Research Articles
Quantifying Uncertainty: Statistical Methods for Robust Flux Estimation in Drug Discovery and Biomedical Research
Accurate quantification of flux uncertainty is critical for validating therapeutic targets, optimizing microbial cell factories, and ensuring the reliability of metabolic models in biomedical research.
Optimizing 13C Labeling Strategies for High-Resolution Metabolic Flux Analysis
This article provides a comprehensive guide for researchers and scientists on optimizing 13C substrate labeling patterns to achieve high-resolution metabolic flux analysis (MFA).
Parallel Labeling Experiments for 13C Metabolic Flux Analysis: A Comprehensive Guide from Foundations to Clinical Applications
This article provides a comprehensive overview of parallel labeling experiments for 13C metabolic flux analysis (13C-MFA), a powerful methodology for quantifying intracellular metabolic reaction rates.
Bridging the Genomic Gaps: Advanced Annotation and Gap-Filling Strategies for Non-Model Organisms
Accurate genome annotation for non-model organisms is a critical yet challenging frontier in genomics, with profound implications for biomedical and drug discovery research.
CHESHIRE Deep Learning: A Complete Guide to Gap-Filling Metabolic Networks for Biomedical Research
This comprehensive guide explores CHESHIRE (CHEbyshev Spectral HyperlInk pREdictor), a deep learning method that predicts missing reactions in Genome-scale Metabolic Models (GEMs) using only metabolic network topology, without requiring experimental...
Navigating Flux Inconsistencies: A Researcher's Guide to Robust Metabolic Modeling
Flux inconsistent reactions present significant challenges in genome-scale metabolic models (GEMs), undermining predictive accuracy in biomedical and biotechnological applications.
Gap-Filling Algorithms for Metabolic Models: From Foundations to AI-Driven Applications in Biomedical Research
Genome-scale metabolic models (GSMMs) are powerful computational tools for predicting cellular phenotypes, but their accuracy is often limited by metabolic gaps resulting from incomplete genomic annotations and knowledge.
Flux Balance Analysis of E. coli Central Carbon Metabolism: A Comprehensive Guide for Systems Biology and Metabolic Engineering
This article provides a comprehensive resource for researchers and scientists on applying Flux Balance Analysis (FBA) to Escherichia coli's central carbon metabolism (CCM).
Flux Balance Analysis for E. coli Metabolism: From Foundational Principles to Advanced Biomedical Applications
This article provides a comprehensive overview of Flux Balance Analysis (FBA) as a cornerstone constraint-based modeling approach for elucidating Escherichia coli metabolism.
A Comprehensive Comparison of Flux Balance Analysis Software for E. coli Metabolic Modeling
Flux Balance Analysis (FBA) is a cornerstone of constraint-based modeling, enabling the prediction of metabolic behavior in Escherichia coli, a key organism in biotechnology and biomedical research.