This article provides a comprehensive guide for researchers and scientists on implementing Flux Balance Analysis (FBA) for E.
This article provides a comprehensive overview of the expanding CRISPR-Cas9 toolkit for metabolic pathway engineering, moving beyond simple gene knockouts to include transcriptional control, epigenetic editing, and base editing.
This article provides a comprehensive guide to the principles of synthetic biology for metabolic engineers in research and drug development.
This article provides a systematic comparison of stoichiometric and kinetic modeling approaches for analyzing cellular metabolism, tailored for researchers and drug development professionals.
Dynamic metabolic engineering represents a paradigm shift from static genetic modification to the design of autonomous, self-regulating microbial cell factories.
Selecting the optimal microbial host is a critical, multi-factorial decision that determines the success of biomanufacturing processes for pharmaceuticals and chemicals.
This article provides a comprehensive overview of the Design-Build-Test-Learn (DBTL) cycle, a foundational framework in metabolic engineering for developing microbial cell factories.
Genome-scale metabolic models (GEMs) provide powerful computational frameworks for systems-level metabolic studies by describing gene-protein-reaction associations across entire metabolic genes.
This guide provides a comprehensive introduction to Flux Balance Analysis (FBA), a cornerstone computational method in systems biology and metabolic engineering.
This article explores the critical challenge of stoichiometric inconsistencies in genome-scale metabolic models (GEMs) and their role in creating network gaps that impair predictive accuracy. Aimed at researchers, scientists, and drug development professionals, it details how these structural errors arise, their impact on flux balance analysis, and the computational methods—from established algorithms like fastGapFill to emerging deep learning tools like CHESHIRE—used to detect and resolve them. The content further covers troubleshooting techniques for error isolation, the validation of gap-filling solutions, and the implications of robust, stoichiometrically consistent models for advancing biomedical research and therapeutic discovery.