title: Prediction of phenotype and gene expression for combinations of mutations.
abstract: Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified.
authors: Institute for Systems Biology, Seattle, WA 98103, USA. gcarter@systemsbiology.org
authors: Gregory W
authors: GW
authors: Carter
authors: Susanne
authors: S
authors: Prinz
authors: Christine
authors: C
authors: Neou
authors: J Patrick
authors: JP
authors: Shelby
authors: Bruz
authors: B
authors: Marzolf
authors: Vesteinn
authors: V
authors: Thorsson
authors: Timothy
authors: T
authors: Galitski
mesh_terms: D000465:Algorithms; D000483:Alleles; D004843:Epistasis, Genetic; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D018740:Genetic Heterogeneity; D005838:Genotype; D016018:Least-Squares Analysis; D008957:Models, Genetic; D009154:Mutation; D010641:Phenotype; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000254:growth & development / Q000648:ultrastructure; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000502:physiology; D014157:Transcription Factors / Q000235:genetics
chemical_list: D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors