EAGER SitS: Developing a Next Generation Modeling Approach for Predicting Microbial Processes in Soil

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Microbes are the key organisms at the core of many of Earth's life support systems. Over the last decade, great progress has been made in identifying the species of microbes present in many environments. In addition, many of the genes that control what these microbes do have been discovered. This research will uncover how microbes control critical ecosystem services. This project will develop a new modeling framework to combine information about microbial activities with environmental data from sensor networks. This approach will advance understanding of how microbes control cycles of elements and the flow of energy on Earth. This work will create valuable tools for managing natural ecosystems and industrial processes. The project will also support undergraduate research as part of the Semester in Environmental Science program at the Marine Biological Laboratory in Woods Hole, MA.In most natural microbial systems, details of microbial metabolic and regulatory networks will remain unknown in the foreseeable future and are too complex to be folded into models of climate, soil fertility for agriculture, or waste management. Rather, development of a scalable biogeochemical modeling approach is critical for detecting the principles by which complex soil systems operate under co-control by microbes and environmental factors. This project will develop a flexible framework for analyzing microbial biogeochemistry from the thermodynamic perspective of maximum entropy production (MEP). The work takes advantage of the high diversity of microbial communities to enable thermodynamically-based predictions about system-level biogeochemical response to global change. The planned thermodynamically-constrained metabolic modeling approach will address two key challenges associated with modeling microbial communities: (1) capturing community self-organization and expression of metabolic function, and (2) re-parameterizing reaction kinetics dynamically as microbial community composition shifts in response to local environmental conditions. The distributed metabolic network modeling approach features a minimal set of optimal control variables that vary over time and space. These variables control stoichiometry and thermodynamics of a distributed metabolic network as well as reaction rates. To leverage existing modeling and experimental work for model-measurement comparisons, the first phase of research will focus on a simplified network including methanogenesis and methanotrophy. Ultimately, the goal is to integrate sensor-derived information with diverse, known microbial capabilities (constrained by thermodynamic principles), to predict shifting activities of microbial communities in soils using far fewer parameters than would be required with conventional modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.