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Curr Opin Biotechnol. 2014 Oct;29:34-8. doi: 10.1016/j.copbio.2014.02.009. Epub 2014 Mar 14.

Refining metabolic models and accounting for regulatory effects.

Author information

1
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, United States; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, United States.
2
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, United States; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, United States. Electronic address: reed@engr.wisc.edu.

Abstract

Advances in genome-scale metabolic modeling allow us to investigate and engineer metabolism at a systems level. Metabolic network reconstructions have been made for many organisms and computational approaches have been developed to convert these reconstructions into predictive models. However, due to incomplete knowledge these reconstructions often have missing or extraneous components and interactions, which can be identified by reconciling model predictions with experimental data. Recent studies have provided methods to further improve metabolic model predictions by incorporating transcriptional regulatory interactions and high-throughput omics data to yield context-specific metabolic models. Here we discuss recent approaches for resolving model-data discrepancies and building context-specific metabolic models. Once developed highly accurate metabolic models can be used in a variety of biotechnology applications.

PMID:
24632483
DOI:
10.1016/j.copbio.2014.02.009
[Indexed for MEDLINE]
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