title: Identifying essential genes in bacterial metabolic networks with machine learning methods.
abstract: BACKGROUND
  Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.
  
  
  RESULTS
  We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.
  
  
  CONCLUSIONS
  Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.
authors.affiliation: Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
authors.forename: Kitiporn
authors.initials: K
authors.lastname: Plaimas
authors.forename: Roland
authors.initials: R
authors.lastname: Eils
authors.forename: Rainer
authors.initials: R
authors.lastname: König
mesh_terms: D001185:Artificial Intelligence; D004926:Escherichia coli / Q000378:metabolism; D005227:Fatty Acids / Q000378:metabolism; D020869:Gene Expression Profiling; D015964:Gene Expression Regulation, Bacterial; D005798:Genes, Bacterial; D020043:Genes, Essential; D016680:Genome, Bacterial; D023281:Genomics; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D011550:Pseudomonas aeruginosa / Q000378:metabolism; D012372:ROC Curve; D012486:Salmonella typhimurium / Q000235:genetics; D049490:Systems Biology
chemical_list: D005227:Fatty Acids