a dopamine production strain capable of producing 69.03 ± 1.2 mg/L, a 2.6 to 6.6-fold improvement over previous state-of-the-art production [53].

For non-model organisms, this knowledge-driven approach might include preliminary multi-omic analyses (genomics, transcriptomics, proteomics, metabolomics) to understand native metabolic networks and regulatory elements [51] [1]. By incorporating fundamental discovery into the DBTL cycle, researchers can generate the necessary biological insights to guide engineering strategies in organisms with poorly characterized genetics and metabolism. This "metabolism-centric" view facilitates the deployment of microbial cell factories beyond the typical landscape of target products and traditional hosts [51].

Phase 1: Design – Rational Planning of Microbial Cell Factories

Computational Tools and In Silico Design Strategies

The Design phase establishes the computational foundation for strain engineering through in silico modeling and bioinformatic analysis. For non-model organisms, this phase begins with genome sequencing and annotation to identify potential metabolic pathways, regulatory elements, and genetic parts [1]. Tools like genome-scale metabolic models (GSMMs) enable constraint-based analysis of metabolic networks, prediction of gene knockout targets, and identification of optimal pathways for target compound production [52]. When comprehensive models are unavailable for non-model hosts, comparative genomics with related model organisms can provide initial insights.

Pathway design involves selecting and optimizing metabolic routes for target compound production. For non-model organisms, this may include leveraging unique native pathways that provide competitive advantages. Key considerations include: precursor availability, cofactor balancing, energy requirements, and potential toxic intermediates. Ribosome Binding Site (RBS) engineering represents a powerful strategy for fine-tuning relative gene expression in synthetic pathways. Research has demonstrated that modulating the Shine-Dalgarno sequence without interfering with secondary structures can effectively control translation initiation rates in bi-cistronic systems [53]. For non-model organisms, characterization of native RBS sequences and identification of constitutive promoter elements are essential preliminary steps.

Specialized Tools for Non-Model Organisms

Developing genetic toolsets for non-model organisms requires specialized approaches:

• CRISPR-Cas systems adapted for non-model hosts enable precise genome editing [1] [54]
• Mobile genetic elements like transposons can facilitate genetic manipulation in challenging systems
• Broad-host-range vectors allow initial testing of genetic parts when host-specific systems are unavailable
• Metagenomic screening identifies novel enzymes and pathways from unculturable microbes [54]

Phase 2: Build – Genetic Construction and Strain Engineering

DNA Assembly and Library Generation Methods

The Build phase translates computational designs into physical DNA constructs and engineered strains. For non-model organisms, establishing reliable transformation protocols is a critical first step. Once achieved, multiple DNA assembly methods can be employed:

• Gibson Assembly: Allows simultaneous assembly of multiple DNA fragments with overlapping homology regions, though complex assemblies with long fragments may present challenges [55]
• Golden Gate Assembly: Uses Type IIS restriction enzymes to create seamless assemblies, enabling standardized modular cloning
• Ligase Chain Reaction (LCR): Employed in automated biofoundries for high-throughput assembly of genetic constructs [52]

When working with non-model organisms, library generation often focuses on modulating gene expression levels through promoter engineering, RBS variation, or gene copy number control. For example, in the development of dopamine production strains, researchers implemented high-throughput RBS engineering to optimize the expression levels of HpaBC (4-hydroxyphenylacetate 3-monooxygenase) and Ddc (L-DOPA decarboxylase) [53].

Genome Reduction Strategies for Chassis Development

Genome reduction represents a valuable top-down approach for developing optimized microbial chassis from non-model organisms. This strategy systematically removes "unnecessary" genes and genomic regions to enhance desirable properties. Key benefits include:

• Enhanced genomic stability through deletion of mobile genetic elements [1]
• Improved metabolic efficiency by reducing cellular maintenance requirements [1]
• Simplified metabolic background for easier analysis and engineering [1]
• Higher transformation efficiency in streamlined genomes [1]

Notable examples include the development of an IS-free E. coli strain that showed 20-25% improvement in recombinant protein production [1], and Streptomyces albus with 15 native antibiotic gene clusters deleted, resulting in two-fold higher production of heterologously expressed biosynthetic gene clusters [1].

Phase 3: Test – Phenotypic Characterization and Analytics

Cultivation Methods and High-Throughput Screening

The Test phase involves cultivating engineered strains under controlled conditions and measuring performance metrics. For non-model organisms, medium optimization is often necessary to support robust growth and production. Standardized cultivation formats enable reproducible comparison between strains:

• Microtiter plates (24, 48, 96-well) for high-throughput screening
• Bench-scale bioreactors for process parameter optimization
• Specialized cultivation systems for non-model organisms with unique physiological requirements

In automated biofoundries, cultivation processes are increasingly roboticized, enabling parallel testing of hundreds of microbial strains [52]. For example, in the optimization of dopamine production, researchers used minimal medium with controlled carbon sources and selective antibiotics to evaluate strain performance [53].

Analytical Techniques for Metabolic Engineering

Comprehensive analytical methods are essential for characterizing engineered strains:

• Chromatography techniques (HPLC, GC) for quantifying metabolites, substrates, and products
• Mass spectrometry for identification and quantification of metabolic intermediates [52]
• Spectrophotometric assays for enzyme activities and metabolic fluxes
• Fluorescence and bioluminescence measurements for reporter gene expression [55]
• Multi-omic analyses (transcriptomics, proteomics, metabolomics) for systems-level understanding [51] [52]

For the dopamine production case study, analytical methods specifically quantified l-tyrosine (precursor), l-DOPA (intermediate), and dopamine (final product) concentrations, enabling calculation of pathway efficiency and identification of potential bottlenecks [53].

Phase 4: Learn – Data Analysis and Model Refinement

Data Integration and Statistical Analysis

The Learn phase transforms experimental data into actionable knowledge through statistical analysis and model refinement. This involves:

• Multi-omic data integration to connect genotypic changes to phenotypic outcomes [51]
• Metabolic flux analysis (MFA) to quantify pathway activities [52]
• Statistical design of experiments (DoE) to identify significant factors affecting production
• Comparative analysis of high-performing versus low-performing strains

In the dopamine production optimization, the learning phase revealed that GC content in the Shine-Dalgarno sequence significantly impacted RBS strength and translation efficiency, providing mechanistic insights for future design iterations [53].

Machine Learning and Artificial Intelligence Applications

Advanced computational methods are increasingly applied to extract patterns from complex biological data:

• Machine learning (ML) algorithms predict gene essentiality and optimal engineering targets [52]
• Deep learning models optimize genetic part function [52]
• Generative adversarial networks (GANs) design novel biological sequences [52]
• Physics-informed neural networks (PINNs) integrate biological constraints with data-driven models [52]

These approaches are particularly valuable for non-model organisms where comprehensive biological knowledge is limited, as they can identify non-intuitive relationships between genotype and phenotype.

DBTL Implementation: Case Studies and Quantitative Outcomes

Successful Applications in Metabolic Engineering

The following table summarizes notable DBTL cycle implementations for strain development, highlighting the quantitative improvements achieved:

Table 1: DBTL Cycle Applications in Microbial Strain Development

Target Product	Host Organism	DBTL Innovations	Key Outcomes	Reference
Dopamine	Escherichia coli	Knowledge-driven DBTL with in vitro lysate studies + RBS engineering	69.03 ± 1.2 mg/L (2.6 to 6.6-fold improvement)	[53]
C5 chemicals from L-lysine	Corynebacterium glutamicum	Systems metabolic engineering within DBTL framework	Enhanced biosynthesis of valuable compounds	[56]
Recombinant proteins	IS-free E. coli	Genome reduction for improved genetic stability	20-25% increase in TRAIL and BMP2 production	[1]
Heterologous natural products	Streptomyces albus	Deletion of 15 native antibiotic gene clusters	2-fold higher production of 5 heterologous BGCs	[1]
Biosensor development	E. coli MG1655	Iterative DBTL with split-lux operon design	Functional biosensor with specific inducibility	[55]

Experimental Protocol: Knowledge-Driven DBTL for Pathway Optimization

Based on the successful dopamine production case study [53], the following detailed protocol exemplifies a knowledge-driven DBTL approach:

Phase 1: Design - In Silico Pathway Design and RBS Library Planning

• Identify target metabolic pathway and required enzymatic activities
• Select candidate genes from suitable sources (native or heterologous)
• Design RBS library with varying Shine-Dalgarno sequences while maintaining coding sequence
• Plan bicistronic designs for coordinated expression of pathway enzymes
• Use UTR Designer or similar tools for computational RBS strength prediction

Phase 2: Build - Construct Assembly and Strain Engineering

• Clone pathway genes into appropriate expression vectors using Gibson assembly or similar methods
• Generate RBS variant library through degenerate oligonucleotides or synthesized DNA fragments
• Transform constructs into production host (e.g., E. coli FUS4.T2 for tyrosine-derived products)
• Verify constructs through colony PCR and sequencing
• Prepare high-throughput cultivation formats for screening

Phase 3: Test - Cultivation and Analytical Characterization

• Inoculate strains in minimal medium with appropriate carbon source (e.g., 20 g/L glucose)
• Add necessary supplements (e.g., 0.2 mM FeClâ‚‚, 50 μM vitamin B₆ for dopamine pathway)
• Induce gene expression at optimal growth phase (e.g., with 1 mM IPTG)
• Monitor growth metrics (OD600) throughout cultivation
• Sample at appropriate time points for metabolite analysis
• Quantify products and intermediates via HPLC or LC-MS
• Calculate production titers, yields, and productivity metrics

Phase 4: Learn - Data Analysis and Model Refinement

• Correlate RBS sequences with production metrics
• Identify optimal expression levels for each pathway enzyme
• Analyze relationship between RBS sequence features (GC content, Shine-Dalgarno strength) and protein expression
• Formulate hypotheses for next DBTL cycle (e.g., additional pathway modifications, regulatory elements)
• Update metabolic models with experimental flux data

Essential Research Reagents and Tools for DBTL Implementation

The following table outlines key reagents and methodologies required for implementing DBTL cycles with non-model organisms:

Table 2: Research Reagent Solutions for DBTL-Based Strain Development

Category	Specific Tools/Reagents	Function in DBTL Cycle	Application Notes
DNA Assembly	Gibson Assembly mix, Golden Gate enzymes, Ligase Chain Reaction (LCR)	Build: Construction of genetic circuits and pathways	LCR preferred in automated biofoundries for high-throughput assembly [52]
Genetic Parts	RBS libraries, promoter collections, terminators	Design: Modulation of gene expression levels	For non-model organisms, native parts often need characterization before use
Host Strains	E. coli MG1655, C. glutamicum, B. subtilis, non-model hosts with desirable traits	Build: Production chassis for pathway implementation	Genome-reduced strains often show improved properties [1]
Selection Markers	Antibiotic resistance genes, auxotrophic complementation markers	Build: Selection of successfully engineered strains	For non-model organisms, marker recycling systems may be necessary
Cultivation Media	Minimal media, defined carbon sources, selective antibiotics	Test: Controlled cultivation conditions	Medium optimization often required for non-model organisms
Analytical Tools	HPLC, GC-MS, LC-MS, plate readers	Test: Quantification of metabolites and performance metrics	Multi-omic analyses provide systems-level insights [51]
Cell-Free Systems	Crude cell lysates, purified enzyme mixes	Design/Test: Preliminary pathway testing without cellular constraints	Enables rapid testing of enzyme combinations before in vivo implementation [53]
Genome Editing	CRISPR-Cas systems, recombinase systems	Build: Targeted genomic modifications	Adaptation required for non-model organisms [1] [54]

Integrated Workflow for Non-Model Organism Engineering

The following diagram illustrates a complete knowledge-driven DBTL workflow specifically adapted for non-model microorganisms, integrating the various components discussed throughout this guide:

The DBTL cycle provides a powerful systematic framework for developing microbial cell factories, with particular value for unlocking the biotechnological potential of non-model organisms. Through iterative design, construction, testing, and learning, researchers can progressively overcome the challenges posed by limited genetic toolsets and incomplete biological knowledge. The integration of multi-omic analyses, automation technologies, and machine learning approaches is accelerating this process, enabling faster development of efficient production strains [51] [52].

Future advancements in DBTL implementation will likely focus on increasing automation and integration across the entire cycle, with biofoundries playing a central role in high-throughput strain development [52]. For non-model organisms, key challenges remain in developing generalizable genetic tools and predictive models that can be adapted across diverse microbial systems. Nevertheless, the continued refinement of DBTL methodologies promises to expand the portfolio of microorganisms available for industrial biotechnology, supporting the transition toward a more sustainable bio-based economy.

The transition from a fossil-based economy to a sustainable, bio-based circular economy is one of the grand challenges of this century [57]. A critical frontier in this transition is the development of microbial cell factories capable of converting abundant one-carbon (C1) compounds—such as CO₂, carbon monoxide (CO), methane (CH₄), formate, and methanol—into value-added chemicals, fuels, and materials [58] [59]. Using C1 feedstocks offers a sustainable alternative to traditional sugar-based biomanufacturing, which competes with food production, and enables the valorization of waste greenhouse gases [6] [60].

While historical metabolic engineering efforts have focused on model organisms like Escherichia coli and Saccharomyces cerevisiae, non-model microorganisms represent a vast reservoir of metabolic diversity and innate physiological robustness [1] [3]. This case study explores the engineering of C1 assimilation pathways within the context of non-model organisms as microbial cell factories, providing an in-depth technical examination of pathway selection, host engineering, and the experimental frameworks required to develop efficient C1-based bioprocesses.

C1 Feedstocks and Assimilation Pathways

C1 substrates vary in their physical state, energy content, and technological readiness for industrial application. The table below summarizes the key characteristics of major C1 feedstocks.

Table 1: Characteristics of Major One-Carbon (C1) Feedstocks

Feedstock	State	Key Sources	Advantages	Challenges
COâ‚‚	Gas	Industrial waste gases, atmosphere	High abundance, carbon neutrality	Requires energy input (e.g., Hâ‚‚, formate) for assimilation [6] [57]
Methanol (CH₃OH)	Liquid	Electrochemical conversion of CO₂, syngas	Water-soluble, avoids gas-liquid transfer issues	Flammability, toxicity, volatility [6] [59]
Formate	Liquid	Electrochemical conversion of COâ‚‚	High solubility, less toxic than methanol	High oxidation state leads to carbon loss as COâ‚‚ [6]
Carbon Monoxide (CO)	Gas	Syngas, steel mill off-gases	High energy content, usable by anaerobes and aerobes	Toxicity, mass transfer limitations [59] [60]
Methane (CHâ‚„)	Gas	Natural gas, biogas	High energy content, potent greenhouse gas	Low solubility, safety risks, mass transfer challenges [6]

Natural C1 Assimilation Pathways

Microorganisms have evolved several natural pathways for C1 assimilation, each with distinct thermodynamic and kinetic properties. Selecting an appropriate pathway is crucial for engineering efficient microbial cell factories.

Table 2: Comparison of Key Natural C1 Assimilation Pathways

Pathway	Key Enzyme(s)	Principal Substrate(s)	ATP Consumed per Pyruvate	Advantages	Disadvantages
Calvin-Benson-Bassham (CBB) Cycle	RuBisCO	COâ‚‚	7	Most common in nature; well-studied	Poor RuBisCO kinetics; high ATP cost [59]
Wood-Ljungdahl Pathway (W-L)	CODH/ACS, FDH	COâ‚‚, CO	1-2	Highly ATP-efficient; anaerobic	Limited to acetogens; slow growth [59]
Reductive Glycine Pathway (rGlyP)	FDH, GCS	COâ‚‚, Formate	2	ATP-efficient; linear and orthogonal topology [6] [59]
Ribulose Monophosphate (RuMP) Cycle	Hexulose-6-phosphate synthase	Methanol	N/A	High carbon efficiency	Complexity of circular, autocatalytic cycle [6] [57]
Serine Cycle	Hydroxypyruvate reductase	Methanol, Formate	N/A	Can be combined with other cycles	Metabolic conflicts with native TCA cycle [6]

Engineering Methodologies for Non-Model Chassis

Host Selection and Genome Reduction

Non-model organisms are often selected as chassis due to their native metabolic capabilities, substrate tolerance, and robustness under industrial process conditions [6] [3]. A systematic workflow is essential for their development.

Diagram 1: Host Development Workflow

Key Steps in Host Development:

• Comprehensive Characterization: The process begins with obtaining a complete genome sequence and annotation, followed by multi-omic analysis (transcriptomics, proteomics, metabolomics, fluxomics) to understand central carbon metabolism and regulation [1].
• Metabolic Modeling: Genome-scale metabolic models (GEMs), sometimes enhanced with enzyme constraints (ecGEMs), are constructed. These models simulate flux distributions, identify metabolic bottlenecks, and guide pathway design. For example, the updated eciZM547 model for Zymomonas mobilis provided superior predictions of proteome-limited growth and flux distributions compared to its predecessor [3].
• Genome Reduction: A top-down approach is commonly used to systematically remove "unnecessary" genomic regions. This process enhances genomic stability by deleting mobile genetic elements (e.g., prophages, insertion sequences), eliminates undesired product synthesis, and can improve growth rates and productivity by reducing the cellular burden of replicating and expressing non-essential DNA [1].

Pathway Implementation and Optimization

Introducing and optimizing C1 assimilation pathways in a new host requires careful balancing of gene expression and metabolic flux.

