My research interests are to apply to tools of computational modeling approaches and artificial intelligence approaches to understanding the molecular mechanisms of microbial community dynamics. Microbial communities are characterized by their tremendous diversity and their functional redundancy. These communities of microorganisms and how they interface with their environment are defined less by the presence or abundance of single species, but rather by the emergent properties of their metabolic and regulatory interactions. Using high-throughput transcriptomic, proteomic, and metabolomic data, it becomes possible to identify the underlying patterns of interactions between organisms and between organisms and their environment.
The result of this analysis is computational models that drive the following experimental goals: 1. Identify the specific molecular mechanisms by with microorganisms sense, respond to, and manipulate other members of their community and their environment. 2. Predict the behavior of organisms and communities for a range of possible environmental conditions, and how those behaviors contribute to the carbon sequestration, stability, and dynamics of ecosystems. 3. Predict the consequence of perturbations to the systems and determine how communities might be rationally designed to drive microbiome communities to desired states.
Peter Larsen
Assistant Computational Biologist
Biosciences Division
Argonne National Laboratory
