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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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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.

  • A preliminary step is to be able to quantify the relevance of a grammar with respect to a structure. In [21], we introduced and assessed quantitative measures for comparing the topology of the parse tree of a protein sequence analyzed by a context-free grammar with the topology of the protein structure.

  • In [24], we established a new framework for learning probabilistic context-free grammars for protein sequences using predicted or experimentally assessed amino acid 3D contacts. We relied on maximum-likelihood and contrastive estimators of parameters in this setting and an implementation for simple yet practical grammars. Tested on samples of protein motifs, grammars developed within the framework showed improved precision in recognition and higher fidelity to protein 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.

  • Heterocyclic Aromatic Amines (HAAs) are environmental and food contaminants classified as probable carcinogens. Our approach based on a refinement of molecular predictions with enzyme activity scores allowed us to accurately predict HAAs biotransformation and their potential DNA reactive compounds [13].

  • We designed a prototype (Pathmodel) implementing inference methods to reconstruct biochemical reactions and metabolite structures to cope with metabolic pathway drift mechanisms. Using known metabolic pathways and metabolomics data, the tool infers alternative pathways compatible with the species known metabolites [29].

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.

  • In this direction, we developed AuReMe for a flexible and reproducible reconstruction of these models. Together with a convenient mean for exploration through a local wiki, AuReMe is well-suited for the study of non-model organisms [12].

  • In addition, a new gap-filling method satisfying the two main semantics of activation in metabolism is available. It enables to refine the models by pinpointing reactions such that metabolic objectives are met [15].

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].