Diagram 2: Pathway Engineering Workflow

Key Engineering Strategies:

• Enzyme Engineering: Key enzymes in C1 metabolism often have kinetic limitations. For instance, RuBisCO has poor catalytic activity and affinity for COâ‚‚ [59], and NAD⁺-dependent methanol dehydrogenase (Mdh) can be a bottleneck in methylotrophy. Directed evolution has been used to increase the catalytic activity of Mdh by 6.5-fold [61].
• Genetic Tool Development: For non-model hosts, genetic tools must often be developed or adapted. This includes creating plasmid systems, using native C1-inducible promoters, and implementing CRISPR-based genome editing systems. For example, improved CRISPRi and CRISPR genome editing with recyclable markers have been developed for the methylotrophic yeast Komagataella phaffii [61].
• Dynamic Regulation and Metabolic Modeling: To prevent metabolic imbalance and intermediate depletion, dynamic regulation can be implemented. Computational tools like Flux Balance Analysis (FBA), Enzyme Cost Minimization (ECM), and Minimum-Maximum Driving Force (MDF) models help predict steady-state fluxes, optimal enzyme concentrations, and identify pathway bottlenecks [6].

Experimental Protocols for Key Analyses

Protocol: CRISPR-Cas Mediated Genome Integration for Pathway Engineering

This protocol outlines the steps for integrating a heterologous C1 assimilation pathway into the chromosome of a non-model bacterium.

• Guide RNA (gRNA) Design and Vector Construction:

  • Identify a genomic "safe-harbor" locus or a site for targeted gene replacement using the host's genome sequence.
  • Design gRNA sequences with high on-target efficiency and minimal off-target effects.
  • Clone the gRNA sequence into a CRISPR plasmid backbone containing a dCas9 or Cas nuclease gene and a homologous repair template. The repair template should contain the pathway genes, flanked by ~500-1000 bp homology arms corresponding to the target locus.

• Transformation and Selection:

  • Introduce the constructed plasmid into the host organism via electroporation or conjugation.
  • Plate the transformation on selective media containing the appropriate antibiotic.
  • Incubate under optimal conditions for the host until colonies appear.

• Screening and Genotype Verification:

  • Screen colonies by colony PCR using primers that bind outside the homology region and within the inserted pathway genes.
  • Sequence the amplified PCR product to confirm precise, error-free integration.
  • Streak verified colonies on counter-selection media (if applicable) to cure the CRISPR plasmid.

• Phenotypic Validation:

  • Test the engineered strain for its ability to grow with the target C1 compound as the sole carbon source in a minimal medium.
  • Analyze pathway intermediate and product formation using HPLC or GC-MS to confirm functional flux through the new pathway [3] [61].

Protocol: ¹³C-Metabolic Flux Analysis (MFA) to Quantify C1 Pathway Activity

MFA is critical for validating the in vivo activity of an engineered C1 pathway and quantifying its flux relative to native metabolism.

• Tracer Experiment:

  • Grow the engineered strain in a bioreactor or controlled fermenter with the labeled C1 substrate (e.g., ¹³C-Methanol or ¹³C-Formate) as the sole carbon source. Ensure the culture reaches metabolic steady-state (constant growth rate and metabolite concentrations).
  • Harvest cells rapidly during mid-exponential growth phase by centrifugation.

• Sample Processing and Metabolite Extraction:

  • Quench metabolism quickly using cold methanol.
  • Extract intracellular metabolites using a methanol/water/chloroform solvent system.
  • Derivatize proteinogenic amino acids or central metabolites for analysis.

• Mass Spectrometry Analysis:

  • Analyze the derivatized samples using Gas Chromatography-Mass Spectrometry (GC-MS).
  • Measure the mass isotopomer distributions (MIDs) of the key metabolites from central carbon metabolism.

• Computational Flux Estimation:

  • Use a stoichiometric model of the host's metabolic network, including the engineered C1 pathway.
  • Input the experimental MIDs into a flux analysis software (e.g., INCA, OpenFlux).
  • Iteratively fit the model to the experimental data by adjusting metabolic fluxes to find the most probable flux map that explains the observed labeling patterns [3].

Visualization and Toolkit

The Scientist's Toolkit: Essential Reagents and Solutions

Table 3: Key Research Reagent Solutions for Engineering C1 Assimilation

Reagent / Solution	Function	Application Example
CRISPR Plasmid System	Enables precise genome editing (knock-in, knockout, replacement).	Integration of the rGlyP into E. coli or a non-model host [57] [61].
Broad-Host-Range Promoter Libraries (e.g., Anderson Series)	Provides standardized, tunable gene expression across different microbial hosts.	Fine-tuning expression of C1 pathway enzymes in methanotrophs like Methylococcus capsulatus [61].
C1-Defined Minimal Media	Provides essential salts, vitamins, and a single C1 source to force assimilation.	Selecting for and characterizing synthetic methylotrophs or formatotrophs [6].
Homologous Repair Template DNA	Serves as a template for precise genomic integration via homology-directed repair.	Replacing native genes with optimized pathway modules [3].
GC-MS with ¹³C Capability	Analyzes the labeling pattern in metabolites for Metabolic Flux Analysis (MFA).	Quantifying flux through an engineered RuMP cycle versus native metabolism [3].
Cephapirin	Cephapirin, CAS:21593-23-7; 24356-60-3, MF:C17H17N3O6S2, MW:423.5 g/mol	Chemical Reagent
Carbenicillin	Carbenicillin, CAS:4697-36-3; 4800-94-6, MF:C17H18N2O6S, MW:378.4 g/mol	Chemical Reagent

Engineering C1 assimilation pathways into non-model microorganisms represents a promising avenue for advancing sustainable biomanufacturing. The success of this endeavor hinges on a systematic workflow that integrates careful host selection, multi-omic analysis, genome reduction, sophisticated metabolic modeling, and the development of advanced genetic tools. While challenges in carbon conversion efficiency and scale-up remain, the synergistic investigation of natural and synthetic C1-trophic microorganisms, powered by synthetic biology and automation, is poised to unlock the full potential of C1 feedstocks for a circular carbon economy [6] [57] [60]. Future work must continue to bridge foundational discoveries with scalable applications, guided by early techno-economic and life cycle assessments to ensure both economic viability and environmental benefits.

Overcoming Recalcitrance: Strategies for Robustness and High-Titer Production

Genome Reduction for Enhanced Genetic Stability and Predictability

The transition from a fossil-fuel-based economy to a bio-based circular economy requires the development of efficient microbial cell factories (MCFs). While traditional model microbes like Escherichia coli and Saccharomyces cerevisiae are well-established, they often lack essential traits required for different bioprocesses, such as robustness against harsh process conditions and tolerance to high substrate and product concentrations [1]. Non-model microorganisms represent a great resource due to their advantageous native traits and unique repertoire of bioproducts [1]. However, their complexity causes unpredictable cellular interactions, making metabolic modeling and functional predictions challenging [1]. Genome reduction serves as a valuable and powerful approach to reduce this complexity, thereby improving the predictability, controllability, and genetic stability of microbial chassis to meet industry requirements [1]. This technical guide explores the application of genome reduction in developing non-model organisms into efficient, stable, and predictable platforms for biomanufacturing.

Microbial Chassis Development and Genome Reduction Strategies

A microbial chassis is defined as an "engineerable and reusable biological platform with a genome encoding several basic functions for stable self-maintenance, growth, and optimal operation" [1]. Creating an effective chassis requires detailed genomic information, molecular tools, and omics-based technologies [1].

Genome reduction can proceed via two primary approaches (Figure 1):

• Top-Down Approach: Starts from an intact wild-type genome and proceeds with systematic removal of "unnecessary" genes and genomic regions [1]. This method is currently less costly and more straightforward, making it widely used.
• Bottom-Up Approach: Entails designing and building an artificially synthesized genome from scratch, as demonstrated by the JCVI-syn3.0 project that created a minimal cell with only 473 genes [1].

Figure 1: Workflow for chassis development emphasizing genome reduction approaches.

Quantitative Benefits of Genome Reduction in Prokaryotes

Extensive studies on prokaryotic strains have demonstrated multiple advantages of genome reduction (Table 1). These benefits include enhanced genomic stability, improved transformation efficiency, optimization of downstream applications, and increased growth rates and productivity [1].

Table 1: Documented benefits of genome reduction in various prokaryotic organisms

Organism	Genome Reduction Strategy	Key Outcomes	Quantitative Improvements
Escherichia coli	Deletion of error-prone DNA polymerases [1]	Enhanced genomic stability	50% decrease in spontaneous mutation rate [1]
Escherichia coli	Development of IS-free strain [1]	Improved recombinant protein production	25% increase in TRAIL and 20% increase in BMP2 production [1]
Streptomyces albus	Deletion of 15 native antibiotic gene clusters [1]	Heterologous expression of BGCs	~2-fold higher production of 5 heterologous BGCs [1]
Streptomyces lividans	Deletion of 10 endogenous antibiotic encoding clusters [1]	Higher growth rate and antibiotic production	4.5-fold increase in deoxycinnamycin production [1]
Bacillus subtilis	Large-scale genomic deletions [1]	Improved cellular performance	Enhanced growth rates and cell density [1]

Genetic Stability Enhancements Through Targeted Deletions

A primary motivation for genome reduction is enhancing genetic stability, which is crucial for industrial bioprocesses requiring consistent, long-term performance. This is achieved through targeted removal of unstable genetic elements:

• Mobile Genetic Elements: Deletion of insertion sequences (IS) and transposable elements prevents random mutations and product inactivation. An IS-free E. coli strain showed significant improvements in recombinant protein production [1].
• SOS Response Components: Removal of error-prone DNA polymerases reduces mutation rates. In E. coli, this approach decreased spontaneous mutation frequency by 50% [1].
• Antibiotic Resistance Genes: Elimination of unnecessary antibiotic markers simplifies metabolic background and improves safety profile [1].
• Prophages and Virulence Factors: Excision of these elements increases biosafety and reduces potential for lysogenic activation during industrial fermentation [1].

Experimental Evolution for Fitness Recovery

A significant challenge in genome reduction is the frequent observation of decreased growth fitness. However, experimental evolution has proven effective in recovering growth performance while maintaining the benefits of a reduced genome (Table 2).

In one notable study, experimental evolution of an E. coli strain with a reduced genome was conducted across nine independent lineages for approximately 1,000 generations [62]. This approach demonstrated that growth rates, which had significantly declined due to genome reduction, could be considerably recovered through adaptation [62].

Table 2: Evolutionary changes in growth parameters of genome-reduced E. coli

Evolutionary Parameter	Observation	Interpretation
Growth Rate Recovery	8 of 9 evolved lineages showed significantly improved growth rates [62]	Compensatory evolution can overcome fitness costs of genome reduction
Carrying Capacity	All evolved lineages showed decreased saturated population densities [62]	Trade-off between growth rate and carrying capacity
Mutation Accumulation	2-13 mutations per lineage with no common mutations across all lineages [62]	Diverse evolutionary paths can lead to similar fitness improvements
Transcriptome Reorganization	Common evolutionary direction despite diversified gene categories [62]	Homeostatic transcriptome architecture conservation

The evolutionary process followed divergent mechanisms across lineages, with genome resequencing identifying 65 fixed mutations across the nine evolved populations [62]. Despite this diversity in mutational patterns, transcriptome reorganization showed a common evolutionary direction and conserved chromosomal periodicity [62].

Figure 2: Experimental evolution workflow for fitness recovery in reduced genomes.

CRISPR/Cas9-Based Genome Editing Protocols for Non-Model Organisms

The CRISPR/Cas9 system has revolutionized genome editing in both model and non-model organisms due to its precision, efficiency, and scalability [63]. This section outlines key protocols for implementing CRISPR/Cas9 in non-model microbes.

sgRNA Design and Validation

Successful gene targeting depends largely on sgRNA design, which should maximize on-target Cas9 activity while minimizing off-target effects [63].

Protocol Steps:

• Target Selection: Identify target sites within 30 bp of the desired modification site [63].
• Bioinformatic Design: Use online tools (e.g., CHOPCHOP, CRISPR Design Tool) to identify guide sequences with high predicted activity and minimal off-target effects [63].
• PAM Consideration: For Streptococcus pyogenes Cas9, the protospacer adjacent motif (PAM) sequence is NGG [63]. If no suitable guides are available, consider Cas9 orthologs with different PAM requirements.
• In Vitro Testing: Clone sgRNAs into expression vectors or generate via in vitro transcription for validation using in vitro cutting assays with Cas9 protein [63].

Delivery Methods for CRISPR/Cas9 Components

Multiple delivery strategies can be employed, each with distinct advantages:

• Plasmid-Based Expression: Co-expression of sgRNA and Cas9 from plasmids, often including selection markers (e.g., GFP, puromycin) [63].
• Ribonucleoprotein (RNP) Complexes: Delivery of preassembled sgRNA and Cas9 protein to reduce unwanted indel formation and decrease off-target effects [63].
• Viral Vectors: For challenging organisms, viral delivery systems can improve transformation efficiency [63].

Saturation Genome Editing for Functional Analysis

Saturation genome editing (SGE) employs CRISPR-Cas9 and homology-directed repair (HDR) to introduce exhaustive nucleotide modifications at specific genomic sites in multiplex, enabling functional analysis of genetic variants while preserving their native genomic context [64].

Key Applications:

• Functional assessment of single nucleotide variants (SNVs) in coding sequences, introns, and untranslated regions (UTRs) [64]
• High-throughput evaluation of variant effects on cell fitness [64]
• Generation of comprehensive functional scores for all possible mutations in a target gene [64]

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key research reagents and their applications in genome reduction studies

Reagent / Tool	Function / Application	Technical Notes
CRISPR/Cas9 System	Targeted genome editing [63]	Requires sgRNA (20-base variable domain) and Cas9 nuclease; PAM: NGG for SpCas9 [63]
Homology-Directed Repair (HDR) Template	Precise gene insertion or modification [63] [64]	Can be single-stranded oligodeoxynucleotides (ssODNs) or double-stranded DNA templates [63]
SgRNA Expression Vector	Delivery and expression of guide RNA [63]	Typically includes RNA polymerase III promoter (U6 or H1) and sgRNA scaffold [63]
Next-Generation Sequencing (NGS)	Verification of edits and off-target analysis [64]	Essential for barcoded deep sequencing of edited regions [63]
Metabolic Model	In silico prediction of metabolic capabilities [1]	Constraint-based reconstruction and analysis (COBRA) of genome-scale models [1]
Antifungal agent 124	Antifungal agent 124, MF:C26H21Cl2F2N5O4S, MW:608.4 g/mol	Chemical Reagent
Tetromycin C5	Tetromycin C5, MF:C50H65NO13, MW:888.0 g/mol	Chemical Reagent

Genome reduction represents a powerful strategy for transforming non-model microorganisms into efficient microbial cell factories with enhanced genetic stability and predictability. By systematically removing non-essential genomic elements, researchers can create simplified chassis platforms with reduced complexity, improved genetic stability, and more predictable behavior. While early generations of genome-reduced strains often face fitness challenges, experimental evolution provides a robust method for recovering growth performance while maintaining beneficial traits. The integration of advanced genome editing tools like CRISPR/Cas9 with high-throughput screening and computational modeling accelerates the development of these tailored production hosts. As synthetic biology and automation technologies continue to advance, genome reduction will play an increasingly important role in creating specialized microbial chassis for sustainable biomanufacturing in the bioeconomy era.

The shift from a fossil-fuel-based economy to a bio-based circular economy represents a critical response to global warming, requiring the development of sustainable bioprocesses that leverage microbial cell factories [1]. While traditional model microorganisms like Escherichia coli and Saccharomyces cerevisiae are well-established in industrial biotechnology, they often lack essential traits required for different bioprocesses, including robustness against harsh process conditions, tolerance to high substrate and product concentrations, and the ability to utilize diverse feedstocks [1]. Non-model microbes represent a tremendous resource due to their advantageous native traits and unique metabolic capabilities, but their development into efficient production platforms faces a significant obstacle: dominant native metabolic pathways that compete with engineered production routes for carbon and energy [3].

This technical guide explores the Dominant-Metabolism Compromised Intermediate-Chassis (DMCI) strategy as a systematic approach to overcome these limitations. Framed within broader thesis research on non-model organisms as microbial cell factories, we present a paradigm for engineering recalcitrant microorganisms by first constructing an intermediate chassis with intentionally compromised native metabolism before introducing target biosynthetic pathways [3]. This approach enables researchers to circumvent the innate metabolic dominance that often limits the production of non-native biochemicals, thereby expanding the repertoire of efficient biorefinery chassis available for sustainable biochemical production.

Conceptual Framework: Principles of the Intermediate Chassis Strategy

The Dominant Metabolism Problem

Many non-model microorganisms with excellent industrial characteristics possess inherently dominant metabolic pathways that efficiently channel carbon and energy toward specific native products. A prime example is Zymomonas mobilis, an ethanologenic bacterium that utilizes the Entner-Doudoroff pathway under anaerobic conditions with extraordinary ethanol production efficiency [3]. While this makes it an outstanding native ethanol producer, this dominant pathway severely restricts the titer and rate of other valuable biochemicals that must compete for the same precursor metabolites [3]. Similar challenges exist across diverse microbial hosts where native metabolic networks have evolved for optimal growth and survival rather than for industrial production of heterologous compounds.

The fundamental challenge lies in the metabolic rigidity of these systems. Direct engineering approaches, such as knocking out key enzymes in dominant pathways or introducing strong heterologous pathways, often result in cellular stress, genetic instability, or compensatory evolutionary responses that restore flux through the native route [3]. The DMCI strategy addresses this limitation through a more sophisticated, iterative approach that gradually rewires cellular metabolism while maintaining viability and stability.

Core Principles of the DMCI Strategy

The Dominant-Metabolism Compromised Intermediate-Chassis strategy operates on three fundamental principles:

• Controlled Flux Diversion: Instead of complete pathway elimination, the approach uses moderate interventions to partially redirect carbon flux from dominant native pathways without causing cellular collapse [3].
• Cofactor Imbalance Engineering: Introduction of pathways that create deliberate cofactor imbalances can strategically drain resources from dominant metabolism while preparing the metabolic network for future engineering [3].
• Stepwise Metabolic Reprogramming: The strategy employs sequential metabolic modifications that collectively compromise the native dominant pathway while building toward a chassis capable of high-yield production of target compounds [3].

