A computational method designed to extract information about gene regulatory network from raw gene expression data sets that are comprised of a time cour se of expression levels is disclosed. At a first step in this method, genes wit h similar temporal expression profiles are clustered into modules characterizi ng by distinct expression signatures. These fundamental patterns of gene expression are analyzed using the assumption that temporal profiles are shap ed by interactions between genes belonging to different modules. The underlying genetic connectivity is retrieved using an optimization procedure developed in computational neurobiology for extracting information about neural circuitry . The objective is to find an optimal regulatory structure making calculated temporal patterns as close as possible to experimental data. A set of algorithms was used to evaluate statistical significance of putative regulatory connections derived from gene expression patterns. The method was utilized to identify regulatory subnetworks underlying the response of yeast cells to treatment with acid and alkaline conditions. Expression profiles of about 1600 genes that showed a significant change in expression during a tim e course were analyzed according to the method of the invention. The genes wer e clustered into 39 distinct modules and statistically significant connections between 16 modules representing most variable genes were identified and mapp ed to a sub-network of known connections. The results demonstrate that the computational method may be a useful tool both in elucidating of crucial elements of genetic network structure and in predicting novel regulatory connections based on gene expression.
COMPUTATIONAL METHOD FOR INFERRING ELEMENTS OF GENE REGULATORY NETWORK FROM TEMPORAL PATTERNS OF GENE EXPRESSION