Abstract

Microbial consortium is a complex adaptive system with higher‐order dynamic characteristics that are not present by individual members. To accurately predict the social interactions, we formulate a set of unstructured kinetic models to quantitatively capture the dynamic interactions of multiple microbial species. By introducing an interaction coefficient, we analytically derived the steady‐state solutions for the interacting species and the substrate‐depleting profile in the chemostat. We analyzed the stability of the possible coexisting states defined by competition, parasitism, amensalism, commensalism, and cooperation. Our model predicts that only parasitism, commensalism, and cooperation could lead to stable coexisting states. We also determined the optimal social interaction criteria of microbial coculture when sequential metabolic reactions are compartmentalized into two distinct species. Coupled with Luedeking–Piret and Michaelis–Menten equations, accumulation of metabolic intermediates in one species and formation of end‐product in another species could be derived and assessed. We discovered that parasitism consortia disfavor the bioconversion of intermediate to final product; and commensalism consortia could efficiently convert metabolic intermediates to final product and maintain metabolic homeostasis with a broad range of operational conditions (i.e., dilution rates); whereas cooperative consortia leads to highly nonlinear pattern of precursor accumulation and end‐product formation. The underlying dynamics and emergent properties of microbial consortia may provide critical knowledge for us to understand ecological coexisting states, engineer efficient bioconversion process, deliver effective gut therapeutics as well as elucidate probiotic‐pathogen or tumor‐host interactions in general.

Highlights

  • Microbes in nature form diverse social interactions and dynamically respond to metabolic and environmental cues at community level

  • We discovered that commensalism consortia could efficiently convert metabolic intermediate to final product and maintain metabolic homeostasis with a broad range of operational conditions

  • To accurately predict the social interactions, we formulate a set of unstructured kinetic models to quantitatively describe the dynamic interactions of multiple microbial species

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Summary

| INTRODUCTION

Microbes in nature form diverse social interactions and dynamically respond to metabolic and environmental cues at community level. Kinetic models have been increasingly important to help us understand microbial social interactions at the consortia‐level (Kong et al, 2018; Song et al, 2014; Succurro & Ebenhöh, 2018). Most of these kinetic equations are developed by correlating cell growth fitness with the nutrient or environmental conditions of the interacting species. We developed a set of unstructured kinetic models to quantitatively capture the dynamic interactions of multiple microbial species. By analyzing the solutions for microbial consortia with sequential metabolic reactions compartmentalized into distinct species, we revealed the design criteria of microbial coculture engineering in chemostat. The dynamics of microbial competition and cooperation may facilitate us to assemble diverse microbial species with defined social interactions for important biotechnological and biomedical applications

| COMPUTATIONAL METHODS
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