Figure 1: Conceptual workflow comparing traditional direct engineering approaches with the DMCI strategy.

Case Study: Engineering Zymomonas mobilis as a Biorefinery Chassis

The Native Metabolic Dominance Challenge in Z. mobilis

Zymomonas mobilis possesses exceptional industrial characteristics, including high sugar uptake rate, high ethanol tolerance, and low biomass formation, making it an attractive candidate for biorefinery applications [3]. However, its metabolism is dominated by an exceptionally efficient ethanol production pathway comprising pyruvate decarboxylase (PDC) and alcohol dehydrogenases (ADHs) that channel up to 95-97% of carbon from glucose to ethanol [3]. Previous attempts to engineer this organism for alternative products, including promoter replacement strategies and competing pathway introduction, achieved only partial success with approximately 65% overall yield from glucose redirected to target products [3].

DMCI Implementation Strategy

The successful implementation of the DMCI strategy in Z. mobilis involved the following key steps:

• Systems Biology Analysis: An improved genome-scale metabolic model (eciZM547) was developed with enzyme constraints to simulate flux distribution dynamics and identify potential intervention points [3].
• Strategic Pathway Selection: A 2,3-butanediol (2,3-BDO) pathway was selected as the initial intervention due to its low cellular toxicity but ability to create NADH/NAD+ cofactor imbalance, which strategically stresses the native ethanol pathway [3].
• Intermediate Chassis Construction: The 2,3-BDO pathway was introduced to create an intermediate chassis with compromised ethanol metabolism, reducing ethanol yield while maintaining cellular viability [3].
• Target Pathway Integration: A D-lactate production pathway was subsequently introduced into the intermediate chassis, resulting in dramatically improved production metrics compared to direct engineering approaches [3].

Quantitative Outcomes of DMCI Implementation

Table 1: Performance comparison of Z. mobilis engineered strains for D-lactate production [3]

Strain Type	D-lactate Titer (g/L)	Yield (g/g glucose)	Ethanol Byproduct (g/L)	Notes
Wild-type Z. mobilis	Not applicable	Not applicable	~97% of carbon	Native metabolism baseline
Direct Engineering	< 50	< 0.70	> 30	Promoter replacement of PDC
DMCI Strategy	> 140	> 0.97	Minimal	2,3-BDO intermediate chassis

The data demonstrates the remarkable efficacy of the DMCI approach, with the engineered strain achieving exceptional D-lactate titers exceeding 140 g/L from glucose and 104.6 g/L from corncob residue hydrolysate, with yields greater than 0.97 g/g glucose [3]. Techno-economic analysis and life cycle assessment confirmed the commercial feasibility and greenhouse gas reduction capability of this lignocellulosic D-lactate production process [3].

Experimental Methodologies: Implementing the DMCI Strategy

Genome-Scale Metabolic Modeling for Strategy Design

High-quality genome-scale metabolic models (GEMs) play critical roles in the rational design of microbial cell factories within the Design-Build-Test-Learn cycle of synthetic biology [3]. For Z. mobilis, the iZM516 model was improved and updated to eciZM547 by integrating enzyme constraints using ECMpy2 and Kcat values from AutoPACMEN [3]. This enzyme-constrained model provided superior predictive accuracy compared to traditional stoichiometric models, correctly predicting proteome-limited growth and carbon flux distribution under different conditions [3]. The modeling workflow included:

• Manual curation of Gene-Protein-Reaction relationships from multiple databases
• Integration of enzyme kinetic parameters
• Validation through 13C-metabolic flux analysis (MFA)
• Simulation of pathway modifications to identify optimal intervention strategies

Genetic Tool Development for Non-Model Organisms

Efficient genome-editing tools are essential for implementing the DMCI strategy in non-model organisms. For Z. mobilis, this involved leveraging multiple editing systems [3]:

• CRISPR-Cas12a System: Heterologous CRISPR-Cas12a for precise genome editing
• Endogenous CRISPR-Cas: Native Type I-F CRISPR-Cas system exploitation
• Repair Pathways: Microhomology-mediated end joining (MMEJ) pathways for enhanced editing efficiency

Similar approaches have been successfully applied to actinomycetes using bacteriophage-derived recombinase systems, meganuclease I-SecI from Saccharomyces cerevisiae, and oligonucleotide recombineering [65].

Analytical and Validation Methods

Robust analytical methods are essential for characterizing intermediate chassis and validating metabolic rewiring:

• 13C-Metabolic Flux Analysis: Quantifies intracellular flux distributions in wild-type and engineered strains [3]
• Metabolite Profiling: HPLC/MS methods for quantifying extracellular metabolites and pathway intermediates
• Enzyme Activity Assays: Determination of key enzyme activities in native and introduced pathways
• Omics Integration: Transcriptomics and proteomics to verify system-wide changes in gene expression and protein abundance

Figure 2: Experimental workflow for implementing the DMCI strategy in non-model organisms.

Complementary Approaches: Genome Reduction for Chassis Streamlining

The DMCI strategy can be effectively combined with genome reduction approaches to further optimize microbial chassis. Genome reduction minimizes unpredictable interactions between synthetic devices and host cells by removing non-essential genes, thereby improving genetic stability and predictability [1] [65]. Successful applications include:

• IS-element Removal: Development of an IS-free E. coli strain enhanced production of recombinant proteins by 20-25% [1]
• Antibiotic Cluster Deletion: Streptomyces albus with 15 native antibiotic gene clusters deleted showed 2-fold higher production of heterologously expressed biosynthetic gene clusters [1]
• SOS Response Elimination: Deletion of error-prone DNA polymerases in E. coli increased genomic stability and decreased spontaneous mutation rates by 50% [1]

Table 2: Genome reduction outcomes in prokaryotic microorganisms [1]

Organism	Reduction Approach	Deleted Elements	Outcome
E. coli	IS-element free	Insertion sequences	25% increase in TRAIL production, 20% increase in BMP2 production
E. coli	SOS response elimination	Error-prone DNA polymerases	50% decrease in spontaneous mutation rate
Streptomyces albus	Antibiotic cluster deletion	15 native antibiotic gene clusters	2-fold higher heterologous BGC production
Streptomyces lividans	Antibiotic cluster deletion	10 endogenous antibiotic clusters	4.5-fold increase in deoxycinnamycin production

Extended Applications: Streptomyces Chassis for Polyketide Production

The principles of the DMCI strategy find application across diverse non-model organisms. In Streptomyces species, chassis development has focused on eliminating competing native pathways to enhance production of target type II polyketides (T2PKs) [66]. A recent study identified Streptomyces aureofaciens J1-022 as a promising chassis due to its native high-yield production of chlortetracycline [66]. Implementation involved:

• Competing Pathway Elimination: In-frame deletion of two endogenous T2PKs gene clusters to create a pigmented-faded host (Chassis2.0) [66]
• Heterologous Pathway Integration: Introduction of oxytetracycline, actinorhodin, and flavokermesic acid biosynthetic gene clusters [66]
• Cryptic Cluster Activation: Direct activation of unidentified biosynthetic gene clusters for novel compound discovery [66]

The resulting Chassis2.0 demonstrated remarkable versatility, achieving a 370% increase in oxytetracycline production compared to commercial strains, while efficiently producing diverse tri-ring, tetra-ring, and penta-ring type II polyketides [66].

Research Reagent Solutions: Essential Tools for Chassis Engineering

Table 3: Key research reagents and their applications in DMCI implementation

Reagent/Category	Function	Example Applications
Genome Editing Tools	Precision genome modification	CRISPR-Cas12a, endogenous Type I-F CRISPR-Cas, meganuclease I-SecI [3] [65]
Metabolic Modeling Software	Metabolic flux prediction and pathway design	ECMpy2 (for enzyme-constrained models), AutoPACMEN (Kcat values) [3]
Analytical Standards	Metabolite quantification and validation	D-lactate, 2,3-butanediol, ethanol, organic acids for HPLC/MS [3]
Cloning Systems	Heterologous pathway integration	ExoCET technology for E. coli-Streptomyces shuttle plasmids [66]
Enzyme Assay Kits	Metabolic enzyme activity quantification	Pyruvate decarboxylase, alcohol dehydrogenase, pathway-specific enzymes

The Dominant-Metabolism Compromised Intermediate-Chassis strategy represents a paradigm shift in metabolic engineering of non-model microorganisms. By systematically overcoming the limitations imposed by dominant native metabolism, this approach enables the development of efficient microbial cell factories from organisms with exceptional native traits but previously recalcitrant metabolic networks. The successful implementation in Zymomonas mobilis and Streptomyces species demonstrates the broad applicability of this strategy for bio-based production of diverse biochemicals.

Future developments will likely focus on integrating artificial intelligence and machine learning with genome-scale models to improve prediction accuracy, expanding the toolkit for genetic manipulation of diverse non-model organisms, and combining DMCI with automated strain engineering for high-throughput chassis development. As the field advances, the DMCI strategy will play an increasingly important role in expanding the repertoire of microbial chassis available for sustainable bioproduction, ultimately supporting the transition to a circular bioeconomy.

Addressing Metabolic Conflicts and Improving Carbon Efficiency

The transition from a fossil-fuel-based economy to a sustainable, bio-based circular economy is a critical step toward achieving net-zero CO2 emissions, and industrial biotechnology serves as a key enabling technology for this shift [1]. While traditional microbial workhorses like Escherichia coli and Saccharomyces cerevisiae have proven their value, they often lack the innate robustness and unique metabolic capabilities required for many industrial processes. Non-model microorganisms represent a vast reservoir of advantageous traits and unique bioproducts, making them promising candidates for next-generation microbial cell factories [1]. However, their native metabolisms are rarely optimized for industrial production, leading to inherent metabolic conflicts and suboptimal carbon efficiency. These conflicts arise from competition for precursors, energy, and reducing equivalents between cell growth and product synthesis, and from rigid regulatory networks that resist flux rewiring.

Addressing these challenges is paramount for developing efficient bio-based production. This guide details advanced strategies to resolve metabolic conflicts and enhance carbon efficiency in non-model organisms, framing them within the essential context of microbial chassis development for a sustainable future.

Microbial Chassis Development for Non-Model Organisms

Developing a non-model microbe into a reliable cell factory requires a systematic approach to create a robust and engineerable microbial chassis. A microbial chassis is defined as a reusable biological platform whose genome is edited for strengthened performance under specified conditions [1]. The general workflow involves comprehensive characterization and refinement of the host organism.

Figure 1: Workflow for Developing a Non-Model Microbial Chassis

A crucial strategy in chassis development is genome reduction, a top-down approach that systematically removes "unnecessary" genomic regions. This process simplifies cellular complexity, improves genetic stability by eliminating mobile genetic elements like insertion sequences (IS), and can enhance growth and production performance by reducing the metabolic burden of replicating and transcribing non-essential DNA [1]. For instance, developing an IS-free E. coli strain boosted recombinant protein production by 20-25% [1]. In Streptomyces albus, the deletion of 15 native antibiotic gene clusters simplified the metabolic background and doubled the production of heterologously expressed biosynthetic gene clusters [1].

Hierarchical Metabolic Engineering Strategies

Modern metabolic engineering operates across multiple biological hierarchies to rewire cellular metabolism comprehensively. This approach, termed hierarchical metabolic engineering, allows for precise intervention from the molecular to the systems level.

Table 1: Hierarchical Metabolic Engineering for Carbon Efficiency

Hierarchy	Engineering Focus	Key Strategies for Addressing Metabolic Conflicts	Example Outcomes
Part Level	Enzymes	Enzyme engineering, cofactor specificity switching, promoter strength tuning.	Increased catalytic efficiency and altered flux control [67].
Pathway Level	Synthetic Pathways	Modular pathway engineering, decoupling growth from production, dynamic regulation.	C. glutamicum: 212 g/L L-lactic acid [67].
Network Level	Metabolic Flux	Cofactor engineering, deleting competing pathways, genome-scale model (GEM)-guided predictions.	OptKnock algorithms identify knockouts for metabolite overproduction [67].
Genome Level	Genomic Architecture	Genome reduction, insertion sequence (IS) element deletion, recoding.	IS-free E. coli: 25% higher recombinant protein yield [1].
Cell Level	Host Physiology	Transporter engineering, tolerance engineering, co-culture systems.	Engineered C. glutamicum for 223.4 g/L L-lysine [67].

Genome-Scale Modeling and Computational Design

Quantitative computational methods are indispensable for predicting and resolving metabolic conflicts. Genome-scale metabolic models (GEMs) are comprehensive representations of an organism's metabolism that allow for in silico simulation of flux distributions.

The ET-OptME framework addresses a key limitation of classical stoichiometric models by incorporating enzyme efficiency and thermodynamic feasibility constraints. This integration delivers more physiologically realistic intervention strategies, significantly improving prediction accuracy and precision—by at least 106% and 292%, respectively, compared to traditional methods [68].

For breaking stoichiometric yield limits, the QHEPath algorithm is a powerful tool. This quantitative heterologous pathway design algorithm evaluates biosynthetic scenarios to identify reactions that, when introduced, can push product yields beyond the native host's theoretical maximum. A systematic analysis of 300 products across 5 industrial organisms revealed that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions [69]. Thirteen common engineering strategies were identified, categorized as carbon-conserving and energy-conserving, with five strategies being effective for over 100 different products [69].

Figure 2: Computational Workflow for Breaking Yield Limits

Experimental Protocols for Key Analyses

Protocol: Implementing a Genome Reduction Strategy

Objective: To create a genetically stable and streamlined chassis from a non-model organism by deleting non-essential genomic regions, including mobile elements and endogenous antibiotic clusters.

Materials:

• Strain: Wild-type non-model organism (e.g., Streptomyces sp.).
• Bioinformatics Tools: Genome annotation software (e.g., RAST), BLAST for comparative genomics.
• Genetic Tools: CRISPR-Cas9 system or other programmable nuclease specific to the host.
• Reagents: Primers for PCR, DNA assembly reagents, antibiotics for selection, agarose gel electrophoresis materials.

Procedure:

• Genome Analysis & Target Identification:
  • Sequence and annotate the genome.
  • Identify targets for deletion: prophages, insertion sequences (IS), genomic islands, and native antibiotic biosynthetic gene clusters (BGCs) using tools like antiSMASH.
  • In silico prediction of gene essentiality via homology search against model organisms.

• Vector Construction:

  • For each target region, design two homology arms (approx. 1 kb each) flanking the deletion site.
  • Clone these arms into a suicide vector or a CRISPR plasmid, framing a counter-selectable marker (e.g., sacB).

• Genetic Transformation & Deletion:

  • Introduce the constructed vector into the host strain via conjugation or transformation.
  • Select for single-crossover integrants.
  • Counter-select for double-crossover events to yield deletion mutants.

• Validation & Phenotyping:

  • Confirm deletions via PCR and sequencing.
  • Phenotypically characterize the mutant: measure growth rate, assess genetic stability over multiple generations, and test production of target compounds versus byproducts.

Application: This protocol was successfully applied to Streptomyces lividans, where the deletion of 10 endogenous antibiotic clusters resulted in a higher growth rate and a 4.5-fold increase in the production of the heterologously expressed antibiotic deoxycinnamycin [1].

Protocol: Computational Identification of Yield-Breaking Pathways

Objective: To use the QHEPath algorithm to identify heterologous reactions that can overcome the stoichiometric yield limit of a target product in a host organism.

Materials:

• Software & Models: A curated Genome-Scale Metabolic Model (GEM) of the host organism, the COBRA Toolbox, the QHEPath web server or algorithm.
• Data: A high-quality, mass- and charge-balanced universal biochemical reaction database (e.g., the curated BiGG database used in CSMN [69]).

Procedure:

• Model Curation and Integration:
  • Ensure the host GEM is high-quality and validated with experimental data.
  • If using a standalone database, integrate it with the host GEM to create a comprehensive metabolic network.

• Define Simulation Parameters:

  • Specify the target product and the carbon substrate (e.g., glucose).
  • Set simulation constraints: aerobic/anaerobic conditions, nutrient uptake rates.

• Calculate Native Yield Limit:

  • Use Flux Balance Analysis (FBA) with the host GEM to compute the producibility yield (Y_P0), which is the maximum theoretical yield achievable by the native network.

• Run QHEPath Analysis:

  • Execute the QHEPath algorithm on the integrated model (e.g., via the QHEPath web server).
  • The algorithm will systematically search the reaction database to find heterologous reactions whose introduction increases the maximum theoretical yield (YmP) beyond YP0.

• Analyze and Prioritize Strategies:

  • Review the output list of suggested heterologous reactions.
  • Analyze the proposed pathways (e.g., carbon-conserving cycles like non-oxidative glycolysis) for physiological feasibility.
  • Prioritize strategies that require the fewest genetic modifications for the largest yield increase.

Application: This approach successfully predicted the introduction of the non-oxidative glycolysis (NOG) pathway to enhance the yield of products like poly(3-hydroxybutyrate) (PHB) in E. coli beyond its native stoichiometric limit [69].

