Inference of Genetic Interactions in Large Scale Genetic Network

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The expression profiles of hundreds and thousands of genes on a genomic scale can be measured simultaneously by recent powerful technologies such as DNA microarrays, DNA chips and so forth. These observed data depending on its environment are usually obtained as snapshots, but can be generated as dense time series that indicate the dynamic behavior. The experimentally observed time-course data should contain enormous information about the regulation of genetic networks in vivo. However, since this information is entirely implicit, it requires adequate analytical and computational methods of retrieval and interpretation. This inference problem of genetic networks by using the experimentally observed time-course data is generally referred to as "inverse problem" and can be defined as function optimization of the values of parameters involved in a suitable model representation of genetic network. The key points to solve such an inverse problem are how to set up canonical representation of … More mathematical modeling of genetic network and how to explore and exploit the values of parameters within immense huge searching space, we had first proposed a novel inferring method of genetic network by combining a dynamic network model called S-system with a computational technique of parameter estimation based on real-coded genetic algorithms (RCGAs). Using S-system modeling and RCGAs with the combination of the UNDX (unimodal normal distribution crossover) and MGG (minmal generation gap), we proposed efficient procedures for the inference of genetic interactions from the experimentally observed time-course data of system components (mRNA). By improving the searching algorithm and by introducing server-client system, we have developed the novel inferring system which can be finding a lots of possibly network candidates that can realize the given experimentally observed time-course data. All of these network candidates can realize the same experimentally observed facts, however, the structures of genetic interactions are different each other. Therefore, we have proposed the analytical method for extracting useful information from many network candidates of. gene expression. In S-system model, the sign of interrelated coefficient shows the kind of interactions such as activation, inhibition, or no relation. The common core interactions are defined by the interactions with sign of which are same among all network candidates of gene expression which inferred based on the same experimentally observed time-course data under the same parameter optimizing conditions. We calculated sensitivity for each interaction included in the network candidates, and compared sensitivity of common core interactions with that of other unique interactions. Less