From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases.
Understanding diseases requires identifying the differences between healthy and affected tissues. Gene expression data have revolutionized the study of diseases by making it possible to simultaneously consider thousands of genes. The identification of disease-associated genes requires studying the genes in the context of the regulatory systems they are involved in. A major goal is to identify specific regulatory networks that are dysfunctional in a given disease state. Although we still have not reached a stage where the elucidation of differential regulatory networks is commonly feasible, recent advances have described the first steps towards this goal - the identification of differential coexpression networks. This review describes the shift from differential gene expression to differential networking and outlines how this shift will affect the study of the genetic basis of disease.
de la Fuente
Alberto
A
CRS4 Bioinformatica, Polaris Edificio 3, Località Piscina Manna, 09010 Pula (CA), Italy. alf@crs4.it
D000818:Animals; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic