Systems Biology of Microbial Communities

Microorganisms typically occur as highly divers communities in nature. This allows for a multiude of interactions to occur between members of the community and makes complex transformation processes possible. Anaerobic digestion is a typically example for such a community-driven process in which organic material is transformed to methane. To optimally control this process in biogas plants, mathematical models have been developed. These models, however, typically only consider major process steps und do not directly consider the high phenotypic diversity of involved communities. Both their composition and activity can today be assessed with high accuracy by culture-independent methods including metagenomics, metatranscriptomics, and metaproteomics.

Our aim is to use such data to create mathematical models detailing the metabolic transformation network within microbial communities. Towards this end, species-specific metabolic network models are inferred and coupled to a dynamic community model following the dynamic Flux-Balance-Analysis approach. Due to their predictive power, such models will be used to early detect looming process breakdowns and to initiate proper counter measures, as well as to explore the potential for flexibel on-demand biogas production.

Following a Systems Biology approach, the McBiogas project requires the close interdisciplinary cooperation between experimental and theoretical work. We use lab-scale biogas reactors as our experimental system, develop bioinformatic methods for the inference of metabolic network models, and design and implement modeling software for the simulation of microbial communities.