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Genomic Science Program

Systems Biology for Energy and the Environment

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Genomic Science Program

DOE Genomic Science Program 2014 Strategic Plan

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Mission-Driven Systems Biology

Publication date: October 2014

Suggested citation: U.S. DOE. 2014.DOE Genomic Science Program: Mission-Driven Systems Biology. 2014 Strategic Plan. U.S. Department of Energy Office of Science.

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Background: DOE-sponsored research has been critical to developing systems biology into the expansive conceptual approach described in the 2009 report by the National Academy of Sciences, A New Biology for the 21st Century:

"Systems biology seeks a deep quantitative understanding of complex biological processes through dynamic interaction of components that may include multiple molecular, cellular, organismal, population, community, and ecosystem functions. It builds on foundational large-scale cataloguing efforts (e.g., genomics, proteomics, metabolomics, etc.) that specify the 'parts list' needed for constructing models. The models relate the properties of parts to the dynamic operation of the systems they participate in."

High-throughput genome sequencing of microbes, plants, and complex environmental assemblages of organisms has provided the vital blueprint necessary to understand the functional potential of organisms and interactive communities. By examining the translation of genetic codes into integrated networks of regulatory elements, catalytic proteins, and metabolic networks that define all living organisms, systems biology research sheds light on the fundamental principles that govern functional properties of organisms and how their processes respond to community interactions and environmental variables.

DOE's Genomic Science program supports systems biology research aimed at identifying these foundational principles driving biological systems of plants, microbes, and multispecies communities relevant to DOE missions in energy and the environment. Building on the foundation of sequenced genomes and metagenomes, the program focuses on a tightly coupled approach that combines experimental physiology, omics-driven analytical techniques, and computational modeling of functional biological networks.

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