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Computational and Predictive Biology

Analyzing biological systems using computational techniques

The pursuit of a holistic understanding of biology requires integration of progressively larger and more complex, experimentally generated datasets, enabled by advancing technologies. Biological research is increasingly dependent on, and driven by, evolving computational analysis and prediction.

The Computational and Predictive Biology Group brings together and collaborates with scientists from related experimental, computational, and technical disciplines to build increasingly detailed computational models of biological systems. These efforts improve accuracy and statistical power, and thereby enable researchers to gain new insights and predict properties and outcomes of biological systems that advance scientific understanding. These models help guide the discovery process, informing experimental design and suggesting new approaches to maximize scientific benefit.

Members of our group work on a range of problems, including investigation of bioenergy plants as feedstock for advanced biofuels; how microbes interact with plants under natural and engineered conditions; and how an individual’s genes may lead them to be more susceptible to pain and opioid addiction. To tackle such problems we also develop new technical methods and systems such as KBase (the Department of Energy Systems Biology Knowledgebase), and work with researchers at the Oak Ridge Leadership Computing Facility to develop exascale applications that run on some of the world’s fastest supercomputers.

Our research is supported by the DOE Office of Science Biological and Environmental Research program through the Center for Bioenergy Innovation, the Plant-Microbe Interfaces Science Focus Area, and individual investigator-driven projects from DOE and other federal agencies such as the National Institutes of Health and the Veterans Administration.