The Scientist's Toolkit: Essential Reagents and Solutions

Table 2: Key Research Reagent Solutions for Metabolic Engineering

Reagent / Tool	Function / Application	Example Use in Non-Model Organisms
CRISPR-Cas9 Systems	Precise gene knock-out, knock-in, and editing.	Tailored CRISPR systems are essential for genome reduction and pathway engineering in genetically recalcitrant non-model hosts [22].
Heterologous Pathway Kits	Pre-assembled genetic modules for common metabolic steps.	Accelerates the construction of pathways like the mevalonate (MVA) pathway for isoprenoid synthesis in microalgae [70].
Genome-Scale Model (GEM) in silico	A computational representation of metabolism for predicting flux and targets.	Used with algorithms like OptKnock and QHEPath to identify gene knockout and heterologous reaction targets [69] [67].
Inducible Promoter Systems	Precisely control the timing and level of gene expression.	Crucial for dynamic pathway control to decouple growth and production phases, minimizing metabolic conflict [1].
Cofactor Regeneration Systems	Enzymatic or metabolic modules to balance NADPH/NADH and ATP.	Engineering soluble transhydrogenase (UdhA) in E. coli to balance NADPH/NADH pools for improved product synthesis [67].
Metabolomics Standards	Certified reference materials for LC/MS and GC/MS.	Used for absolute quantification of intracellular metabolites to identify kinetic bottlenecks and thermodynamic bottlenecks in pathways [68].

Case Study: Isoprenoid Biosynthesis in Microalgae

Microalgae represent a compelling case of non-model organisms where carbon efficiency is paramount, as they directly fix COâ‚‚. Isoprenoid biosynthesis here competes directly with primary carbon assimilation.

Metabolic Conflicts:

• Precursor Competition: The central precursor, pyruvate, is required for both the methylerythritol phosphate (MEP) pathway (for isoprenoids) and the TCA cycle (for energy generation).
• Energy/Reducing Power Demand: The MEP pathway is ATP and NADPH intensive, competing with carbon fixation and other anabolic processes.

Engineering Solutions Applied:

• Precursor Push: Overexpression of rate-limiting enzymes in the MEP pathway, such as DXS (1-deoxy-D-xylulose-5-phosphate synthase), to pull carbon flux toward isoprenoid precursors (IPP and DMAPP) [70].
• Competitive Pathway Knockout: At the network level, knockout of storage metabolism genes (e.g., for starch or lipid synthesis) can reduce carbon diversion, making more pyruvate and G3P available for the MEP pathway [70].
• Cofactor Balancing: At the part and network levels, introducing NADPH-regenerating enzymes or engineering MEP pathway enzymes to use NADH instead of NADPH can resolve conflicts related to reducing power [70].
• Subcellular Compartmentalization: At the cell level, engineering the pathway within the chloroplast can leverage the high NADPH and ATP levels generated by the light reactions of photosynthesis, directly aligning the product synthesis with the energy source [70].

Figure 3: Resolving Metabolic Conflicts in Microalgal Isoprenoid Pathways

Enhancing Tolerance to Substrates, Products, and Process Conditions

The shift from a fossil-fuel-based economy to a sustainable, bio-based circular economy is a critical global imperative, with industrial biotechnology serving as a key enabling technology [1]. Within this framework, non-model microorganisms represent a vast and largely untapped resource for the production of chemicals, fuels, and materials due to their unique metabolic capabilities and inherent resilience to harsh conditions [1] [6] [71]. However, the industrial potential of these organisms is often limited by their sensitivity to stressors encountered during fermentation, including inhibition by high concentrations of substrates and desired products, as well as challenging process conditions like low pH and high temperature [72] [73] [74].

Microbial robustness—the ability of a strain to maintain stable production performance (titer, yield, and productivity) in the face of genetic, metabolic, and environmental perturbations—is distinct from and more critical than mere tolerance, which only describes the ability to grow or survive under stress [72]. Enhancing this robustness is therefore a cornerstone for developing non-model microbes into efficient microbial cell factories (MCFs). This technical guide outlines the core mechanisms, experimental methodologies, and engineering strategies for enhancing the tolerance of non-model organisms, providing a roadmap for their domestication and industrial application.

Core Mechanisms of Microbial Stress Tolerance

Microorganisms deploy a complex array of physiological and metabolic responses to cope with environmental stress. Understanding these core mechanisms is the first step toward rationally engineering robust strains.

Cell Envelope and Membrane Remodeling

The cell membrane is the primary barrier against external stress. Bacteria and yeasts dynamically adjust their membrane composition to maintain integrity and fluidity under stress.

• Fatty Acid Profile Regulation: Under acetic acid stress, acetic acid bacteria (AAB) increase the proportion of unsaturated fatty acids (UFAs), cyclopropane fatty acids, and lysophospholipids, while also extending fatty acid chain length. These changes decrease membrane fluidity and strengthen integrity, forming a more stable barrier [74].
• Phospholipid Headgroup Adjustment: AAB adjust membrane phospholipid composition by increasing phosphatidylcholine (PC) and phosphatidylglycerol (PG) content while reducing phosphatidylethanolamine (PE). This increases membrane hydrophilicity, limiting the passive transport of lipophilic molecules like acetic acid into the cell [74].
• Extracellular Polymeric Substance (EPS) Production: Biofilms and capsules, primarily composed of extracellular polysaccharides (EPS), serve as critical physical barriers. In Acetobacter spp., capsular polysaccharides (CPS) and pellicle polysaccharides (PPS) function as biofilm-like barriers that restrict the diffusion of acetic acid into the cell, significantly enhancing resistance [74].

Transport and Efflux Systems

Active transport is a key line of defense for reducing intracellular concentrations of toxic compounds.

• Proton Motive Force (PMF)-Driven Efflux: In AAB, an efflux pump dependent on the proton motive force expels acetic acid from the cell independently of ATP. This system is linked to the respiratory chain, where the oxidation of ethanol generates a proton gradient used to power acetic acid export [74].
• ABC Transporters: ATP-binding cassette (ABC) transporters use ATP hydrolysis to actively transport substrates against concentration gradients. The ABC transporter AatA is significantly induced by acetic acid in AAB, and its deletion drastically reduces acid resistance. Comparative genomics suggests that acid-resistant Komagataeibacter spp. possess more genes encoding ABC transporters than less tolerant strains [74].

Stress Response Proteins and Global Regulators

Cells reprogram their gene expression in response to stress, often orchestrated by global and specific transcription factors.

• Global Transcription Machinery Engineering (gTME): This approach involves engineering broad-acting components of the transcription machinery, such as sigma factors in bacteria or the TATA-binding protein in yeast, to globally reprogram cellular metabolism for enhanced tolerance. For example, engineering the sigma factor δ70 (rpoD) improved E. coli tolerance to ethanol and SDS, and similar strategies have been applied in Zymomonas mobilis and Rhodococcus ruber [72].
• Specific Transcription Factors and Chaperones: Engineering specific regulators, such as the Haa1 transcription factor in S. cerevisiae, can improve tolerance to acetic acid. Additionally, the heterologous expression of global regulators from extremophiles, such as IrrE from Deinococcus radiodurans, has been shown to enhance tolerance to ethanol, butanol, and osmotic stress in E. coli [72]. Upregulation of stress response molecular chaperones is also a common mechanism observed in AAB under multiple stresses [74].

Experimental Protocols for Enhancing Tolerance

This section provides detailed methodologies for key experiments aimed at identifying tolerance mechanisms and generating robust strains.

Adaptive Laboratory Evolution (ALE)

Objective: To generate strains with enhanced tolerance to a specific stressor through serial passaging under selective pressure.

Protocol Workflow:

Detailed Procedure:

• Initial Culture: Inoculate the parental non-model strain (e.g., Yarrowia lipolytica for succinic acid production) into a shake flask containing a standard growth medium supplemented with a sub-lethal concentration of the stressor (e.g., 20 g/L succinic acid at pH 3.5) [73].
• Serial Passaging: Incubate the culture at optimal temperature with shaking. Monitor cell density (OD600) and product accumulation. Once the culture reaches the late logarithmic phase (e.g., OD600 ~8.0), transfer an aliquot of the culture (e.g., 5-10% v/v) into fresh medium with the same or a slightly increased concentration of the stressor [73].
• Progressive Stress Escalation: Gradually increase the stressor concentration over successive transfers. For example, in the evolution of Y. lipolytica, the succinic acid concentration was escalated from 20 g/L to 35 g/L and finally to 50 g/L [73].
• Termination and Isolation: Continue the process until growth and production metrics stabilize over several transfers. From this stabilized population, isolate individual single clones on solid agar plates.
• Phenotypic Screening: Evaluate the isolated clones in parallel fermentations to identify those with superior tolerance and production performance compared to the parental strain. The evolved strain E501 of Y. lipolytica obtained via this method showed a 7.2% increase in succinic acid titer in a bioreactor [73].

Downstream Analysis:

• Genome Resequencing: Sequence the genomes of the evolved strains and compare them to the parental strain to identify potential mutations (e.g., single nucleotide polymorphisms, insertions, deletions) conferring the tolerant phenotype. In Y. lipolytica E501, a mutation was identified in the gene encoding the 26S proteasome regulatory subunit Rpn1 [73].
• Transcriptome Analysis: Perform RNA sequencing (RNA-Seq) on the evolved strain under stress conditions versus the parental strain to identify differentially expressed genes and pathways involved in the stress adaptation [73].

Global Transcription Machinery Engineering (gTME)

Objective: To alter the global transcription network of a cell, leading to the simultaneous activation of multiple stress-responsive genes and a complex tolerant phenotype.

Protocol Workflow:

Detailed Procedure:

• Target Selection: Select a gene encoding a global regulator, such as the sigma factor δ70 (rpoD) in bacteria or the TATA-binding protein (SPT15) in yeast [72].
• Mutant Library Construction: Use error-prone PCR or other mutagenesis techniques to introduce random mutations into the selected gene, creating a vast library of mutant alleles.
• Library Expression: Clone the mutant library into an appropriate expression vector and transform it into the host strain. A control strain with the wild-type allele should be created in parallel.
• High-Throughput Screening: Plate the transformed library onto solid media or grow in liquid culture under the target selective pressure (e.g., high ethanol, low pH, or inhibitory substrates). Screen for clones that exhibit superior growth, fluorescence-linked production markers, or other selectable phenotypes.
• Validation: Isolate the best-performing mutants and characterize their tolerance and production performance in controlled bench-scale bioreactors. For example, an engineered rpoD mutant in Z. mobilis resulted in a two-fold increase in ethanol production under stress [72].

Engineering Strategies for Robustness

Beyond experimental evolution, several rational and semi-rational strategies can be employed to enhance robustness.

Genome Reduction for Stability and Performance

Genome reduction is a top-down approach to create simplified and optimized chassis cells by deleting non-essential genes, including mobile genetic elements and biosynthetic clusters for unwanted by-products.

• Enhanced Genomic Stability: Deleting insertion sequences (IS) and prophages reduces random mutations. For instance, creating an IS-free E. coli strain increased the production of recombinant proteins TRAIL and BMP-2 by 25% and 20%, respectively [1].
• Elimination of Competitive Pathways: In Streptomyces albus, the deletion of 15 native antibiotic gene clusters simplified the metabolic background and doubled the production of heterologously expressed biosynthetic gene clusters [1].
• Improved Cellular Economy: Large-scale deletion of genomic regions can lower the metabolic burden of replicating "junk" DNA, potentially leading to higher growth rates and substrate conversion efficiency [1].

Metabolic Engineering of Central Carbon Metabolism

Fine-tuning core metabolic pathways, such as glycolysis, is crucial for ensuring precursor and energy supply under stressful production conditions.

• Glycolytic Flux Optimization: In an evolved, acid-tolerant Y. lipolytica strain, the co-overexpression of rate-limiting enzymes in the glycolytic pathway further boosted succinic acid production. This rational engineering step, combined with prior ALE, resulted in a strain achieving 112.54 g/L succinic acid at low pH, the highest titer reported for such conditions [73].
• Transport System Engineering: Modulating the expression of genes for glucose transporters (e.g., by engineering the untranslated regions of ptsG in E. coli) can fine-tune substrate uptake rates, preventing metabolic overflow and improving yield and productivity under stress [73].

Quantitative Data on Tolerance Engineering

Table 1: Selected Examples of Enhanced Tolerance and Production in Microorganisms

Host	Stressor	Engineering Strategy	Key Outcome	Citation
Yarrowia lipolytica	Succinic acid, low pH	Adaptive Laboratory Evolution (ALE) + Glycolytic engineering	SA titer: 112.54 g/L; Yield: 0.67 g/g; Productivity: 2.08 g/L/h at pH 3.5	[73]
Zymomonas mobilis	Ethanol (9%)	gTME (engineering of rpoD)	Two-fold increase in ethanol production	[72]
Escherichia coli	Ethanol, Butanol	Heterologous expression of irrE (from D. radiodurans)	10 to 100-fold increased tolerance	[72]
Streptomyces albus	N/A (Metabolic background)	Genome reduction (deletion of 15 antibiotic clusters)	~2-fold increase in production of heterologous BGCs	[1]
Saccharomyces cerevisiae	Ethanol (10%)	gTME (engineering of Rpb7)	40% increase in ethanol titers	[72]

Table 2: Key Mechanisms of Acetic Acid Tolerance in Acetic Acid Bacteria (AAB)

Mechanism	Specific Action	Physiological Effect	Citation
Membrane Alteration	Increased UFAs, PC, PG; decreased PE	Reduced membrane fluidity and passive acid influx	[74]
Efflux Systems	PMF-driven efflux pump; ABC transporter (AatA)	Active expulsion of acetic acid from the cell	[74]
Biofilm Formation	Production of CPS and PPS	Physical barrier restricting acid diffusion	[74]
Enzyme Activity	Enhanced PQQ-ADH/ALDH activity	Efficient channeling of electrons to respiration	[74]
Stress Proteins	Upregulation of molecular chaperones	Protection and refolding of intracellular proteins	[74]

The Scientist's Toolkit: Essential Reagents and Solutions

Table 3: Research Reagent Solutions for Tolerance Engineering

Reagent / Tool	Function / Application	Example Use Case
Error-Prone PCR Kits	Creates random mutagenesis libraries for gene evolution.	Used in gTME to generate mutant libraries of global transcription factors like rpoD [72].
CRISPR-Cas9 Systems	Enables precise genome editing, knock-outs, and multiplexed deletions.	Essential for genome reduction strategies and deleting competitive pathways in non-model hosts [10] [71].
Genome-Scale Metabolic Models (GEMs)	In silico models for predicting metabolic flux, gene essentiality, and engineering targets.	Used to calculate maximum theoretical yields (YT) and identify gene knockout targets for improved production [10].
Plasmid Vectors with Native Promoters	Facilitates heterologous gene expression and metabolic engineering.	Using native C1-inducible promoters in non-model hosts can optimize pathway expression with lower metabolic burden [6].
Fibrous Bed Bioreactor (isFBB)	A system for in situ product removal and long-term cultivation.	Used in ALE of Y. lipolytica to obtain SA-tolerant strains under continuous product stress [73].

Enhancing the tolerance of non-model microorganisms is a multi-faceted challenge that requires an integrated approach. By leveraging a deep understanding of intrinsic tolerance mechanisms—such as membrane remodeling, efflux systems, and stress responses—and applying powerful experimental methodologies like Adaptive Laboratory Evolution and Global Transcription Machinery Engineering, researchers can effectively domesticate these promising hosts. The subsequent application of genome reduction and targeted metabolic engineering further refines these evolved chassis, locking in robust traits and optimizing performance. As genetic toolkits for non-model organisms continue to expand, the systematic engineering of robustness will be paramount to unlocking their full potential, ultimately enabling more efficient, sustainable, and economically viable bioprocesses within a circular bioeconomy.

Integrating Automation and AI for High-Throughput Strain Optimization

The field of microbial biotechnology is undergoing a transformative shift from manual, sequential experimentation to integrated, AI-driven workflows. This evolution is particularly crucial for the development of non-model organisms as microbial cell factories (MCFs). Unlike established model organisms, non-model hosts often possess unique, advantageous native traits—such as specialized metabolic capabilities, stress resistance, and robustness under industrial bioprocess conditions—but are treated as biological "black boxes" due to limited characterization [6]. The integration of automation and artificial intelligence (AI) creates a framework to efficiently decipher this complexity, accelerating the optimization of these promising hosts for a sustainable bioeconomy [9]. This guide details the methodologies and technologies enabling this high-throughput, intelligent approach to strain optimization.

Core Components of an Automated AI-Driven Workflow

An effective high-throughput strain optimization pipeline merges laboratory automation with computational intelligence to create a self-improving experimental cycle.

The Automation Backbone: From Manual to High-Throughput

Automation hardware forms the physical foundation of the workflow, enabling the rapid execution of experiments at scales impossible manually.

• Liquid Handling Systems: Platforms like the Hamilton Microlab Star automate the preparation of cultivation media, lysis buffers, and assay mixtures in microtiter plates (e.g., 96-well format), ensuring precision and reproducibility while freeing researcher time [75].
• Robotic Bioreactor Arrays: Miniaturized and parallelized bioreactor systems (e.g., 24- or 48-bioreactor setups) allow for the simultaneous investigation of numerous growth and production conditions with tight control over parameters like pH, temperature, and feeding.
• High-Throughput Analytics: Integrated systems automatically sample from cultures and analyze them via techniques such as HPLC, mass spectrometry, or plate-reader-based assays to generate the high-quality data required for AI training.

The AI Brain: From Data to Predictive Insight

AI and machine learning (ML) transform raw data into predictive models that guide experimental design, moving beyond simple automation to create self-optimizing systems [76].

• Machine Learning for Predictive Modeling: ML algorithms (e.g., random forests, gradient boosting, neural networks) analyze historical and real-time data to predict strain performance, identify key optimization parameters, and forecast production titers and yields.
• AI Orchestration Layers: Modern tech stacks require a central AI layer that seamlessly exchanges data between systems—from electronic data capture to clinical trial management systems—to support real-time intelligence and decision-making [76]. This creates a unified view for research teams.
• Simulation Engines: Before a single experiment is conducted, simulation models powered by machine learning can identify likely failure points, such as nutrient limitations or metabolic bottlenecks, allowing for proactive redesign of the experimental plan [76].

