Dataset Information

Machine learning based analyses on metabolic networks supports high-throughput knockout screens.

ABSTRACT:

Background

Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics.

Results

This paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a comprehensive experimental dataset, which consists of the phenotypic outcomes from single knockout mutants of Escherichia coli (KEIO collection). We yielded very reliable results with high accuracy (93%) and precision (90%). We show that topologic, genomic and transcriptomic features describing the network are sufficient for defining the essentiality of a reaction. These features do not substantially depend on specific media conditions and enabled us to apply our approach also for less specific media conditions, like the lysogeny broth rich medium.

Conclusion

Our analysis is feasible to validate experimental knockout data of high throughput screens, can be used to improve flux balance analyses and supports experimental knockout screens to define drug targets.

SUBMITTER: Plaimas K

PROVIDER: S-EPMC2526078 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Publications

Machine learning based analyses on metabolic networks supports high-throughput knockout screens.

Plaimas Kitiporn K Mallm Jan-Phillip JP Oswald Marcus M Svara Fabian F Sourjik Victor V Eils Roland R König Rainer R

BMC systems biology 20080724

<h4>Background</h4>Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics.<h4>Results</h4>This paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a com ...[more]

PMID: 18652654

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Dataset Information

Machine learning based analyses on metabolic networks supports high-throughput knockout screens.

Background

Results

Conclusion

Publications

Machine learning based analyses on metabolic networks supports high-throughput knockout screens.

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