Prediction and Simulation of Metabolic Interactions in Artificial Microbial Ecosystems

Most microorganisms in nature belong to complex communities where they constantly interact with other microbial species. Understanding and predicting these interactions is an important open challenge relevant to many disciplines, ranging from metabolic engineering to human infectious diseases. A promising approach towards this goal is to extend existing systems biology approaches for predictive modeling. In particular, genome-scale models of metabolism can be used to predict the growth rate and all the internal and exchange metabolic rates (fluxes) of a microbe, e.g. with the approach of flux balance analysis (FBA). Recent extensions of flux balance models have been used to predict whether a given pair of microbes would display a cross-feeding interaction during growth on appropriately designed media. In addition, the prototype of a platform for Computation of Microbial Ecosystems in Time and Space (COMETS) has been built to perform spatio-temporal simulations of dynamical processes in microbial ecosystems. This work extends and applies these frameworks to address specific questions about mutualistic interactions in microbial ecosystems relevant for metabolic engineering and bioenergy applications. In particular, I focus (i) on the pair of organisms Shewanella oneidensis and Lactococcus lactis, which has promising potential for novel applications in wastewater treatment and bioremediation, and (ii) on interactions involving methylotroph organisms such as Methylobacterium extorquens. This organism is able to grow on compounds such as methane and methanol which have gained increasing interest, since methanol can be produced from diverse renewable sources and represents a valuable feedstock for biotechnological applications.