title: Identifying genes of gene regulatory networks using formal concept analysis.
abstract: In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g., in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application, our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.
authors.affiliation: Center for Applied Computer Science, University of Cologne, Cologne, Germany.
authors.forename: Jutta
authors.initials: J
authors.lastname: Gebert
authors.forename: Susanne
authors.initials: S
authors.lastname: Motameny
authors.forename: Ulrich
authors.initials: U
authors.lastname: Faigle
authors.forename: Christian V
authors.initials: CV
authors.lastname: Forst
authors.forename: Rainer
authors.initials: R
authors.lastname: Schrader
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D004260:DNA Repair; D045643:DNA Repair Enzymes / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks / Q000235:genetics*; D015233:Models, Statistical; D009169:Mycobacterium tuberculosis / Q000235:genetics / Q000378:metabolism; D017422:Sequence Analysis, DNA
chemical_list: D001426:Bacterial Proteins; D045643:DNA Repair Enzymes