Table 1: Key AI/ML Models and Their Applications in Strain Optimization

Model Type	Primary Function	Use-Case Example
Flux Balance Analysis (FBA)	Predicts steady-state metabolic fluxes to optimize objectives like biomass formation [6].	Assessing compatibility and energy balance of a new synthetic pathway.
Enzyme Cost Minimization (ECM)	Estimates optimal enzyme and metabolite concentrations to minimize protein investment [6].	Designing a metabolically efficient chassis for a target pathway.
Minimum-Maximum Driving Force (MDF)	Identifies pathways with the highest thermodynamic potential [6].	Selecting the most feasible synthetic C1 assimilation route.

Experimental Design and Protocol for High-Throughput Optimization

The power of automation and AI is fully realized only when coupled with rigorous experimental design. The following protocol, centered on Design of Experiments (DoE), provides a detailed template for a high-throughput optimization campaign.

Protocol: DoE-Based Optimization of a Critical Process Parameter

This protocol outlines the optimization of a lysis buffer for efficient protein recovery from a non-model bacterium, demonstrating a generalizable DoE strategy [75].

1. Experimental Planning and DoE Setup

• Objective Definition: Clearly define the optimization goal (e.g., "Maximize soluble protein concentration and specific enzyme activity from E. coli BL21").
• Factor Selection: Identify critical factors to optimize. In this case, four lysis buffer components were chosen: EDTA, lysozyme, Triton X-100, and polymyxin B [75].
• DoE Software: Use specialized software (e.g., MKS Umetrics MODDE) to generate an experimental plan. The software selects the optimal combination of factor levels to efficiently explore the design space with a minimal number of experiments.

2. Automated Mixture Preparation

• Stock Solutions: Prepare concentrated stock solutions of all selected factors.
• Liquid Handler Programming: Import the experimental plan from the DoE software into a laboratory automation database. Generate worklists and program a liquid handling robot to automatically dispense specified volumes into a 96-deep well plate, ensuring accuracy and reproducibility [75].

3. Cultivation and Cell Harvest

• Strain and Cultivation: Use a defined strain (e.g., E. coli BL21 transformed with a plasmid for heterologous protein expression). Employ controlled fed-batch cultivation systems (e.g., EnBase technology) to achieve reproducible cell growth [75].
• Normalization and Harvest: Measure the optical density (OD600) of the culture. Calculate the harvest volume needed to normalize cell pellets to a standard biomass (e.g., 1 mL of culture at an OD600 of 5). Centrifuge the culture in microtube racks and store the cell pellets at -20°C [75].

4. High-Throughput Lysis and Analysis

• Automated Lysis: Using the liquid handler, resuspend cell pellets in the pre-prepared lysis buffer mixtures.
• Incubation and Clarification: Incubate the plate to allow for cell disruption, then centrifuge to remove cell debris.
• Assay: Automatically transfer supernatant to a new assay plate. Use methods like the bicinchoninic acid (BCA) assay for total soluble protein and a specific enzymatic assay (e.g., for ß-galactosidase activity) to measure the outcome [75].

5. Data Integration and Model Building

• Data Analysis: Import the resulting activity and protein data back into the DoE software.
• Model Generation: The software builds a statistical model (e.g., a multiple linear regression model) that describes the relationship between the factor concentrations and the responses. This model can then identify the optimal buffer composition and predict performance within the tested design space [75].

Figure 1: DoE-based high-throughput optimization workflow for a key process parameter like lysis buffer composition.

The Scientist's Toolkit: Essential Reagents for High-Throughput Workflows

Table 2: Key Research Reagent Solutions for Strain Optimization

Reagent / Material	Function	Example Use in Protocol
Lysis Buffer Components	Chemoenzymatic cell disruption for metabolite/protein release [75].	EDTA (chelating agent), Lysozyme (enzyme), Triton X-100 (detergent) are optimized via DoE for efficient extraction [75].
DoE Software	Plans experiments to efficiently explore multi-parameter space with minimal runs.	Software like MKS Umetrics MODDE designs the experimental matrix for lysis buffer optimization [75].
EnBase / Fed-Batch Media	Provides quasi-constant feeding in microtiter plates, enabling reproducible growth.	Used in preculture and main culture to achieve standardized cell material for lysis experiments [75].
Automation-Compatible Assays	Enable high-throughput measurement of key performance indicators (KPIs).	BCA assay for total soluble protein and a specific enzymatic assay (e.g., for ß-galactosidase) are used as KPIs [75].

Data Management and AI Integration for Self-Optimizing Systems

The sheer volume of data generated by high-throughput workflows necessitates robust data management and advanced AI integration to close the loop between experimentation and design.

Data as the Foundation

• Centralized Data Lakes: All data from automated platforms—including genomic, metabolomic, process parameters, and analytical results—must be aggregated into a centralized, standardized repository. This "data lake" becomes the resource for AI training.
• FAIR Data Principles: Data must be curated to be Findable, Accessible, Interoperable, and Reusable to maximize its utility for both current and future AI models [77].

Closing the Loop with AI and ML

The integration of AI transforms a linear workflow into a cyclic, self-improving system. AI models use the data from completed experiments to propose new, more optimal strains and conditions for the next iteration.

Figure 2: The self-optimizing feedback loop for strain development, where AI uses experimental data to generate improved designs.

• AI-Generated Hypotheses: Predictive models can prioritize the most promising genetic edits (e.g., promoter swaps, gene knockouts) or process conditions to test next, dramatically accelerating the design-build-test-learn (DBTL) cycle [77].
• Towards Self-Optimizing Trials: The ultimate expression of this integration is a system where AI not only suggests designs but also autonomously adjusts ongoing experiments in real-time—for example, by reallocating resources based on performance data or adapting protocols to gather endpoint data for promising sub-populations more quickly [76].

The integration of automation and AI is no longer a futuristic concept but a present-day necessity for unlocking the full potential of non-model organisms in the bioeconomy. By adopting the high-throughput workflows, experimental designs, and data-driven strategies outlined in this guide, researchers can transition from slow, sequential experimentation to fast, parallelized, and intelligent strain optimization. This paradigm shift promises to expedite the development of robust microbial cell factories, transforming sustainable biomanufacturing from a ambitious goal into a practical reality.

From Lab to Market: Evaluating Performance and Commercial Viability

The strategic selection and engineering of microbial chassis are pivotal for developing economically viable bioprocesses. While model organisms like Escherichia coli and Saccharomyces cerevisiae have historically dominated industrial biotechnology, non-model microorganisms offer untapped potential due to advantageous native traits such as substrate utilization range, resilience under industrial conditions, and innate high-level production of valuable compounds. This whitepaper provides a technical guide for benchmarking the performance of microbial cell factories, with a specific focus on non-model organisms. We synthesize frameworks for comparing key performance metrics—titer, yield, and productivity (TYP)—across diverse hosts, detail experimental protocols for reliable evaluation, and visualize the essential workflows for systematic chassis development. By integrating computational modeling, advanced genetic tool development, and metabolic engineering strategies, this resource aims to equip researchers with the methodologies needed to navigate the expanding landscape of non-model chassis for sustainable bioproduction.

The transition from a fossil-based economy to a sustainable, bio-based circular economy is a critical global priority [1]. Microbial cell factories (MCFs) are central to this transition, enabling the production of chemicals, materials, and fuels from renewable feedstocks. For decades, metabolic engineering efforts have concentrated on a handful of model organisms, such as Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae, due to their well-annotated genomes and extensive genetic toolkits [3] [1]. However, these traditional hosts often lack the robust phenotypes required for industrially efficient, low-cost bioprocesses.

Non-model microorganisms represent a vast and largely unexplored resource. Many possess innate, advantageous traits such as:

• Enhanced Robustness: Tolerance to high temperatures, extreme pH, and inhibitory compounds found in low-cost feedstocks like lignocellulosic hydrolysates.
• Specialized Metabolism: Native pathways for high-level synthesis of target products, eliminating the need for extensive pathway engineering.
• Utilization of Diverse Feedstocks: Ability to grow on non-food biomass and one-carbon (C1) compounds, supporting more sustainable biorefinery concepts [6] [78].

Examples of promising non-model chassis include Zymomonas mobilis, known for its high glycolytic flux and ethanol tolerance; Halomonas spp., which grow under high-salt conditions that minimize contamination risks in open bioreactors; and Kluyveromyces marxianus, a thermotolerant, fast-growing yeast capable of fermenting a wide range of sugars [3] [78] [79]. The development of these hosts into reliable platforms is accelerated by advances in synthetic biology, systems biology, and genome-editing technologies [1].

A systematic approach to benchmarking their performance against established models and each other is essential for guiding rational chassis selection and engineering. This requires a deep understanding of the interlinked TYP metrics and the methodologies for their accurate determination.

Theoretical Frameworks for Performance Benchmarking

Defining the Critical Triad: Titer, Yield, and Productivity

The performance of a microbial cell factory is quantitatively assessed by three primary metrics:

• Titer: The concentration of the target product in the fermentation broth, typically expressed in grams per liter (g/L). A high titer is critical for reducing downstream processing costs.
• Yield: The efficiency of substrate conversion into the product, expressed as grams of product per gram of substrate (g/g) or moles per mole (mol/mol). Yield directly determines raw material costs and process economics.
• Productivity: The rate of product formation, expressed as grams per liter per hour (g/L/h). High productivity increases bioreactor output and reduces capital costs.

These metrics are often locked in a trade-off [80] [81]. For instance, strategies that maximize yield (e.g., growth-coupling) may slow down cell growth, thereby reducing volumetric productivity. Similarly, achieving a high titer might require prolonged fermentation times, also impacting productivity. The optimal balance of TYP is dictated by the target product's market value and the specific bioprocess design.

Computational Modeling for Predicting Metabolic Potential

In silico models are indispensable for predicting the theoretical performance of a chassis before embarking on costly experimental work.

• Genome-Scale Metabolic Models (GEMs): GEMs mathematically represent the metabolic network of an organism. Using Flux Balance Analysis (FBA), researchers can calculate two key metrics:

  • Maximum Theoretical Yield (Y~T~): The maximum product yield per substrate when all cellular resources are devoted to production, ignoring maintenance and growth [10].
  • Maximum Achievable Yield (Y~A~): A more realistic yield that accounts for resources allocated for cellular growth and maintenance energy [10].

  A comprehensive study calculated Y~T~ and Y~A~ for 235 chemicals in five representative industrial microorganisms (B. subtilis, C. glutamicum, E. coli, P. putida, S. cerevisiae), providing a valuable resource for initial host selection [10]. For example, for L-lysine production from glucose, S. cerevisiae showed the highest Y~T~ (0.857 mol/mol), while C. glutamicum is the preferred industrial producer due to its actual in vivo flux and high tolerance [10].

• Enzyme-Constrained Models (ecModels): These extend GEMs by incorporating enzymatic and proteomic constraints, offering more accurate predictions of metabolic fluxes. For instance, an enzyme-constrained model of Z. mobilis (eciZM547) accurately simulated a shift from glucose-limited to proteome-limited growth, which was not captured by the traditional GEM [3].

• Dynamic FBA and Optimization: These frameworks model time-dependent changes in metabolite concentrations and fluxes in a batch culture. They can identify the maximum theoretical productivity and optimal dynamic flux profiles that balance growth and production phases, often suggesting that two-stage fermentation strategies can nearly double productivity for compounds like succinate [81].

The diagram below illustrates the typical workflow integrating computational and experimental approaches for chassis benchmarking.

Quantitative Performance Benchmarking Across Hosts

Systematic computational analyses provide a foundational comparison of the inherent metabolic capacities of different microorganisms. The following table summarizes the maximum theoretical yields (Y~T~) for a selection of valuable chemicals produced by both model and non-model hosts, highlighting the potential advantages of non-traditional chassis.

Table 1: Maximum Theoretical Yields (Y~T~) of Selected Chemicals in Different Microbial Chassis

Target Chemical	Host Strain	Maximum Theoretical Yield (Y~T~) (mol/mol Glucose)	Key Chassis Feature	Reference
L-Lysine	Saccharomyces cerevisiae	0.857	Native L-2-aminoadipate pathway	[10]
	Bacillus subtilis	0.821	Diaminopimelate pathway	[10]
	Corynebacterium glutamicum	0.810	Industrial producer, diamino-pimelate pathway	[10]
	Escherichia coli	0.799	Diaminopimelate pathway	[10]
Succinic Acid	Escherichia coli (Engineered)	Varies with strain	Model organism, extensive tools	[81]
	Actinobacillus succinogenes (Native)	Varies with strain	Natural high-yield producer	[81]
D-Lactate	Zymomonas mobilis (Engineered)	> 0.97 g/g	Dominant metabolism compromised chassis	[3]
Polyhydroxybutyrate (PHB)	Halomonas bluephagenesis	High accumulation (64.74 g/L titer reported)	Growth under non-sterile conditions	[78]
L-Lactic Acid	Kluyveromyces marxianus (Engineered)	0.81 g/g (yield achieved)	Acid tolerance, thermotolerance	[79]
Vitamin B6 (PN)	Escherichia coli (Engineered)	Enhanced via decoupling	Parallel pathway engineering	[80]

Experimental data from engineered strains further validates the potential of non-model hosts, demonstrating that they can achieve industrially relevant performance levels.

Table 2: Experimental Performance Metrics of Non-Model Microbial Cell Factories

Host Organism	Target Product	Performance Metrics	Cultivation Mode & Key Achievement	Reference
		Titer (g/L)	Yield (g/g)	Productivity (g/L/h)
Halomonas bluephagenesis TD01	Polyhydroxybutyrate (PHB)	64.74	Not specified	1.46	Fed-batch, seawater-based medium, unsterile conditions	[78]
Zymomonas mobilis (Engineered)	D-Lactate	140.92 (glucose), 104.6 (corncob residue)	> 0.97	Not specified	Fed-batch from lignocellulosic hydrolysate	[3]
Kluyveromyces marxianus (Engineered)	L-Lactic Acid	120	0.81	Not specified	Requires less neutralizing agent, ferments xylose	[79]
Escherichia coli (Growth-Coupled)	Anthranilate & Derivatives	> 2-fold increase	Not specified	Not specified	Pyruvate-driven growth-coupling strategy	[80]

Experimental Protocols for Reliable Benchmarking

To ensure fair and accurate comparisons of TYP metrics across different host organisms, standardized experimental protocols are critical.

Controlled Fermentation Conditions

Benchmarking experiments must be conducted under tightly controlled and comparable conditions:

• Bioreactor Operation: Use bench-scale bioreactors with precise control over temperature, pH, dissolved oxygen, and agitation. For initial screening, micro-bioreactors can be used for high-throughput data generation.
• Media Formulation: Employ chemically defined media to ensure reproducibility and avoid batch-to-batch variations associated with complex media. The carbon source (e.g., glucose, xylose, glycerol) and its initial concentration must be standardized.
• Cultivation Mode: The chosen mode (batch, fed-batch, or continuous) significantly impacts TYP. For benchmarking, fed-batch fermentation is often the most relevant as it mimics industrial production, allowing for high cell densities and product titers by controlling substrate feeding to avoid overflow metabolism [3] [79].
• Data Collection: Monitor cell growth (optical density or dry cell weight), substrate consumption, and product formation throughout the fermentation. Take frequent samples for robust kinetic analysis.

Analytical Techniques for Quantification

Accurate measurement is foundational to reliable benchmarking.

• Cell Density: Dry Cell Weight (DCW) is the gold standard for quantifying biomass.
• Substrates and Metabolites: Techniques like High-Performance Liquid Chromatography (HPLC) are standard for quantifying sugars, organic acids, and other metabolites in the culture broth.
• Product Identification and Quantification: Depending on the product, use HPLC, Gas Chromatography (GC), or LC-Mass Spectrometry (LC-MS). For polymers like PHB, specialized gravimetric or spectroscopic methods are used after extraction [78].

Metabolic Flux Analysis (MFA)

13C-MFA is a powerful technique for quantifying the in vivo intracellular flux distribution in central carbon metabolism. It involves feeding cells with a 13C-labeled substrate (e.g., [1-13C]-glucose) and measuring the isotopic labeling patterns in proteinogenic amino acids or metabolites via GC-MS. The computed flux map reveals how carbon is routed through the network, identifying flux bottlenecks and inefficiencies in engineered strains. This technique was used, for instance, to validate flux predictions in Z. mobilis under different aeration conditions [3].

Engineering Strategies to Enhance TYP Metrics

Once a chassis is selected, its performance can be radically improved through metabolic engineering. The strategies below address the core trade-off between cell growth and product synthesis.

Reconciling Growth and Production

A central challenge in metabolic engineering is that engineered pathways often compete with native metabolism for precursors and energy, impairing growth. Several strategies have been developed to resolve this conflict:

• Growth-Coupling: This approach links product synthesis to biomass formation, creating a selective advantage for high-producing cells. This can be achieved by designing synthetic metabolic routes that fulfill an essential cellular function, such as generating a key precursor like pyruvate, erythrose-4-phosphate, or acetyl-CoA [80]. For example, an E. coli strain engineered for anthranilate production had key pyruvate-producing genes deleted. Growth was restored only when a heterologous pathway produced anthranilate, which also regenerated pyruvate, thereby coupling production to growth [80].

• Decoupling Growth and Production: Conversely, orthogonal systems can be designed to separate the two phases. A common strategy is dynamic regulation, where genetic circuits trigger product synthesis in response to a specific cellular cue (e.g., depletion of a nutrient, entry into stationary phase). This allows for a dedicated growth phase followed by a production phase [80].

• Pathway Orthogonalization: This involves creating parallel metabolic pathways that do not interfere with native metabolism, for example, by using non-native carbon sources or engineered enzymes with different cofactor specificities [80].

