title: A systems-biology approach to modular genetic complexity.
abstract: Multiple high-throughput genetic interaction studies have provided substantial evidence of modularity in genetic interaction networks. However, the correspondence between these network modules and specific pathways of information flow is often ambiguous. Genetic interaction and molecular interaction analyses have not generated large-scale maps comprising multiple clearly delineated linear pathways. We seek to clarify the situation by discerning the difference between genetic modules and classical pathways. We review a method to optimize the discovery of biologically meaningful genetic modules based on a previously described context-dependent information measure to obtain maximally informative networks. We compare the results of this method with the established measures of network clustering and find that it balances global and local clustering information in networks. We further discuss the consequences for genetic interaction networks and propose a framework for the analysis of genetic modularity.
authors.affiliation: Institute for Systems Biology, 1441 North 34th Street, Seattle, Washington 98103, USA.
authors.forename: Gregory W
authors.initials: GW
authors.lastname: Carter
authors.forename: Cynthia G
authors.initials: CG
authors.lastname: Rush
authors.forename: Filiz
authors.initials: F
authors.lastname: Uygun
authors.forename: Nikita A
authors.initials: NA
authors.lastname: Sakhanenko
authors.forename: David J
authors.initials: DJ
authors.lastname: Galas
authors.forename: Timothy
authors.initials: T
authors.lastname: Galitski
mesh_terms: D016000:Cluster Analysis; D004843:Epistasis, Genetic; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D049490:Systems Biology