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Data-driven integration of genome-scale regulatory and metabolic network models [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 2015.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
Article numbers 409 : digital, PDF file
Additional Creators
Lawrence Berkeley National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.
Report Numbers
E 1.99:1211515
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Note
Published through SciTech Connect.
05/05/2015.
Frontiers in Microbiology 6 ISSN 1664-302X AM
Saheed Imam; Sascha Schauble; Aaron N. Brooks; Nitin S. Baliga; Nathan D. Price.
Funding Information
AR0000426
EE0006315
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