The following diagram illustrates the core engineering concepts of growth-coupling and dynamic regulation.

Advanced Tool Development for Non-Model Chassis

The engineering of non-model organisms requires the development of specialized genetic toolkits.

• Genetic Parts: Identification and characterization of native constitutive and inducible promoters, ribosomal binding sites (RBS), and terminators are essential for fine-tuning gene expression. For example, the strong promoter kasO*p and its variants have been widely used in Streptomyces [82].
• Genome Editing Systems: The adaptation of CRISPR-Cas systems (e.g., Cas9, Cas12a) has revolutionized genome editing in non-model hosts, enabling efficient gene knockouts, integrations, and multiplexed engineering. These tools have been successfully implemented in Z. mobilis, Halomonas, and K. marxianus [3] [78] [79].
• Genome Reduction: Systematic deletion of non-essential genomic regions, including mobile genetic elements and endogenous antibiotic clusters, can improve genomic stability, increase transformation efficiency, and redirect metabolic flux toward product synthesis [1] [83].

Adaptive Laboratory Evolution (ALE)

ALE is a powerful complementary technique where microbial populations are cultured over many generations under selective pressure (e.g., high product concentration, inhibitor tolerance). This allows beneficial mutations to accumulate, leading to improved phenotypes. For instance, ALE of an engineered K. marxianus strain for LA production led to an 18% increase in titer. A causal mutation was identified in the general transcription factor gene SUA7, which improved biomass production under LA stress [79].

The Scientist's Toolkit: Essential Research Reagents and Solutions

The following table details key reagents and methodologies critical for conducting the benchmarking and engineering workflows described in this guide.

Table 3: Essential Research Reagent Solutions for Chassis Benchmarking and Engineering

Category	Item / Technique	Primary Function in R&D	Example Application in Non-Model Hosts
Genetic Toolbox	CRISPR-Cas Systems (e.g., Cas12a)	Precise genome editing (deletions, insertions)	Developed for Z. mobilis and Halomonas for gene knockout [3] [78].
	Constitutive & Inducible Promoters	Fine-tuned control of gene expression	kasOp variants in *Streptomyces; native inducible systems in actinomycetes [82].
	Plasmid Vectors & Shuttle Systems	Cloning and heterologous gene expression.	Vectors with host-specific replication origins and selection markers for Halomonas [78].
Analytical Tools	HPLC / GC-MS	Quantifying substrates, metabolites, and products.	Standard for measuring sugar consumption and organic acid production (e.g., lactate, succinate) [79].
	13C-Metabolic Flux Analysis (13C-MFA)	Quantifying intracellular metabolic flux distributions.	Used to validate model predictions and analyze central carbon metabolism in Z. mobilis [3].
Computational Resources	Genome-Scale Metabolic Models (GEMs)	In silico prediction of metabolic capabilities and yields.	iZM516/iZM547 for Z. mobilis; used for pathway design and host selection [3] [10].
	Flux Balance Analysis (FBA) Software (e.g., CobraPy)	Simulating and optimizing metabolic fluxes under constraints.	Calculating maximum theoretical and achievable yields (Y~T~ and Y~A~) for target chemicals [10].
Strain Improvement	Adaptive Laboratory Evolution (ALE)	Generating evolved strains with enhanced phenotypes (titer, tolerance).	Improved LA production and stress tolerance in K. marxianus [79].

The systematic benchmarking of titer, yield, and productivity is a cornerstone of developing efficient microbial cell factories. While model organisms provide a reliable starting point, the future of sustainable biomanufacturing lies in harnessing the unique capabilities of non-model microorganisms. As demonstrated by the performance of engineered Zymomonas, Halomonas, and Kluyveromyces, these chassis can achieve industrial-level TYP metrics while offering inherent advantages in process simplicity and feedstock flexibility.

The path forward requires an integrated approach: using computational models to guide host selection and pathway design, employing advanced genetic tools to engineer these often recalcitrant hosts, and applying rigorous experimental protocols to benchmark their performance fairly. By adopting the frameworks and strategies outlined in this technical guide, researchers can accelerate the development of next-generation biorefineries, paving the way for a more sustainable and circular bioeconomy.

In the development of non-model microbial cell factories, computational capacity analysis provides a critical framework for evaluating production potential. This technical guide delineates the core concepts of maximum theoretical yield (YT) and maximum achievable yield (YA), detailing the computational methodologies for their quantification. By integrating genome-scale metabolic models with host-aware frameworks, we present protocols for predicting strain performance and engineering strategies that reconcile the inherent trade-offs between growth and product synthesis, thereby facilitating the rational design of efficient bioproduction platforms.

The transition from model organisms to non-model microbial chassis represents a frontier in synthetic biology, unlocking access to novel metabolites and robust phenotypes for biomanufacturing [5]. Central to evaluating the potential of any microbial cell factory is a rigorous assessment of its metabolic capacity, quantified through two fundamental metrics: the maximum theoretical yield (YT) and the maximum achievable yield (YA). These metrics provide a quantitative basis for comparing the innate production capabilities of diverse microorganisms and for setting realistic engineering targets.

Maximum Theoretical Yield (YT) is defined as the maximum production of a target chemical per given carbon source when all cellular resources are hypothetically dedicated to its synthesis, ignoring any metabolic fluxes toward cell growth and maintenance [10]. It is determined solely by the stoichiometry of reactions in the host's metabolic network. In contrast, the Maximum Achievable Yield (YA) provides a more realistic measure by accounting for the metabolic resources diverted to cell growth and maintenance, including non-growth-associated maintenance energy (NGAM) [10]. Unlike chemical processes, bioprocesses require energy for the generation and maintenance of cells, which serve as biocatalysts, making YT an unattainable ideal in practice. The accurate discrimination between YT and Y_A is therefore critical for project design and economic forecasting in industrial applications.

For non-model organisms, which often lack the extensive genetic tools and characterized parts of their model counterparts, computational analysis offers a powerful strategy to prioritize engineering efforts. By calculating these yields in silico, researchers can identify the most promising host organisms for a target chemical before embarking on costly and time-consuming laboratory experiments [10] [84].

Core Computational Methodologies

The computational determination of YT and YA relies predominantly on the use of Genome-Scale Metabolic Models (GEMs). GEMs are mathematical representations of the metabolic network of an organism, encapsulating gene-protein-reaction associations that allow for the simulation of metabolic fluxes under defined conditions [10] [84].

Model Construction and Curation

The foundation of a reliable yield analysis is a high-quality, mass-and-charge-balanced GEM. For non-model organisms, this often begins with the draft reconstruction of a metabolic network from its annotated genome sequence. This process can be facilitated by tools that leverage existing databases like Rhea to ensure reaction balance [10]. For reactions not found in standard databases, manual curation is necessary. The final model should accurately represent the organism's native metabolic network, including its biosynthetic pathways for biomass constituents.

To analyze the production of a non-native chemical, the host's GEM must be expanded with heterologous metabolic reactions. Research involving five common industrial microorganisms revealed that for more than 80% of 235 target chemicals, fewer than five heterologous reactions were required to establish functional biosynthetic pathways [10]. This finding underscores the feasibility of minimally expanding metabolic networks to enable production in diverse chassis.

Simulation Techniques for Yield Calculation

Once a constrained GEM is established, constraint-based reconstruction and analysis (COBRA) methods are applied to calculate yields. The core simulation is Flux Balance Analysis (FBA), which computes the flow of metabolites through the network to maximize or minimize a defined objective function, typically the biomass reaction for simulating growth [10].

• Calculating Y_T: To compute the maximum theoretical yield, FBA is performed with the production flux of the target chemical set as the objective function. Critically, constraints that force biomass formation are relaxed or set to zero, simulating a scenario where all metabolic resources are devoted to product synthesis without any requirement for growth [10].
• Calculating Y_A: The maximum achievable yield provides a more industrially relevant metric. Its calculation incorporates constraints that enforce a minimum specific growth rate, typically set to 10% of the maximum biomass production rate to ensure viability, and includes a non-growth-associated maintenance (NGAM) energy requirement [10]. This simulation reflects the necessary competition for resources between growth and production.

Table 1: Key Parameters for Yield Calculation via GEMs

Parameter	Role in Y_T Calculation	Role in Y_A Calculation	Typical Value/Source
Objective Function	Maximize product exchange flux	Maximize product exchange flux	N/A
Growth Constraint	Unconstrained or set to zero	Lower bound set to enforce minimum growth	e.g., ≥10% of max growth rate [10]
NGAM	Often ignored	Included as a constraint	Experimentally determined ATP requirement
Carbon Source Uptake	Constrained to a fixed value	Constrained to a fixed value	e.g., 10 mmol/gDW/h for glucose

A Protocol for Yield Analysis in Non-Model Organisms

The following section provides a detailed, step-by-step experimental protocol for conducting a computational capacity analysis, adaptable for both model and non-model organisms.

Phase 1: Establishing the Computational Framework

• Genome Acquisition and Annotation: Obtain a high-quality genome sequence for the target non-model organism. Utilize annotation pipelines (e.g., RAST, Prokka) to identify protein-coding genes and assign initial functional annotations.
• Draft GEM Reconstruction: Use automated reconstruction software (e.g., ModelSEED, CarveMe) with the annotated genome to generate a draft metabolic model. For non-model organisms, this draft will require extensive manual curation.
• Model Curation and Validation:
  • Ensure all reactions are mass and charge balanced.
  • Verify the model can produce all essential biomass precursors (amino acids, nucleotides, lipids, cofactors).
  • Validate the model by comparing in silico predicted growth capabilities on different carbon sources with experimental data, if available. Adjust the model to correct for discrepancies.
• Incorporation of Heterologous Pathways:
  • Identify a heterologous biosynthetic pathway for the target chemical from literature or metabolic databases.
  • Add the necessary enzymatic reactions and transport processes to the curated GEM. Ensure all cofactor requirements (e.g., NADH, NADPH, ATP) are correctly specified.
  • Confirm the metabolic network can produce the target chemical from the desired carbon source by performing a pathway check.

Phase 2: Simulation and Data Analysis

• Defining Simulation Conditions: Set the constraints for the simulation, including the carbon source uptake rate, oxygen availability (aerobic, microaerobic, anaerobic), and any other relevant nutrients.
• Y_T Simulation: Set the lower bound for the biomass reaction to zero. Set the objective function to maximize the flux of the target chemical's exchange reaction. Run FBA and record the product flux normalized to the substrate uptake flux (mol product / mol substrate).
• YA Simulation:
  • Determine the organism's maximum growth rate (μmax) by setting the biomass reaction as the objective and running FBA.
  • Re-constrain the model with the biomass reaction lower bound set to a fraction of μmax (e.g., 0.1 * μmax) to enforce minimum growth.
  • Ensure the NGAM constraint is active.
  • Set the objective function to maximize the target chemical production flux. Run FBA and record the normalized product flux.
• Comparative Analysis: Calculate YT and YA for the target chemical across different host strains and conditions. Rank the organisms based on their Y_A, as this metric is more predictive of industrial performance [10].

The following diagram illustrates the logical workflow and key decision points in this protocol.

Interpreting Results and Engineering Implications

The calculated YT and YA values are not merely descriptive; they provide a blueprint for metabolic engineering. The gap between YT and YA for a given strain underscores the metabolic cost of growth and maintenance, while differences in Y_A across strains highlight host-specific metabolic capacities [10].

The Growth-Production Trade-off

A fundamental challenge in metabolic engineering is the inherent trade-off between cell growth and product synthesis [80] [85]. Engineered microbial cell factories often face competition for shared precursors, energy (ATP), and reducing equivalents (NADPH) between the endogenous metabolic network supporting growth and the introduced heterologous pathway. This competition can result in diminished cellular fitness and suboptimal production [80]. Computational analyses reveal that strains selected for very high growth rates may consume most of the substrate for biomass, yielding low volumetric productivity. Conversely, strains with excessively low growth but high synthesis rates also achieve low productivity because a smaller population takes longer to accumulate product [85]. Therefore, an optimal sacrifice in growth rate is often required to maximize overall culture performance.

Strategies for Yield Enhancement

Computational results directly inform several metabolic engineering strategies to enhance yields:

• Host Strain Selection: A comprehensive in silico evaluation of five industrial microorganisms (E. coli, S. cerevisiae, B. subtilis, C. glutamicum, P. putida) for 235 bio-based chemicals demonstrated that the most suitable host is highly chemical-dependent. For instance, while S. cerevisiae showed the highest Y_T for l-lysine, other chemicals showed clear host-specific superiority [10]. This underscores the value of systematic comparison before experimental work.
• Dynamic Metabolic Engineering: To overcome the growth-production trade-off, engineered genetic circuits can be designed to implement a two-stage production process. Cells are allowed to first grow maximally, after which a genetic switch is triggered to activate a high-synthesis, low-growth state [80] [85]. This strategy can achieve higher culture-level performance than one-stage processes.
• Cofactor Engineering and Pathway Orthogonalization: Systematic analysis of heterologous reactions and cofactor exchanges can identify opportunities to rewire innate metabolism, potentially increasing yields beyond the host's native capacity [10] [84]. Creating orthogonal metabolic systems that decouple production from host regulation is another advanced strategy to mitigate resource competition [80].

Table 2: Computational Strategies to Bridge the Gap Between Y_T and Y_A

Strategy	Computational Approach	Expected Outcome	Example
Host Selection	Compare Y_A across multiple GEMs for the same target chemical.	Identify chassis with innate metabolic architecture favoring high yield.	Selecting S. cerevisiae over E. coli for l-lysine production due to higher Y_T [10].
Gene Knockout Identification	In silico knockout simulations (e.g., OptKnock) to couple growth to production.	Impose selective pressure for production, enhancing strain stability and yield.	Predicting gene knockouts in E. coli for improved l-valine production [10].
Dynamic Pathway Regulation	"Host-aware" modeling to design circuits that inhibit host metabolism post-growth.	Temporally separate growth and production phases to maximize both biomass and product titer.	Circuits that inhibit host metabolism to redirect flux to product synthesis [85].

The Scientist's Toolkit: Essential Research Reagents and Platforms

The following table details key reagents, software, and databases essential for conducting the computational analyses described in this guide.

Table 3: Key Research Reagents and Computational Tools

Item / Resource	Category	Function / Application	Relevance to Non-Model Organisms
Genome Sequence	Primary Data	Foundation for draft GEM reconstruction.	High-quality, contiguous assembly is critical for accurate model building.
Rhea Database	Database	Provides mass-and-charge-balanced biochemical reactions.	Essential for curating reactions and ensuring thermodynamic consistency [10].
COBRA Toolbox	Software	MATLAB-based suite for constraint-based modeling.	Standard platform for performing FBA and other GEM simulations.
ModelSEED / CarveMe	Software	Platforms for automated GEM reconstruction from genome annotations.	Accelerates the initial model-building process for novel organisms.
Design-Expert Software	Software	Statistical tool for experimental design and optimization.	Used for optimizing fermentation conditions post-computational analysis (e.g., medium composition) [86].
SLiM / Nemo	Software	Forward-in-time population genetics simulators.	Models genetic load and evolutionary trajectories in engineered populations [87].
CRISPR Tools	Molecular Tool	Enables precise gene editing and functional genomics screens.	Key for validating predictions and engineering non-model chassis, including breaking recalcitrance [5].

Computational capacity analysis, centered on the discrimination between maximum theoretical and achievable yields, is an indispensable component of the modern metabolic engineering workflow. For the burgeoning field of non-model organism utilization, GEM-driven predictions offer a rational path to de-risk projects and accelerate the development of high-performing cell factories. By providing a systems-level understanding of metabolic trade-offs and enabling the in silico testing of engineering strategies, these approaches allow researchers to move beyond trial-and-error and strategically design microbes for efficient, sustainable, and economic biomanufacturing in the bioeconomy era.

Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA) for Sustainability

The transition from traditional, fossil-based production to a sustainable bioeconomy hinges on the development of efficient and economically viable microbial cell factories. For non-model organisms—those less characterized than workhorses like E. coli or S. cerevisiae—this presents unique challenges and opportunities. These organisms often possess innate abilities to utilize complex substrates or produce valuable compounds but lack well-established genetic tools. Within this research context, Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA) emerge as critical methodologies for guiding research and development (R&D) toward commercially viable and environmentally sustainable processes [88]. TEA is a modeling tool that evaluates the economic feasibility of a production process at scale, identifying cost drivers and setting targets for R&D [89] [90]. LCA, in contrast, is a systematic framework for quantifying the environmental impacts of a product or process throughout its entire life cycle, from raw material extraction to end-of-life disposal [91] [92].

Performing these analyses during the early stages of bioprocess development, even at the laboratory scale, is paramount. It allows researchers to identify economic and environmental "hotspots" before processes are locked in, enabling strategic optimization of both the organism and the process for sustainability and cost [92] [93]. This guide provides a technical roadmap for integrating TEA and LCA into the research lifecycle for non-model organism bioprocesses, complete with methodologies, protocols, and data presentation frameworks.

Methodological Foundations of TEA and LCA

Techno-Economic Analysis (TEA) for Bioprocesses

TEA is a systematic approach that combines process modeling, engineering design, and financial analysis to estimate the economic performance of an industrial-scale facility [90]. For early-stage research on non-model organisms, its primary value lies in quantifying unit economics, identifying cost hotspots, and setting clear, quantifiable targets for strain and process engineering [89].

The following diagram outlines the core workflow for conducting a TEA, illustrating the iterative relationship between process modeling and financial analysis.

