Fusing Individual-based Models with Genome-scale Metabolic Networks - a Novel Approach for Quantifying Emergent Microbial Community Spatiotemporal Dynamics

Metabolic capabilities of organisms govern their growth characteristics and their capability to adapt to changing environmental conditions. In natural systems, substrates can change dynamically and spatially with multiple species competing for resources. Genome-scale metabolic network models (GEMs) offer quantitative frameworks for consideration of the metabolism of organisms in the concourse. Community modeling has been used in studies to analyze multi-species consortia, however the high dimensionality of GEMs limits their use in complex systems especially for applications in spatially-explicit individual-based models (IBM). The use of IBM permits exploration of species interaction with their environment and neighbors within a prescribed physical domain. This study proposes to capitalize on recently developed algorithms, redGEM and lumpGEM for the unbiased and systematic reduction of GEMs into reduced metabolic models (rGEMs) that retain compatibility with their parent GEMs. The reduced computational burden of rGEMs makes them scalable and makes their incorporation in IBM frameworks feasible. We formulated a hybrid model fusing an individual-based model (IBM) with advanced thermodynamics-based flux analysis (TFA) and reanalyzed a 3-member consortium comprised of E. coli, S. enterica and M. extorquens. While the IBM provides detailed description of the physical environment, dispersion and motility characteristics of organisms, the rGEMs and TFA provide a reduced description of the stoichiometric properties and bioenergetics constraints imposed on organisms, including intracellular and extracellular concentrations of all defined metabolites at the local scale experienced by an individual. We report the first application of such a hybrid approach that systematically merges stoichiometric models and individual-based models for study of interacting multispecies communities in simple physical spaces (surfaces). The expansion to consideration of microbial communities in complex environments will be discussed.