(719e) A Multi-Level Programming Framework for Metabolic Modeling of Microbial Communities | AIChE

(719e) A Multi-Level Programming Framework for Metabolic Modeling of Microbial Communities


Zomorrodi, A. R. - Presenter, The Pennsylvania State Univeristy
Maranas, C. D. - Presenter, The Pennsylvania State University

Microorganisms in their natural environments rarely live alone but rather function in concert in integrative and interactive units, (i.e., 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. Spurred by recent advances in reconstruction and analysis of metabolic networks of individual microorganisms, researchers have started to reconstruct and analyze in silico, metabolic models of simple microbial consortia. Despite these efforts, a comprehensive flux balance analysis modeling procedure to analyze and characterize microbial communities of increasing size with any combination of positive or negative interactions is still lacking. Here, we introduce a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level 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 a separate biomass maximization problem for each species as inner problems and subsequently integrate them in the outer problem by selecting the alternate optima of the inner problems so as a community-level objective function is optimized. This modeling framework is general enough to capture any type of interactions (positive, negative or combination of both) for any number of species (or guilds) involved. We first analyze a simple and well-determined microbial community involving the syntrophic association between D. vulgaris and M. maripaludis to demonstrate its ability in recapitulating known interactions. Next, we considered modeling two more complex ecological systems to determine the relative abundance of each microorganism and uncover the direction and extent of inter-species metabolite transfers. This study demonstrates the importance of trade-offs between species- and community-level fitness criteria and lays the foundation for analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models.