Figure 1: TEA Workflow for Bioprocess Development

Key Steps in an Early-Stage TEA:

• Establish Fact Base and Scope: Define the target product, production scale (e.g., tonnes per year), and the geographical location for the hypothetical facility. Identify the target organism, key fermentation metrics (titer, rate, yield), and the primary carbon feedstock [89].
• Process Flow Diagram (PFD): Develop a detailed PFD that outlines every unit operation, from feedstock preparation and fermentation to downstream purification and waste treatment. This is the conceptual backbone of the model [89].
• Mass and Energy Balance: Quantify the inputs and outputs for every unit operation in the PFD. This involves calculating the flow rates of all components (substrates, nutrients, biomass, product) and the associated energy requirements (electricity, steam, cooling) based on stoichiometry and engineering principles [89].
• Equipment Sizing and Costing: Based on the mass and energy balances, size all major equipment (bioreactors, centrifuges, chromatography systems, etc.). Capital costs (CAPEX) are then estimated using scaling laws (e.g., Costâ‚‚ = Cost₁ × (Sizeâ‚‚/Size₁)^0.6) and vendor quotes [89].
• Cost of Goods Sold (COGS) Calculation: COGS is the sum of all costs to produce the product. It includes:
  • Direct Manufacturing Costs: Raw materials, utilities, labor.
  • Fixed Manufacturing Costs: Maintenance, overhead.
  • Capital-Depreciated Costs: Annualized cost of the equipment and facility [89] [90].
• Financial Analysis and Minimum Selling Price (MSP): A discounted cash flow analysis is performed over the project's lifetime. The MSP is the product price at which the net present value (NPV) of the project becomes zero, representing the break-even price [94] [93] [89].
• Sensitivity Analysis: This tests how sensitive the MSP is to changes in key parameters (e.g., feedstock cost, fermentation titer, energy consumption). It identifies the most influential variables and prioritizes R&D efforts [89].

Life Cycle Assessment (LCA) for Bioprocesses

LCA is a standardized methodology (ISO 14040/14044) for evaluating the environmental impacts of a product system from "cradle to grave." For early-stage bioprocess development, a "cradle-to-gate" approach is common, encompassing impacts from raw material extraction up to the production of the final product at the factory gate [92].

The LCA framework, as shown below, is an iterative process that assesses multiple environmental impact categories.

Figure 2: The Four Phases of Life Cycle Assessment (LCA)

Key Steps in an Early-Stage LCA:

• Goal and Scope Definition: Define the purpose of the study, the functional unit (e.g., 1 kg of purified protein), and the system boundaries. For non-model organisms using waste feedstocks, defining system boundaries is critical to avoid confusion about allocated impacts [91].
• Life Cycle Inventory (LCI): This is the data collection phase. It involves creating a detailed inventory of all material and energy inputs (e.g., substrates, electricity, chemicals) and emissions/outputs (e.g., COâ‚‚, wastewater) associated with the process defined in the scope [92].
• Life Cycle Impact Assessment (LCIA): The inventory data is translated into potential environmental impacts using established characterization methods. Common categories for bioprocesses include:
  • Global Warming Potential (GWP): kg COâ‚‚-equivalent [93] [95].
  • Freshwater Eutrophication Potential (FEUP): kg P-equivalent [95].
  • Terrestrial Acidification Potential (TAP): kg SOâ‚‚-equivalent [95].
  • Water Consumption: m³ [95].
• Interpretation: Analyze the results to identify environmental hotspots—process steps or inputs that contribute most significantly to the overall impact. This directly informs process optimization for sustainability [92].

Integrated TEA/LCA Protocol for Early-Stage Research

This protocol provides a step-by-step guide for conducting a combined TEA/LCA during the early technology development phase (Technology Readiness Level 3-5).

Experimental Design and Data Requirements

The foundation of a reliable TEA/LCA is high-quality, scalable data. The table below outlines the core data requirements and their sources for an integrated analysis.

Table 1: Core Data Requirements for Early-Stage TEA and LCA

Data Category	Specific Parameters	Data Source	TEA Relevance	LCA Relevance
Fermentation Performance	Titer (g/L), Productivity (g/L/h), Yield (g-product/g-substrate)	Lab-scale bioreactor experiments	Sizes bioreactor volume, impacts CAPEX/OPEX	Drives substrate and energy inputs per functional unit
Downstream Processing	Recovery efficiency (%) for each step (e.g., centrifugation, filtration, chromatography), Chemical/water consumption	Bench-scale purification experiments	Determines equipment sizing and consumables cost	Contributes to chemical use, waste streams, and energy load
Raw Materials	Type and quantity of carbon source (e.g., glucose, glycerol), nutrients, induction chemicals	Experimental protocol	Major OPEX driver (up to 50-60% of costs) [96]	Major contributor to GWP, eutrophication, and land use [91] [92]
Utilities	Electricity (kWh), Steam (kg), Cooling Water (m³) per kg product	Estimated from mass/energy balance and equipment models	Significant OPEX component	Often the dominant impact category, especially if grid electricity is used [91] [95]

Step-by-Step Computational Workflow

• Develop a Base Case Process Model: Using data from Table 1, build a process model in a spreadsheet or specialized software (e.g., SuperPro Designer). The model must reflect the PFD and incorporate mass/energy balances.
• Scale-Up the Process: Scale the lab data to a commercially relevant production volume (e.g., 10-100 tonnes/year). Use engineering correlations for bioreactor scale-up (e.g., constant oxygen transfer rate). A common scale for analysis is 10 MT/day [94].
• Calculate Economic and Environmental Indicators:
  • TEA: Compute the CAPEX, OPEX, and subsequently the MSP.
  • LCA: Using the scaled inventory data, calculate the impact for each category (e.g., GWP).
• Identify and Validate Hotspots: Analyze the model outputs to find the unit operations or inputs that are the largest contributors to cost (MSP) and environmental impact (e.g., GWP). For non-model organisms, fermentation (due to low titer/yield) and downstream processing (due to complexity) are common hotspots [92] [93].
• Perform Scenario and Sensitivity Analysis: Test how improvements in key parameters affect the results. For example:
  • What if the fermentation titer is improved from 0.7 mg/L to 1 g/L? One study showed this could reduce MSP from ~$3.9 million/kg to ~$4,260/kg and GWP from 2,540 kg COâ‚‚eq/g to 6.36 kg COâ‚‚eq/g [93].
  • What is the effect of using renewable electricity? Switching to wind energy can reduce environmental impacts by 34-81% [95].
  • How does in-situ product extraction affect costs? It can lower MSP by 41-61% and GWP by 30-75% [93].

The Scientist's Toolkit: Research Reagents and Solutions

When working with non-model organisms, specific reagents and tools are essential for gathering the high-quality data needed for robust TEA/LCA models.

Table 2: Key Research Reagent Solutions for Bioprocess Development

Item/Category	Function in R&D	Relevance to TEA/LCA
Specialized Carbon Substrates	Testing growth and product formation on non-conventional feedstocks (e.g., C1 gases, food waste hydrolysates) [96] [97].	Defines the raw material cost (OPEX) and environmental burden of feedstock production. Waste streams can reduce both cost and impact [96].
Strain Engineering Tools	CRISPR/Cas systems, promoters, and vectors adapted for non-model hosts to improve titer, rate, and yield (TRY).	Directly impacts the fermentation performance metrics that are primary drivers of both CAPEX and OPEX in the TEA model.
Analytical Standards	High-purity standards for the target product and key metabolites for HPLC/MS quantification.	Essential for accurately measuring titer and yield in lab experiments, which are foundational input parameters for the models.
Cell Disruption Reagents	Enzymes (lysozyme) or chemicals for efficient lysis of robust microbial cells (e.g., microalgae) [97].	Affects the efficiency and cost of the downstream processing. Inefficient lysis increases energy and chemical use, raising cost and environmental impact.
High-Throughput Screening Kits	Assays for rapid quantification of product formation and metabolic activity in microtiter plates.	Enables faster strain optimization, generating the performance data needed to set realistic TEA/LCA targets for the final engineered strain.

Case Studies and Data Synthesis

Synthesizing quantitative data from published case studies provides critical benchmarks for researchers. The following tables summarize key findings from TEA and LCA studies across various bioprocesses.

Table 3: Techno-Economic Performance of Various Bioprocesses

Product	Organism/Pathway	Key Assumptions	Minimum Selling Price (MSP)	Primary Cost Drivers
Tabersonine	Engineered S. cerevisiae	Base case: 0.7 mg/L titer	$3,910,000/kg	Low fermentation titer, downstream processing (chromatography) [93]
Tabersonine	Engineered S. cerevisiae	Optimized: 1 g/L titer	$4,262/kg	Downstream processing (chromatography becomes dominant at higher titers) [93]
CNC/PDMS Hybrid Membrane	Chemical Process	10 MT/day production scale	$3.68/m²	Capital cost ($136M) and operating cost ($139M/year) [94]
3-HP from C1 feedstocks	Engineered microbes (proposed)	Two-stage bio-cascade or electro-bio-cascade	Higher than fossil-based alternatives	Low carbon yield (<10%), cost of C1 feedstocks (>57% of OPEX), fermentation equipment [96]

Table 4: Life Cycle Assessment Results for Various Bioprocesses

Product / System	Functional Unit	Global Warming Potential (GWP)	Other Impact Highlights	Major Environmental Hotspots
Mannosylerythritol Lipids (MELs)	1 kg of biosurfactant	Not Specified	Acidification, Eutrophication	Substrate provision (20% of Climate Change, >70% of Acidification/Eutrophication), bioreactor aeration (33% of Climate Change), solvent use in purification (42% of Climate Change) [92]
Plant Cell Cultures (PCC)	1 kg of fresh biomass	Close to heated greenhouse crops	FEUP, TAP	Electricity consumption (82-93% of GWP, FEUP, TAP). Using wind energy reduced impacts by 34-81% [95]
Single Cell Protein (SCP)	1 kg of protein	Varies by substrate	Acidification, Eutrophication	Electricity (main hotspot in most systems), substrate type and pre-treatment [91]
Microalgal Protein	1 kg of protein	Highly energy-intensive	Land Use, Water Use	Energy inputs for cultivation (mixing, pumping) and dewatering. Biorefinery approaches can mitigate impacts [97]

Critical Considerations for Non-Model Organisms

Applying TEA and LCA to non-model organism research requires addressing specific challenges:

• Data Uncertainty: Inherent biological variability and unoptimized processes in non-model hosts lead to high uncertainty in performance data. This must be explicitly addressed through rigorous sensitivity and scenario analysis [89]. The margin of error for an early-stage TEA can be ±50% [89].
• Defining System Boundaries in LCA: When using waste-derived feedstocks (a key advantage for some non-model organisms), the LCA should clearly state how upstream impacts are allocated. A lack of consensus on this can lead to confusion about the actual environmental impacts [91].
• The "Nth Plant" Assumption vs. Reality: Many TEAs use an "nth plant" concept, assuming a mature, optimized industry. For pioneering processes with non-model organisms, a "first-of-a-kind" or "pioneer plant" analysis is more appropriate, as it better reflects the higher costs and risks of early scale-up [88].
• Beyond GWP: While GWP is a critical metric, a holistic sustainability assessment for non-model organisms should include other impact categories like water consumption, land use, and eutrophication, where bio-based processes may have significant advantages or trade-offs [95].

The transition from a fossil-fuel-based economy to a sustainable, bio-based circular economy is a critical global imperative, requiring the development of efficient microbial cell factories for chemical production [1]. While model microorganisms like Escherichia coli and Saccharomyces cerevisiae have been traditional workhorses, non-model organisms often possess superior innate traits for industrial bioprocessing, including robustness, diverse substrate utilization, and unique metabolic capabilities [3] [1]. This case study examines the development of microbial cell factories for producing D-lactate—a key precursor for bioplastics—and amino acids, focusing on the engineering of non-model chassis. We explore the metabolic engineering strategies, experimental methodologies, and performance outcomes across diverse microbial platforms, providing a technical framework for harnessing non-model organisms in industrial biotechnology.

Metabolic Engineering Strategies for Non-Model Chassis Development

Overcoming Dominant Native Metabolism

A primary challenge in engineering non-model chassis is redirecting carbon flux away from dominant native pathways. Zymomonas mobilis, a gram-negative bacterium with exceptional industrial characteristics including high sugar uptake and ethanol yield, exemplifies this challenge. Its innate dominance of the ethanol production pathway via efficient pyruvate decarboxylase (PDC) and alcohol dehydrogenases (ADHs) restricts the synthesis of other valuable biochemicals [3].

To address this, a Dominant-Metabolism Compromised Intermediate-Chassis (DMCI) strategy was developed. Instead of directly engineering the chassis for target biochemicals, researchers first introduced a low-toxicity but cofactor-imbalanced 2,3-butanediol (2,3-BDO) pathway. This strategic intermediate step effectively weakened the native ethanol pathway, creating a platform chassis amenable to further engineering. Subsequently, a recombinant D-lactate producer was constructed, achieving remarkable yields exceeding 140.92 g/L from glucose and 104.6 g/L from corncob residue hydrolysate (yield > 0.97 g/g glucose) [3]. This DMCI approach demonstrates a paradigm for engineering recalcitrant microorganisms as biorefinery chassis.

Genome Reduction for Chassis Streamlining

Genome reduction represents a valuable top-down approach for developing robust microbial chassis. By systematically removing "unnecessary" genes and genomic regions, cellular complexity is reduced, leading to improved predictability, controllability, and industrial performance. The benefits of this approach include enhanced genomic stability (through deletion of mobile genetic elements), improved transformation efficiency, optimization of downstream applications, and potentially higher growth rates and productivity [1].

In E. coli, development of an insertion sequence (IS)-free strain enhanced production of recombinant proteins by 20-25% [1]. For Streptomyces albus, deletion of 15 native antibiotic gene clusters resulted in approximately 2-fold higher production of heterologously expressed biosynthetic gene clusters [1]. These examples demonstrate how reducing metabolic background noise and eliminating competitive pathways can significantly improve production capabilities in microbial chassis.

Cofactor Engineering for Pathway Optimization

Cyanobacteria present a unique challenge for metabolic engineering due to their distinct cofactor balance, which favors NADPH over NADH—the opposite preference of most bacterial enzymes. This cofactor imbalance can limit the efficiency of heterologous pathways in cyanobacterial systems [98] [99].

Several innovative strategies have been employed to address this limitation:

• Enzyme Engineering: The cofactor preference of D-lactate dehydrogenase (LdhD) from Lactobacillus bulgaricus was successfully reversed from NADH to NADPH through rational protein engineering. A quadruple mutant (D176A/I177R/F178S/N180R) exhibited a fundamental shift in cofactor preference, with kcat/KmNADPH becoming approximately 5.2-fold higher than kcat/KmNADH [99].

• Heterologous Cofactor Systems: Introduction of a soluble transhydrogenase (sth from Pseudomonas aeruginosa) in Synechocystis sp. PCC 6803 helped balance cofactor availability, resulting in improved D-lactate productivity [98].

• Combinatorial Approaches: In Synechococcus elongatus PCC7942, combining codon-optimized NADPH-dependent LdhD with a lactate transporter (LldP) and CO2 enrichment enhanced D-lactate production to 798 mg/L, demonstrating the power of integrated strategies [99].

Case Study: D-Lactate Production in Diverse Chassis

Production Metrics Across Microbial Platforms

Table 1: Comparative D-Lactate Production in Engineered Microbial Chassis

Chassis Organism	Carbon Source	Titer (g/L)	Yield (g/g)	Productivity (g/L/h)	Key Engineering Strategy	Citation
Zymomonas mobilis	Glucose	140.92	0.97	N/A	DMCI strategy to bypass dominant ethanol pathway	[3]
Zymomonas mobilis	Corncob residue hydrolysate	104.6	>0.97	N/A	DMCI strategy using lignocellulosic feedstocks	[3]
Saccharomyces cerevisiae	Glucose	61.5	0.612	N/A	PDC1 deletion + integration of two d-LDH copies	[100]
Synechocystis sp. PCC 6803	CO2 (Photoautotrophic)	1.14	N/A	~0.0042	Codon-optimized d-LDH + soluble transhydrogenase	[98]
Synechocystis sp. PCC 6803	CO2 + Acetate	2.17	N/A	~0.009	Mixotrophic cultivation with acetate supplementation	[98]
Lactococcus lactis NZ9000	Starch	15.0	N/A	0.625	Replacement of L-Ldh with heterologous D-Ldh + α-amylase expression	[101]
Methylomonas sp. DH-1	Methane	6.17	N/A	0.057	Inducible promoter regulation + glgC deletion to prevent ADP-glucose accumulation	[102]
Synechococcus elongatus PCC7942	CO2	0.798	N/A	N/A	Engineered NADPH-dependent LdhD + lactate transporter	[99]
Komagataella phaffii	Methanol	5.38	N/A	N/A	UV mutagenesis of engineered D-lactate producing strain	[103]

Experimental Protocols for D-Lactate Production

EngineeringZymomonas mobilisfor High-Titer D-Lactate

Strain Development Protocol:

• DMCI Intermediate Construction: Introduce the 2,3-butanediol pathway genes into Z. mobilis to create cofactor imbalance and weaken the dominant ethanol pathway.
• D-Lactate Pathway Integration: Introduce D-lactate dehydrogenase gene with optimized expression signals.
• Model-Guided Optimization: Utilize the enzyme-constrained genome-scale metabolic model eciZM547 to simulate flux distributions and identify pathway bottlenecks.
• Fermentation Process: Cultivate engineered strain in bioreactor with glucose or corncob residue hydrolysate as carbon source. Monitor D-lactate production via HPLC.

Analytical Methods: Quantify D-lactate concentration and purity using HPLC with appropriate standards. Determine optical purity by chiral column chromatography [3].

