Modeling gene expression networks using fuzzy logic.

Pan Du; Jian Gong; Eve Syrkin Wurtele; Julie A Dickerson

doi:10.1109/tsmcb.2005.855590

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Modeling gene expression networks using fuzzy logic.

Pan Du·Jian Gong·Eve Syrkin Wurtele·Julie A Dickerson

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Abstract

Gene regulatory networks model regulation in living organisms. Fuzzy logic can effectively model gene regulation and interaction to accurately reflect the underlying biology. A new multiscale fuzzy clustering method allows genes to interact between regulatory pathways and across different conditions at different levels of detail. Fuzzy cluster centers can be used to quickly discover causal relationships between groups of coregulated genes. Fuzzy measures weight expert knowledge and help quantify uncertainty about the functions of genes using annotations and the gene ontology database to confirm some of the interactions. The method is illustrated using gene expression data from an experiment on carbohydrate metabolism in the model plant Arabidopsis thaliana. Key gene regulatory relationships were evaluated using information from the gene ontology database. A new regulatory relationship concerning trehalose regulation of carbohydrate metabolism was also discovered in the extracted network.

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Animals Arabidopsis Arabidopsis Proteins Carbohydrate Metabolism Computer Simulation Fuzzy Logic Gene Expression Regulation Humans Models, Biological Models, Statistical Oligonucleotide Array Sequence Analysis Signal Transduction Transcription Factors Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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Pan Du, Jian Gong, Eve Syrkin Wurtele, Julie A Dickerson. 2005. Modeling gene expression networks using fuzzy logic.. https://doi.org/10.1109/tsmcb.2005.855590

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