OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities

TLDR

Microorganisms typically inhabit complex communities, yet the metabolic roles of individual species and their interactions remain poorly understood. This study aims to develop an efficient modeling framework to illuminate these metabolic interactions in microbial communities. OptCom implements a multi‑level, multi‑objective flux balance analysis that optimizes species‑level fitness within inner problems while maximizing community fitness in the outer problem, enabling modeling of any interaction type among any number of species. Using OptCom, the authors quantified syntrophic associations, assessed sub‑optimal growth in phototrophic mats, and revealed trade‑offs between species and community fitness, establishing a foundation for genome‑scale metabolic analysis of multi‑species systems.

Abstract

Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models.