Photoautotrophic D-Lactate Production in Cyanobacteria

Strain Construction Protocol:

• Gene Integration: Amplify D-lactate dehydrogenase gene (ldhD) from Leuconostoc mesenteroides or engineered variant with switched cofactor preference.
• Codon Optimization: Optimize gene sequence for cyanobacterial expression and synthesize.
• Transformation: Introduce expression cassette into neutral site of Synechocystis sp. PCC 6803 or Synechococcus elongatus PCC7942 chromosome via natural transformation or conjugation.
• Cofactor Engineering: Co-express soluble transhydrogenase (sth) from Pseudomonas aeruginosa to balance NADH/NADPH ratio.

Cultivation Conditions: Grow engineered strains in BG-11 medium under continuous illumination. For mixotrophic growth, supplement with 10-20 mM acetate. Maintain cultures with CO2-enriched air (1-5% CO2) at 30°C with constant shaking [98] [99].

Analytical Methods: Measure D-lactate concentration in culture supernatant using HPLC or enzymatic assay kits. Confirm optical purity (>99.9%) by chiral HPLC [98].

Case Study: Amino Acid Production in Engineered Chassis

Host Selection and Metabolic Capacity Analysis

Genome-scale metabolic models (GEMs) provide powerful tools for evaluating the metabolic capacities of different host strains for amino acid production. Computational analysis of five representative industrial microorganisms (Bacillus subtilis, Corynebacterium glutamicum, Escherichia coli, Pseudomonas putida, and Saccharomyces cerevisiae) reveals significant variations in their theoretical production capabilities [10].

For L-lysine production under aerobic conditions with D-glucose, S. cerevisiae shows the highest maximum theoretical yield (YT) of 0.8571 mol/mol glucose, despite utilizing the distinct L-2-aminoadipate pathway compared to the diaminopimelate pathway used by bacterial strains [10]. However, in industrial practice, C. glutamicum remains the dominant production host due to its exceptional in vivo metabolic fluxes and high product tolerance, demonstrating that theoretical capacity must be balanced with practical implementation factors [10].

Table 2: Amino Acid Production in Microbial Chassis

Amino Acid	Preferred Chassis	Key Engineering Strategies	Notable Production Metrics	Citation
L-Lysine	Corynebacterium glutamicum	Enhancement of diaminopimelate pathway, transporter engineering	Industrial-scale production achieved	[10]
L-Glutamate	Corynebacterium glutamicum	Membrane engineering, trigger manipulation for export	Major industrial producer (>2 million tons/year)	[104]
γ-Aminobutyric Acid (GABA)	Levilactobacillus brevis	Optimization of glutamate decarboxylase, anaerobic/aerobic condition screening	Production by free and immobilized cells	[104]

Engineering Microbial Membranes for Amino Acid Export

Membrane engineering represents a crucial strategy for enhancing amino acid production, particularly for industrial-scale processes. In Corynebacterium glutamicum, mechanosensitive channels of the MscCG type play a major role in glutamate efflux. Patch-clamp experiments on proteoliposomes revealed that the mechanosensitivity and activation threshold of these channels depend significantly on membrane lipid composition, particularly the presence of anionic lipids like phosphatidylglycerol [104].

Membranes containing anionic phosphatidylglycerol were demonstrated to be "softer" than membranes containing only non-anionic lipids, affecting the force-from-lipids dependence of mechanosensitive channel gating. This understanding of membrane properties enables more rational engineering of export systems in microbial chassis for improved amino acid production [104].

The Scientist's Toolkit: Essential Research Reagents and Methods

Table 3: Key Research Reagent Solutions for Microbial Cell Factory Development

Reagent/Method	Function/Application	Example Use Case	Citation
CRISPR-Cas Systems	Precision genome editing	Endogenous Type I-F CRISPR-Cas used in Zymomonas mobilis for genome engineering	[3]
Genome-Scale Metabolic Models (GEMs)	In silico prediction of metabolic fluxes	eciZM547 model with enzyme constraints for Zymomonas mobilis pathway design	[3]
Enzyme-Constrained Metabolic Models (ecModels)	Enhanced prediction of proteome-limited growth	eciZM547AutoPACMENmean for Zymomonas mobilis	[3]
Cofactor Engineering Tools	Switching enzyme cofactor specificity	Site-directed mutagenesis of LdhD for NADPH preference in cyanobacteria	[99]
UV Mutagenesis	Random mutagenesis for strain improvement	Enhancement of D-lactate production in Komagataella phaffii	[103]
Adaptive Laboratory Evolution (ALE)	Improving product tolerance	Evolution of Methylomonas sp. DH-1 for lactate tolerance (8.0 g/L)	[102]
HPLC with Chiral Columns	Analysis of optical purity	Determination of D-lactate enantiomeric purity (>99.9%)	[100]
13C-Metabolic Flux Analysis (13C-MFA)	Experimental determination of metabolic fluxes	Validation of flux predictions in Zymomonas mobilis under different conditions	[3]

This comparative case study demonstrates that non-model microorganisms offer compelling advantages as microbial cell factories for D-lactate and amino acid production. The exceptional performance of engineered Zymomonas mobilis for D-lactate production highlights how leveraging innate metabolic capabilities can yield superior industrial strains. The development of specialized strategies like the DMCI approach for bypassing dominant metabolism provides a blueprint for engineering recalcitrant microorganisms.

Future advancements in non-model chassis development will likely focus on several key areas: First, the continued refinement of genome-scale modeling with enzyme constraints will enhance our ability to predict metabolic bottlenecks. Second, the integration of machine learning and automation in strain engineering pipelines will accelerate the design-build-test-learn cycle. Third, expanding the portfolio of well-characterized non-model hosts will provide more specialized options for different feedstock and product combinations. Finally, strategies for enhancing product tolerance, such as the adaptive evolution employed for methanotrophs, will be crucial for reaching commercial production targets.

As synthetic biology tools become more sophisticated and our understanding of microbial physiology deepens, non-model organisms are poised to become the cornerstone of the emerging bioeconomy, enabling sustainable production of chemicals, materials, and fuels from renewable resources.

Navigating Regulatory and Scale-Up Challenges for Industrial Adoption

The transition from fossil-based production to a sustainable bioeconomy is a global imperative, driving intensive research into microbial cell factories (MCFs). While model organisms like Escherichia coli and Saccharomyces cerevisiae have historically dominated industrial biotechnology, non-model microorganisms represent an untapped reservoir of metabolic diversity with superior potential for producing valuable chemicals, materials, and pharmaceuticals [1] [3]. These organisms often possess innate advantages such as robust stress tolerance, versatile substrate utilization, and unique metabolic capabilities that are difficult to engineer into traditional hosts [3] [105]. However, their development into reliable industrial platforms faces significant regulatory and scale-up challenges that must be systematically addressed [1] [106].

Non-model microbes constitute approximately 99% of microbial biodiversity, offering immense potential for biomanufacturing diverse natural products [7]. Organisms such as Zymomonas mobilis, various Streptomyces species, and numerous non-conventional yeasts exhibit industrial characteristics including resistance to harsh process conditions, innate immunity from phage infection, high yield with minimal by-products, and ability to utilize diverse feedstocks [1] [3]. Despite these advantages, the very characteristics that make non-model organisms appealing also create unique hurdles in their pathway toward commercialization. This technical guide examines these challenges through the lens of regulatory compliance and scale-up implementation, providing researchers with strategic frameworks for successful industrial adoption.

Regulatory Considerations for Non-Model Organisms

Regulatory Landscape and Classification

The regulatory pathway for non-model organisms begins with proper classification based on intended application and inherent risk profile. Microorganisms used in industrial bioprocesses typically fall under one of three regulatory categories based on product type and application:

• Biopharmaceuticals: Stringent Good Manufacturing Practice (GMP) compliance, requiring extensive documentation of genetic stability, product consistency, and absence of harmful by-products [106]
• Food/Feed Additives: Generally Recognized As Safe (GRAS) designation requirements, focusing on absence of toxin production and pathogenicity [7]
• Industrial Biochemicals: Environmental Protection Agency (EPA) or similar national agency oversight, emphasizing containment and environmental impact assessment [106]

The fundamental regulatory requirement for all categories involves comprehensive characterization to demonstrate the absence of pathogenic elements and genetic instability [1]. This necessitates the identification and removal of mobile genetic elements, pathogenicity islands, and antibiotic resistance genes that may raise regulatory concerns [1]. For example, in the development of Streptomyces albus as a production chassis, researchers successfully deleted 15 native antibiotic gene clusters, simultaneously improving safety profile and production efficiency [1].

Genetic Stability Assessment Protocols

Establishing genetic stability is a cornerstone regulatory requirement, particularly for engineered non-model organisms. The following experimental protocol provides a standardized approach for generating the necessary stability data for regulatory submissions:

Objective: To demonstrate genetic and phenotypic stability of engineered non-model microbial strains over multiple generations under simulated production conditions.

Materials:

• Cryopreservation medium (e.g., 15% glycerol in appropriate growth medium)
• Selective plates with and without antibiotic pressure
• PCR reagents and primers for target gene amplification
• Fermentation equipment (bench-scale bioreactors)
• Metabolic activity assays (e.g., HPLC for product quantification)

Methodology:

• Inoculate the engineered strain in appropriate liquid medium and passage daily by transferring 1% inoculum to fresh medium for 30 consecutive days (approximately 300 generations)
• At every 10-generation interval:
  • Withdraw samples for cryopreservation at -80°C
  • Plate appropriate dilutions on non-selective and selective media to assess plasmid loss or genetic drift
  • Isolate genomic DNA from at least 10 randomly selected colonies for PCR verification of integrated genetic elements
  • Inoculate production medium with sampled cultures to assess metabolic stability and product profile consistency
• Quantify key performance parameters including specific growth rate, biomass yield, and product titer at each interval
• Compare endpoint isolates with original strain using molecular techniques (e.g., restriction fragment length polymorphism or whole-genome sequencing for final validation)

Data Analysis: Statistical comparison of performance metrics across generations using ANOVA with post-hoc testing; significant deviation (p < 0.05) indicates instability requiring further strain improvement [1].

This systematic approach to stability testing addresses key regulatory concerns while providing valuable data for process optimization. Documentation should include complete records of all experimental procedures, raw data, and statistical analyses for regulatory review.

Scale-Up Challenges and Strategic Solutions

Technical Hurdles in Industrial Implementation

Scaling non-model organisms from laboratory to industrial production presents multifaceted challenges that extend beyond those encountered with established model systems. The key technical hurdles include:

Table 1: Scale-Up Challenges and Mitigation Strategies for Non-Model Organisms

Challenge	Impact on Bioprocess	Mitigation Strategy	Validation Method
Genetic instability	Declining productivity over extended cultivation	Genome reduction to remove mobile elements; orthogonal expression systems	Long-term serial passage studies (≥30 generations) [1]
Metabolic burden	Reduced growth rate and substrate conversion	Dynamic pathway regulation; metabolic balancing	13C-metabolic flux analysis; proteomic allocation assessment [3] [107]
Feedstock variability	Inconsistent performance with industrial substrates	Adaptive laboratory evolution; robustness engineering	Performance testing on actual industrial waste streams [3]
Product inhibition	Limited final titer and productivity	In situ product recovery; transporter engineering	Toxicity assays; continuous fermentation systems [107]
Oxygen transfer limitations	Reduced aerobic efficiency at large scale	Promoter engineering for microaerobic expression; bioreactor redesign	Scale-down models; dissolved oxygen gradient studies [105]

Process Scaling Framework

A systematic approach to scaling non-model organisms employs computational guidance combined with empirical validation at each scale transition. The following workflow provides a structured methodology:

Phase 1: Strain Optimization and Characterization

• Develop genome-scale metabolic models (GEMs) integrated with enzyme constraints (ecGEMs) to predict flux distributions and identify bottlenecks [3]
• Implement genome reduction strategies to remove non-essential genes, mobile elements, and competing pathways [1]
• Conduct multi-omics analysis (transcriptomics, proteomics, metabolomics) to understand regulatory networks [1] [105]

Phase 2: Laboratory-Scale Process Development

• Establish design space for critical process parameters (pH, temperature, aeration, feed strategy)
• Employ scale-down models to simulate large-scale heterogeneity
• Develop advanced process control strategies based on real-time monitoring

Phase 3: Pilot-Scale Validation

• Transfer process to 100-1000L scale with geometrically similar bioreactors
• Validate consistency of key performance indicators (titer, yield, productivity)
• Generate data for techno-economic analysis (TEA) and life cycle assessment (LCA) [3] [105]

Phase 4: Industrial-Scale Implementation

• Implement at commercial scale (≥10,000L) with rigorous monitoring
• Establish control strategy for process variability
• Continuously monitor genetic stability and product quality

The following diagram illustrates this scaling workflow with key decision points:

Case Study: Engineering Zymomonas mobilis as a Biorefinery Chassis

The gram-negative bacterium Zymomonas mobilis exemplifies both the potential and challenges of developing non-model organisms for industrial applications. This organism naturally possesses exceptional industrial characteristics including high sugar uptake rate, ethanol tolerance, and unique Entner-Doudoroff pathway metabolism [3]. However, its dominant ethanol production pathway presents a significant obstacle for producing alternative chemicals.

Engineering Strategy and Implementation

Researchers addressed this challenge through a Dominant-Metabolism Compromised Intermediate-Chassis (DMCI) strategy [3]. Rather than directly engineering the chassis for target biochemicals, they first constructed an intermediate chassis with compromised dominant metabolism by introducing a low-toxicity but cofactor-imbalanced 2,3-butanediol pathway. This approach successfully reduced flux through the native ethanol pathway while maintaining cell viability.

The experimental implementation included:

• Genome-Scale Model Enhancement: Development of an enzyme-constrained metabolic model (eciZM547) integrating enzyme kinetics to simulate flux distribution dynamics and guide pathway design [3]
• Pathway Engineering: Introduction of heterologous D-lactate dehydrogenase with concomitant downregulation of native pyruvate decarboxylase (PDC) activity
• Fermentation Optimization: Development of fed-batch strategies with controlled nutrient feeding to maximize production while minimizing by-products

Performance Outcomes and Economic Validation

The engineered Z. mobilis strain achieved remarkable production metrics:

• D-lactate titer of >140.92 g/L from glucose and >104.6 g/L from corncob residue hydrolysate
• Exceptional yield exceeding 0.97 g/g glucose [3]
• Significant reduction in ethanol by-production

Techno-economic analysis (TEA) and life cycle assessment (LCA) demonstrated commercial feasibility and greenhouse gas reduction capability when using lignocellulosic feedstocks [3]. This comprehensive approach, addressing both technical and economic considerations, provides a template for commercial development of other non-model organisms.

Essential Research Reagents and Methodologies

Successful navigation of regulatory and scale-up challenges requires specialized reagents and methodologies tailored to non-model organisms. The following toolkit represents essential resources for researchers developing these systems:

Table 2: Essential Research Reagents and Methodologies for Non-Model Organism Development

Category	Specific Reagents/Tools	Function	Application Examples
Genetic Tool Development	Endogenous CRISPR-Cas systems; MMEJ repair machinery; Constitutive & inducible promoters	Strain-specific genetic modification; Fine-tuned gene expression	Z. mobilis: Endogenous Type I-F CRISPR-Cas & MMEJ [3]
Metabolic Modeling	Genome-scale metabolic models (GEMs); Enzyme-constrained models (ecGEMs); Flux balance analysis	Predict metabolic fluxes; Identify engineering targets; Simulate pathway performance	eciZM547 for Z. mobilis [3]
Analytical & Screening	HPLC/UPLC systems; GC-MS; RNA-seq reagents; High-throughput screening microfluidics	Product quantification; Multi-omics analysis; Rapid strain screening	13C-MFA for central carbon metabolism [3]
Fermentation & Scale-Up	Bench-scale bioreactors; Dissolved oxygen probes; Online metabolite sensors; Scale-down models	Process parameter optimization; Large-scale performance prediction	Fed-batch cultivation with online monitoring [3] [106]
Stability Assessment	Selective media; PCR reagents; Sequencing kits; Long-term cryopreservation systems	Genetic stability verification; Contamination detection	30-generation serial passage protocol [1]

Integrated Development Roadmap

Navigating the path from laboratory discovery to industrial implementation requires systematic attention to both technical and regulatory considerations. The following integrated framework provides a structured approach:

This roadmap highlights the parallel progression of technical development and regulatory preparedness essential for successful commercialization. Early integration of regulatory considerations significantly reduces time to market and mitigates the risk of late-stage failures.

Non-model microorganisms represent the next frontier in industrial biotechnology, offering unprecedented opportunities for sustainable production of valuable chemicals, materials, and pharmaceuticals. By systematically addressing the dual challenges of regulatory compliance and scale-up implementation through integrated frameworks, researchers can unlock the immense potential of these microbial workhorses. The strategies outlined in this technical guide provide a pathway to transform promising laboratory strains into industrially viable platforms that will drive the bioeconomy forward.

As the field advances, emerging technologies including artificial intelligence, automated strain engineering, and continuous bioprocessing will further accelerate the development timeline. However, the fundamental principles of thorough characterization, genetic stability, and process robustness will remain essential for successful industrial adoption of non-model microbial cell factories.

Conclusion

The strategic development of non-model organisms as microbial cell factories represents a frontier in biomanufacturing, moving beyond the constraints of traditional hosts to access a wider chemical space. Success hinges on a multidisciplinary approach that combines advanced genetic tools, systems-level metabolic understanding, and pragmatic process evaluation. Future directions will be shaped by the integration of AI and automation to accelerate the DBTL cycle, the continued expansion of the genetic toolbox for recalcitrant hosts, and a stronger focus on designing strains for specific, sustainable feedstocks like C1 compounds. For biomedical and clinical research, this progress promises more efficient and sustainable production routes for complex natural products, APIs, and diagnostic precursors, ultimately strengthening and greening the drug development pipeline.

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