Modeling Emergent Metabolic Interactions within Microbial Communities
Siegel, Ryan
Siegel, Ryan
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Abstract
Microbial organisms live in complex communities where they interact with their neighbors through metabolic processes. These communities regulate many processes in the human body, for example in the human gut microbiome; thus, understanding their structure and dynamics is important. Several methods for Microbial Community-Scale Metabolic Modeling (MCMM) have been developed to understand metabolism in microbial communities. Each method balances trade-offs between simplifying assumptions and computational complexity. Steady-state approaches efficiently provide a snapshot of microbial community metabolism, but require unsupported, simplifying assumptions, such as the optimization of overall community growth. Dynamic methods simulate community metabolism through emergent interactions that arise from individual optimization of each organism�s growth but are computationally inefficient. We present a new steady-state method, termed greedy interaction flux balance analysis (giFBA), an efficient MCMM approach maintaining the biologically relevant assumptions that each organism is individually optimized and resources in competition have equal uptake rates. We compared giFBA to common steady-state methods (compartmentalized FBA & MICOM), using both synthetic and real models. In synthetic, competing communities, standard methods result in pareto frontiers or unintuitive solutions, while our method produces similar, but more intuitive solutions. A synthetic, growth-coupled community revealed a surprisingly complex solution, with period-2 stability, where again our method provides an intuitive result. We then applied our method to genome-scale metabolic models of bacteria from the human gut microbiome to demonstrate its ability to provide insight into health-relevant metabolism. Our simulations demonstrated cross-feeding between the common gut species Escherichia coli & Bacteroides thetaiotaomicron, coupled-growth affecting butyrate production between Bifidobacterium longum subsp. infantis & Anaerobutyricum hallii , and competition between 4 bacteria - including the opportunistic gut pathogen Clostridioides difficile. The method we developed is an open-source, Python package, and general tool with broad applicability in gut health, soil science, and other fields.
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Date
1/1/2026
Student Status
Graduate Student
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Poster
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Biomedical Engineering
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College of Engineering and Mathematical Sciences
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Engineering