Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Metabolism: from enzyme sequences to systems ecology

Participants : Meziane Aite, Arnaud Belcour, Marie Chevallier, Mael Conan, François Coste, Olivier Dameron, Clémence Frioux, Jeanne Got, Jacques Nicolas, Anne Siegel, Hugo Talibart.

Efficient identification of substitutable context-free grammars by reduction [F. Coste, J. Nicolas] To study more formally the approach by reduction initiated by ReGLiS [40], we introduced a formal characterization of the grammars in reduced normal form (RNF) which can be learned by this approach. Modifying the core of ReGLiS to ensure polynomial running time, we show that local substitutable languages represented by RNF context-free grammars are identifiable in polynomial time and thick data (IPTtD) from positive examples by reduction [19].

Learning grammars capturing 3D structural features of proteins [F. Coste, H. Talibart] With the team of Witold Dyrka in Polland, we investigated the problem of learning context-free grammars modeling well protein sequences with respect to their 3D structures.

Metabolic pathway inference from non genomic data [A. Belcour, M. Aite, J. Nicolas, A. Siegel, N. Théret, V. Dellannée, M. Conan] We designed methods for the identification of metabolic pathways for which enzyme information is not precise enough.

Large-scale eukaryotic metabolic network reconstruction [A. Siegel, M. Chevallier, C. Frioux, M. Aite, J. Cambefort] Metabolic network reconstruction has attained high standards but is still challenging for complex organisms such as eukaryotes.

Systems ecology: design of microbial consortia [C. Frioux, A. Siegel]. Finding key elements among hundreds or thousands in microbiota to explain metabolic behaviours or prepare biological experimentations is a highly combinatorial problem. We introduced a two-step approach, MiSCoTo to screen the metabolic capabilities of microbiotas and exhaustively select members of interest by solving optimization problems with logic programming. We applied these methods to data from the Human Microbiome Project and a system composed of the Human metabolic network and 773 models for gut bacteria [14], [11].