Integration of genome-wide data to infer genetic networks

author: Xavier Gidrol, CEA/Laboratoire d’Exploration Fonctionnelle des Genomes
published: Nov. 20, 2007,   recorded: September 2007,   views: 4279

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To comprehend biology as a system, one needs to analyze the structure and dynamics of cell components as modules rather than isolated part. Progress in technological devices, analytical methods and biological models are required to decipher molecular networks and eventually analyze the cell as a system. Clustering analysis of gene expression profiles allows the analysis of “ correlation” between genes and biological conditions. However it is yet restrictive as it does not reveal the causality of regulatory relationships. Besides it is very difficult to infer molecular networks from expression profiling only, as the only accessible information is the steady-state concentration of mRNA. This information is necessary but not sufficient to characterize the structure of transcriptional network and analyze its dynamic and functional properties. Modeling of transcriptional networks should take into account information such as RNA concentrations, cis-acting sequences, transcriptional activity and so forth, since each variable carries unique biological information. However due to limitation in accurate and highly parallel measuring technologies, these data are not routinely accessible. We have developed innovative bioarrays to measure with sufficient accuracy, parallelism and throughput relevant data to infer transcriptional networks. For instance, we are manufacturing DNA array containing promoter regions (human) to perform ChIP on chip analysis in order to localize for a given transcription factor, all putative binding sites onto the genome. One can then return to DNA arrays to confirm hypothesis generated from ChIP on chip data. We are also developing cell microarrays to characterize, genome wide, upstream regulators for a given gene on one hand and transcriptional activity on the other hand. Technological breakthroughs in micro and nanotechnologies to generate comprehensive and relevant data are thus as critical as innovation in analytical methods for deciphering transcriptional regulatory networks and developing system biology.

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