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


CONCLUSIONS
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.
Plaimas
Kitiporn
K
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. k.plaimas@dkfz.de
Mallm
Jan-Phillip
JP
Oswald
Marcus
M
Svara
Fabian
F
Sourjik
Victor
V
Eils
Roland
R
König
Rainer
R
D000465:Algorithms; D001185:Artificial Intelligence; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D017353:Gene Deletion; D023281:Genomics; D053858:Metabolic Networks and Pathways; D004364:Pharmaceutical Preparations / Q000378:metabolism; D012343:RNA, Transfer / Q000378:metabolism; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D004364:Pharmaceutical Preparations; D012343:RNA, Transfer