Abstract
Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor-gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions.
Recommended Citation
D. Barua et al., "An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models," PLoS Computational Biology, vol. 6, no. 10, PLoS ONE, Oct 2010.
The definitive version is available at https://doi.org/10.1371/journal.pcbi.1000970
Department(s)
Chemical and Biochemical Engineering
International Standard Serial Number (ISSN)
1553-734X
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2010 PLoS ONE, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Oct 2010
PubMed ID
21060853
Comments
This work was funded by the U.S. Department of Energy Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC02-07ER64494) and by the U.S. Department of Energy (DOE) Office of Biological and Environmental Research under the Genomics:GTL Program via the Shewanella Federation consortium and the Microbial Genome Program (DE-AC05-76RLO 1830).