title: Refining metabolic models and accounting for regulatory effects.
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.
authors.affiliation: DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, United States; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, United States.
authors.forename: Joonhoon
authors.initials: J
authors.lastname: Kim
authors.affiliation: 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.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Reed
mesh_terms: D001709:Biotechnology; D023281:Genomics; D006801:Humans; D053858:Metabolic Networks and Pathways; D055432:Metabolomics; D008954:Models, Biological