Abstract
Social interaction between microbes can be described at many levels of details, ranging from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing the best level to model microbial communities without losing generality remains a challenge. Here we propose to model cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems, which is suitable to empirical data. We applied our method to three published examples of multi-strain Escherichia coli communities with increasing complexity consisting of uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately described empirical data and could quantify exchange rates elusive by other means, such as the byproduct secretions, even for a complex community of 14 amino acid auxotrophs. We used the three models to study each community’s limits of robustness to perturbations such as variations in resource supply, antibiotic treatments and invasion by other “cheaters” species. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities.
Significance statement The behavior of complex multispecies communities such as the human microbiome is hard to predict by its composition alone. Our efforts to engineer such communities would benefit from mechanistic models that accurately describe how microbes exchange metabolites with each other and how their environment shapes these exchanges. But what is the most appropriate level of details to model microbial interaction? We propose an intermediate level to model metabolic exchanges that accurately describes population dynamics and stability of microbial communities. We demonstrate this approach by constraining models with experimental data from three laboratory communities with increasing levels of complexity. Each model allows us to predict metabolic byproduct leakage fractions as well as how external perturbations such as nutrient variations or addition of antibiotics impact those communities. Our work paves the way to model real-world applications including precise engineering of the microbiome to improve human health.