Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

Abstract

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

“…Numerous studies have employed GEMs to investigate metabolic interactions and functionality within microbial communities, including those found in the human gut (8), termite gut (9), mangrove sediments (10), soil microbial communities (11), and plant root (12). Community-scale metabolic models are typically constructed using: (i) the mixed-bag approach, which involves integrating all metabolic pathways and transport reactions into a single model with one cytosolic and one extracellular compartment; (ii) compartmentalization, where multiple GEMs are combined into a single stoichiometric matrix, with each species assigned to a distinct compartment; (iii) costless secretion, wherein models are simulated using a dynamically and iteratively updated medium based on exchange reactions and metabolites within the community (13, 14). The choice of approach depends on the specific objectives and scenarios.…”