An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models.
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 interacti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2010-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC2965739?pdf=render |
| _version_ | 1811338108959981568 |
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| author | Dipak Barua Joonhoon Kim Jennifer L Reed |
| author_facet | Dipak Barua Joonhoon Kim Jennifer L Reed |
| author_sort | Dipak Barua |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:05:54Z |
| format | Article |
| id | doaj.art-df70c41135954f9181e68878e0ce7bb3 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T18:05:54Z |
| publishDate | 2010-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-df70c41135954f9181e68878e0ce7bb32022-12-22T02:36:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-01-01610e100097010.1371/journal.pcbi.1000970An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models.Dipak BaruaJoonhoon KimJennifer L ReedIntegrated 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.http://europepmc.org/articles/PMC2965739?pdf=render |
| spellingShingle | Dipak Barua Joonhoon Kim Jennifer L Reed An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Computational Biology |
| title | An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. |
| title_full | An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. |
| title_fullStr | An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. |
| title_full_unstemmed | An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. |
| title_short | An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. |
| title_sort | automated phenotype driven approach geneforce for refining metabolic and regulatory models |
| url | http://europepmc.org/articles/PMC2965739?pdf=render |
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