Prediction of phenotype and gene expression for combinations of mutations.
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.
Carter
Gregory W
GW
Institute for Systems Biology, Seattle, WA 98103, USA. gcarter@systemsbiology.org
Prinz
Susanne
S
Neou
Christine
C
Shelby
J Patrick
JP
Marzolf
Bruz
B
Thorsson
Vesteinn
V
Galitski
Timothy
T
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
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors