A bayesian network driven approach to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae

Jingchun Zhu, Ashwini Jambhekar, Aaron Sraver, Joseph DeRisi

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YH81 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions.

Original languageEnglish (US)
Article numbere94
JournalPloS one
Volume1
Issue number1
DOIs
StatePublished - Dec 20 2006

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