Data-driven integration of genome-scale regulatory and metabolic network models
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. 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.
- Authors:
-
- 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
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- OSTI Identifier:
- 1211515
- Grant/Contract Number:
- AR0000426; EE0006315
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Microbiology
- Additional Journal Information:
- Journal Volume: 6; Journal ID: ISSN 1664-302X
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; metabolic networks; transcriptional networks; constraint-based modeling; network integration; flux balance analysis; signaling, regulation; metabolism
Citation Formats
Imam, Saheed, Schauble, Sascha, Brooks, Aaron N., Baliga, Nitin S., and Price, Nathan D. Data-driven integration of genome-scale regulatory and metabolic network models. United States: N. p., 2015.
Web. doi:10.3389/fmicb.2015.00409.
Imam, Saheed, Schauble, Sascha, Brooks, Aaron N., Baliga, Nitin S., & Price, Nathan D. Data-driven integration of genome-scale regulatory and metabolic network models. United States. https://doi.org/10.3389/fmicb.2015.00409
Imam, Saheed, Schauble, Sascha, Brooks, Aaron N., Baliga, Nitin S., and Price, Nathan D. Tue .
"Data-driven integration of genome-scale regulatory and metabolic network models". United States. https://doi.org/10.3389/fmicb.2015.00409. https://www.osti.gov/servlets/purl/1211515.
@article{osti_1211515,
title = {Data-driven integration of genome-scale regulatory and metabolic network models},
author = {Imam, Saheed and Schauble, Sascha and Brooks, Aaron N. and Baliga, Nitin S. and Price, Nathan D.},
abstractNote = {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.},
doi = {10.3389/fmicb.2015.00409},
journal = {Frontiers in Microbiology},
number = ,
volume = 6,
place = {United States},
year = {Tue May 05 00:00:00 EDT 2015},
month = {Tue May 05 00:00:00 EDT 2015}
}
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