Data-driven integration of genome-scale regulatory and metabolic network models

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

doi:10.3389/fmicb.2015.00409

Title: Data-driven integration of genome-scale regulatory and metabolic network models

Journal Article · Tue May 05 00:00:00 EDT 2015 · Frontiers in Microbiology

DOI:https://doi.org/10.3389/fmicb.2015.00409· OSTI ID:1211515

Imam, Saheed ^[1]; Schauble, Sascha ^[2]; Brooks, Aaron N. ^[1]; Baliga, Nitin S. ^[3]; Price, Nathan D. ^[1]

Institute for Systems Biology, Seattle, WA (United States)
Institute for Systems Biology, Seattle, WA (United States); Friedrich Schiller Univ., Jena (Germany)
Institute for Systems Biology, Seattle, WA (United States); Univ. of Washington, Seattle, WA (United States). Dept. of Biology and Microbiology; Univ. of Washington, Seattle, WA (United States). Molecular and Cellular Biology Program

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000426; EE0006315

OSTI ID:: 1211515

Journal Information:: Frontiers in Microbiology, Vol. 6; ISSN 1664-302X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 39 works

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Perspective: Dimensions of the scientific method Voit, Eberhard O. PLOS Computational Biology, Vol. 15, Issue 9 https://doi.org/10.1371/journal.pcbi.1007279	journal	September 2019
The Genome-Scale Integrated Networks in Microorganisms Hao, Tong; Wu, Dan; Zhao, Lingxuan Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.00296	journal	February 2018
Genome-scale metabolic models applied to human health and disease: Genome-scale metabolic models Cook, Daniel J.; Nielsen, Jens Wiley Interdisciplinary Reviews: Systems Biology and Medicine, Vol. 9, Issue 6 https://doi.org/10.1002/wsbm.1393	journal	June 2017
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