Generating Data form the Evolution of Artificial Regulatory Networks - Archive ouverte HAL
Communication Dans Un Congrès Année : 2007

Generating Data form the Evolution of Artificial Regulatory Networks

Résumé

Existing regulatory network models attempt to copy the ``in vivo" regulatory principles by reproducing founded biological results ``in silico". These models sometimes don't reflect the biological principal of protein regulation and they don't take into account the organisms evolution. An innovative approach is presented on this contribution, based on the analyze of the existing models. Biological principles of regulatory networks have been considered. This new model can provide tools to study regulatory networks emergence and evolution and to acquire knowledge from generated time series. Studying networks in silico provide us the tools for controlling the environment and a better behavior analysis.
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Dates et versions

hal-01613779 , version 1 (10-10-2017)

Identifiants

  • HAL Id : hal-01613779 , version 1

Citer

Yolanda Sanchez-Dehesa, Jose Maria Pena, Guillaume Beslon. Generating Data form the Evolution of Artificial Regulatory Networks. Workshop Data Mining in Functional Genomics and Proteomics. Current Trends and Future Directions co-located with ECML/PKDD 07, Sep 2007, Varsovie, Poland. pp.91-103. ⟨hal-01613779⟩
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