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Scientific Axes |
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Team leader: Jacques NICOLAS
(A few slides presenting Symbiose are available here ) The Scientific axes on which the project focuses derive from our choice of modelling complex biological systems in a discrete framework, while managing efficiency issues. More precisely, the project links together three main directions of research:
This track concerns the search for relevant (e. g. functional) spatial or logical structures in macromolecules, either with intent to model specific spatial structures (secondary and tertiary structures, disulfide bounds ... ) or general biological mechanisms (transposition ... ). In the framework of language theory and combinatorial optimization, we try to answer four types of problems: the design of grammatical models on biological sequences; efficient filtering and model matching in data banks; protein structure prediction; and machine learning of grammatical models from sequences. Corresponding disciplinary fields are algorithmic on words, machine learning, data analysis and combinatorial optimization. The ultimate goal, for the biologist, is to explain how the combination of genetic and metabolic interactions determines the phenotype which is observed at the molecular level, particularly in case of diseases. The scarcity of quantitative data on biological phenomena implies the use of qualitative models. Our approach is based on the definition of graph models of biological networks and the derivation of discrete or differential models for explaining and predicting (in a broad meaning) the behavior of the biological system. A special attention is paid to the diagnosis of large scale models described by their interaction graph. We investigate the practical usage of parallelism to speed up computations in genomics. Topics of interest range in intensive sequence comparisons to pattern or model matching, including structure prediction. We work on the codesign of algorithms and hardware architectures tailored to the treatment of such applications. It is based on the study of reconfigurable machines employing Field Programmable Logical Arrays (FPGA)or fast components such as flash memories or Graphical Processing Units.
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