title: Revising regulatory networks: from expression data to linear causal models.
abstract: Discovering the complex regulatory networks that govern mRNA expression is an important but difficult problem. Many current approaches use only expression data from microarrays to infer the likely network structure. However, this ignores much existing knowledge because for a given organism and system under study, a biologist may already have a partial model of gene regulation. We propose a method for revising and improving these initial models, which may be incomplete or partially incorrect, with expression data. We demonstrate our approach by revising a model of photosynthesis regulation proposed by a biologist for Cyanobacteria. Applied to wild type expression data, our system suggested several modifications consistent with biological knowledge. Applied to a mutant strain, our system correctly modified the disabled gene. Power experiments with synthetic data that indicate that reliable revision is feasible even with a small number of samples.
authors.affiliation: Institute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306, USA. sbay@apres.stanford.edu
authors.forename: S D
authors.initials: SD
authors.lastname: Bay
authors.forename: J
authors.initials: J
authors.lastname: Shrager
authors.forename: A
authors.initials: A
authors.lastname: Pohorille
authors.forename: P
authors.initials: P
authors.lastname: Langley
mesh_terms: D020869:Gene Expression Profiling; D016014:Linear Models; D008957:Models, Genetic; D009415:Nerve Net; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000235:genetics*
chemical_list: D012333:RNA, Messenger