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

Kitiporn Plaimas, Jan-Phillip Mallm, Marcus Oswald, Fabian Svara, Victor Sourjik, Roland Eils, Rainer Konig
2008 BMC Systems Biology  
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
more » ... ataset, 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.
doi:10.1186/1752-0509-2-67 pmid:18652654 pmcid:PMC2526078 fatcat:qjnttyoz6jdhngd5535iezh5he