Modelling the Bacterial Metabolism in the Rich Environment

Background/Objectives: Bacteria rarely live in well-defined environment. Even in the biotechnological reactors cells are surrounded by hundreds of byproducts and their derivatives, let alone complex microbial communities in the gut or soil. Yet all modern genome-scale metabolic (GEM) models were developed with minimal media in mind.Methods: In this study, we propose the use of a novel FBA-PRCC (Flux Balance Analysis – Partial Rank Correlation Coefficient) approach for the analysis of GEM models under nutrient-rich conditions. This method combines flux space sampling with global sensitivity analysis (GSA), enabling a more comprehensive understanding of the metabolic behavior of organisms beyond traditional constraint-based modeling techniques. Results: Using FBA-PRCC, we identify two novel modes of species–metabolite interaction: attraction and avoidance. These concepts offer a framework to quantify and utilize interspecies metabolic dependencies, potentially transforming how we understand community function in microbiomes. Our results show that sensitivity coefficients provide complementary insights to standard knockout analysis, Flux Variability Analysis (FVA), and CoPE-FBA. However, analysis of auxotrophic mutants reveals that sensitivity coefficients are highly non-robust in the presence of alternative pathways: even weakly active bypasses can suppress signals from key metabolic routes.Conclusions: While FBA models are usually developed for well-characterized laboratory strains in controlled conditions, they fail when applied to bacteria in the complex environments like the human gut or skin. Better description of the metabolite transport and new modelling approaches are required to overcome this problem.