DOE Genomic Science Program Foundational Scientific Focus Area (FSFA)
The Foundational Scientific Focus Area (FSFA), "Principles of Microbial Design," is made up of a multidisciplinary team of investigators at four institutions who seek to identify fundamental principles governing microbial community structure and function and from which higher-order properties, such as resistance and resilience, emerge. The FSFA is led by Pacific Northwest National Laboratory (PNNL) and is part of the U.S. Department of Energy's (DOE's) Genomic Science Program.
Why Does It Matter? Knowing the fundamental rules and basic underlying principles governing the functioning of complex, interacting cellular systems can guide the engineering of stable microbial communities for sustainable cost-efficient production of biofuels, and inform science and policy associated with environmental stewardship and climate change.
Our research plan supports DOE goals to achieve a predictive understanding of microbes and microbial communities and to provide foundational knowledge for rational design of microbial systems. We do so through four Research Themes shown here.
Methods: To identify candidate design principles, we are examining autotrophic microbial communities found in diverse extreme environments. By combining field studies with experiments using simplified consortia and isolates from those environments, we are identifying common attributes across the different systems.
The reduced complexity of our model systems allows us to use derived genome sequence data, which enables use of multiple omics analyses to test underlying mechanistic relationships. This, in turn, provides a foundation for building models of community metabolism, spatial relationships, and regulatory and interaction networks. These models will be used iteratively with targeted experiments to predict the dynamic behavior of communities and understand how higher-order community properties arise.
What's Next? Future work will be aimed at confirming identified design principles using more complex natural systems, then applying these principles to engineer new microbial communities with predictable behavior.