This article provides a comprehensive exploration of COBRApy, the essential Python package for constraint-based reconstruction and analysis of genome-scale metabolic models.
13C Metabolic Flux Analysis (13C-MFA) is a powerful technique for quantifying intracellular metabolic reaction rates, a crucial capability for understanding cell physiology in metabolic engineering, biotechnology, and disease mechanism research.
This article provides a comprehensive guide for researchers and scientists on optimizing 13C substrate labeling patterns to achieve high-resolution metabolic flux analysis (MFA).
Artificial Metabolic Network (AMN) hybrid models represent a transformative approach in systems biology, integrating mechanistic Genome-Scale Metabolic Models (GEMs) with machine learning to overcome the limitations of traditional constraint-based methods.
This article provides a comprehensive guide to the application of Monte Carlo sampling in 13C-based metabolic flux analysis (MFA), a critical technique for quantifying reaction rates in living cells.
This article provides a comprehensive overview of the integration of Flux Variability Analysis (FVA) with 13C-derived metabolic constraints, a powerful approach to refine genome-scale metabolic models.
This article provides a comprehensive overview of the Elementary Metabolite Units (EMU) framework, a powerful computational methodology that has transformed 13C-Metabolic Flux Analysis (13C-MFA).
This article provides a comprehensive guide for researchers and scientists on the integration of 13C Metabolic Flux Analysis (13C-MFA) with constraint-based models (CBMs) in plant systems.
Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) is a powerful computational method that integrates high-resolution isotopic labeling data from 13C-tracer experiments with comprehensive genome-scale metabolic models.
This article provides a comprehensive guide for researchers and drug development professionals on designing and executing successful 13C labeling experiments with NMR spectroscopy.