Modeling regulatory networks with weight matrices

D.C. Weaver, Christopher Workman, Gary D. Stormo

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    Systematic gene expression analyses provide comprehensive information about the transcriptional responseto different environmental and developmental conditions. With enough gene expression data points,computational biologists may eventually generate predictive computer models of transcription regulation.Such models will require computational methodologies consistent with the behavior of known biologicalsystems that remain tractable. We represent regulatory relationships between genes as linear coefficients orweights, with the "net" regulation influence on a gene's expression being the mathematical summation of theindependent regulatory inputs. Test regulatory networks generated with this approach display stable andcyclically stable gene expression levels, consistent with known biological systems. We include variables tomodel the effect of environmental conditions on transcription regulation and observed various alterations ingene expression patterns in response to environmental input. Finally, we use a derivation of this modelsystem to predict the regulatory network from simulated input/output data sets and find that it accuratelypredicts all components of the model, even with noisy expression data.
    Original languageEnglish
    Title of host publicationPacific Symposium on Biocomputing '99, Proceedings
    Place of PublicationRiver Edge
    PublisherWorld Scientific
    Publication date1999
    Publication statusPublished - 1999
    EventPacific Symposium on Biocomputing, January 4 - 9 - Mauna Lani, Hawaii
    Duration: 1 Jan 1999 → …


    ConferencePacific Symposium on Biocomputing, January 4 - 9
    CityMauna Lani, Hawaii
    Period01/01/1999 → …

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