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Front Microbiol. 2015 May 5;6:409. doi: 10.3389/fmicb.2015.00409. eCollection 2015.

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

Author information

1
Institute for Systems Biology Seattle, WA, USA.
2
Institute for Systems Biology Seattle, WA, USA ; Jena University Language and Information Engineering Lab, Friedrich-Schiller-University Jena Jena, Germany.
3
Institute for Systems Biology Seattle, WA, USA ; Departments of Biology and Microbiology, University of Washington Seattle, WA, USA ; Molecular and Cellular Biology Program, University of Washington Seattle, WA, USA ; Lawrence Berkeley National Lab Berkeley, CA, USA.

Abstract

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. 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.

KEYWORDS:

constraint-based modeling; flux balance analysis; metabolic networks; metabolism; network integration; regulation; signaling; transcriptional networks

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