Systems biology approaches to metabolic flux and network analysis

Abstract

Metabolic networks present one of biology's fundamental challenges thousands of interconnected reactions whose coordinated regulation enables cellular function yet defies intuitive understanding. This research applied constraint-based modeling and metabolic flux analysis to characterize network topology and flux distributions in Escherichia coli under diverse growth conditions. Genome-scale metabolic models were refined using experimental flux measurements from 13C-labeling experiments across 12 carbon sources. Flux variability analysis identified 847 reactions with context-dependent activity, while elementary mode analysis enumerated 2.3 million distinct flux distributions supporting growth. Network centrality metrics identified 43 hub metabolites whose perturbation disproportionately affects network function. Comparison between aerobic and anaerobic conditions revealed wholesale pathway rewiring, with 312 reactions showing greater than 2-fold flux changes. These analyses establish quantitative frameworks for predicting metabolic responses to genetic and environmental perturbations, with applications in metabolic engineering and drug target identification.