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SYMBIOSE Research team

Biological systems and models, bioinformatics and sequences

  • Leader : Dominique Lavenier
  • Research center(s) : CRI Rennes - Bretagne Atlantique
  • Field : Computational Sciences for Biology, Medicine and the Environment
  • Theme : Computational Biology and Bioinformatics
  • Partner(s) : CNRS,Université Rennes 1

Team presentation

Symbiose is a bioinformatics project. Our research specificities include our interest in large scale studies (genomes, proteomes or regulation networks) and discrete methods necessary to handle the associated complexity. Our methods relate on discrete optimization, analysis of systems of qualitative equations and formal language modeling. Our goal is to push forward their range of applicability by exploring the impact of specialized machines or algorithms.

Research themes

We have a global concern for high performance computing and two types of modeling tasks:
  • Optimized algorithms on parallel specialized architectures First and foremost, large scale studies need a fine tuning and management of computational resources. We investigate the practical usage of parallelism to speed up computations in genomics. Topics of interest range from intensive sequence comparisons to pattern or model matching, including structure prediction. We work on the co design 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.
  • Modeling sequence/structure relationships 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 address various 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.
  • Systems biology: network modeling and analysis 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 interaction networks and the derivation of discrete or differential models for explaining and predicting (in a broad meaning) the behavior of the observed biological system. A special attention is paid to the diagnosis of large scale models described by their interaction graph.

International and industrial relations

The main international teams we cooperate with are the following
  • Argentina, Universidad Nacional de Córdoba: Grammatical inference
  • Bulgaria, IPP and Sofia University: Protein structures
  • China, Institute of Computing Technology, Beijing: Parallelization of bioinformatics algorithms onto multicore processors
  • Germany, Postdam university: Logic programming and boolean constraint solving.
  • Greece, Institute of Communication and Computer Systems, National Technical Univ. of Athens: Oncosimulator.
  • US, Stony Brook University: Drosophila developmental biology
  • India, NCBS Bangalore: systems biology, biophysics

    Keywords: Bio-informatics Genomics HPC Sequence analysis Structure prediction Regulation networks