Constraint-Based Dynamic Simulation of Complex Microbiomes with µbialSim

The cohabitation in high-diversity consortia is the norm for microbial species in most natural and medical settings. Individual species‘ metabolic activities both depend on and modulate their immediate environment, making the full consortium a complex system which continuously responds to its embedding environment in a self-organizing manner. For many applications, a means for targeted manipulation of these systems would be highly desirable. However, the many layers, and the high diversity of interactions in these systems make them difficult to analyze, understand, and ultimatly to control. Focussing on the exchange of metabolites between species, we present the numerical simulator code µbialSim (pronounced „microbialsim“), which allows for the simulation of high diversity consortia following the dynamic Flux-Balance-Analysis approach which relies on genome-scale metabolic network models of individual species. We demonstrate its capabilities by simulating a 773-species human gut microbiome. Batch and chemostat conditions can be considered and simulated trajectories provide quantitative predictions on the dynamics of compound concentrations, species abundancies, and intracellular metabolic flux distributions, enabling an in-depth analysis of metabolic exchange patterns. Once a microbial consortium of interest is implemented in µbialSim, scenario simulations allow for the fast and efficient screening of intervention strategies able to nudge the consortium to desired state, for example increased methane production in anerobic digestion microbiomes, or healthy states in gut microbiomes. The faithful representation of the complexity of metabolic interactions in microbial consortia in computational models paves the way to both explore the potential and limits of control strategies, and the de novo design of synthetic consortia with defined properties.

A preprint on µbialSim is available at: https://www.biorxiv.org/content/10.1101/716126v1

The Matlab source code is available under the GNU General Public License v3.0 at: https://git.ufz.de/UMBSysBio/microbialsim