Modeling gene expression networks using fuzzy logic.
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.
Du
Pan
P
Virtual Reality Applications Center, Iowa State University, Ames 50011-3060, USA.
Gong
Jian
J
Syrkin Wurtele
Eve
E
Dickerson
Julie A
JA
D000818:Animals; D017360:Arabidopsis / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000378:metabolism; D050260:Carbohydrate Metabolism / Q000502:physiology; D003198:Computer Simulation; D017143:Fuzzy Logic; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*
D029681:Arabidopsis Proteins; D014157:Transcription Factors