Advanced probabilistic model building genetic algorithms and application to bioinformatics

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The complexity of real applications and/or the size of the data to be analyzed makes it difficult for exact solutions to problems to be found in reasonable computational time. This is especially true in bioinformatics. Biological models are extremely complex and the data generated by genome sequencing, proteomics, gene regulation, protein/protein interaction and now macro-biology (cell models, organism or ecosystem simulation, etc.) projects often grow exponentially. Providing efficient heuristics to analyze such problems is therefore very relevant.Dr. Gras propose a research program based on optimization strategies using probabilistically guided heuristics. It is a new domain of heuristic strategies that has emerged recently. It is based on powerful techniques from statistical analysis and combinatorial optimization. This is an innovative and promising approach as a major part of the bioinformatics problems can be represented as optimization problems. Modern metaheuristics, based on the building of a probabilistic model, have proven their potential efficiency to solve such optimization problems. Moreover, due to the construction of the probabilistic model, these methods can reveal very informative knowledge about the intrinsic structure of the data. Dr. Gras proposes to apply such approaches and conceive new ones, associated with the use of expert (biologist) knowledge to solve bioinformatics problems.As this program aims to develop tools to analyze biological data that are correlated to Alzheimer disease, cardiovascular disease, bovine spongiform encephalopathy and cancer, it can lead to a better understanding of these phenomena and to the discovery of new diagnostic techniques and even new therapies. This program also involves the analysis and then the establishment of a model and the simulation of ecological systems. That would be essential for the understanding of evolution of biological populations and of pollution effects on ecosystems.