M2 - Recherche de graines dans les réseaux métaboliques

Type de poste
Niveau d'étude minimal
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Nom de la structure d'accueil

200 Avenue de la Vieille Tour
33405 Talence

Clémence Frioux
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Metabolic networks are graph-based objects that comprise biochemical reactions which transform substrates into products. Genome-scale metabolic networks (GSMN) gather all reactions that an organism could catalyze according to the contents of its genome. They permit the modelling of the phenotype of an organism according to its genotype. While obtaining a GSMN for a species was a long run project a few years ago, it is now possible to systematically build good quality drafts thanks to automatic methods. The reconstructed GSMNs are then the basis of mathematical models aiming at representing the behavior of an organism’s metabolism in given conditions. The latter conditions are generally a set of nutrients that can activate metabolic reactions in a domino effect. Given these nutrients, or "seeds", one can use algorithms [1] and identify the reactions that can be activated in given conditions and the metabolites that are expected to be producible. The reverse problem is the subject of this internship. It consists in proposing seeds that would be sufficient to trigger a desired activation in the metabolic network [2]. A commonly used method is to rely on the graph structure of the network to identify seeds [3,4]. This method has been widely used but does not comply with the network expansion constraints that we use to model reaction activation. An algorithm based on a modified version of network expansion has also been proposed [5]. Here we want to rely on the original network expansion algorithm, implemented in logic programming _ Answer Set Programming ASP [6] _ [7] to develop and validate such search for seeds in GSMNs. We have a basis for the implementation and need a motivated intern to handle the project by enhancing the ASP code, ensuring a good-quality python embedding (continuous integration, tests…), and build a benchmark to test and compare the new methods to others. Python development skills are required. ASP and logic programming skills are a bonus but can also be acquired during the internship.

For additional information do not hesitate to get in touch with the team.

1. Ebenhöh, O., Handorf, T. & Heinrich, R. Structural analysis of expanding metabolic networks. Genome Informatics Int Conf Genome Informatics 15, 35–45 (2004).
2. Handorf, T., Christian, N., Ebenhöh, O. & Kahn, D. An environmental perspective on metabolism. J Theor Biol 252, 530–537 (2008).
3. Carr, R. & Borenstein, E. NetSeed: a network-based reverse-ecology tool for calculating the metabolic interface of an organism with its environment. Bioinformatics 28, 734–735 (2012).
4. Borenstein, E. & Feldman, M. W. Topological Signatures of Species Interactions in Metabolic Networks. J Comput Biol 16, 191–200 (2009).
5. Cottret, L. et al. Lecture Notes in Computer Science. 233–244 (2008) doi:10.1007/978-3-540-87361-7_20.
6. Gebser, M., Kaminski, R., Kaufmann, B. & Schaub, T. Answer Set Solving in Practice. Synthesis lectures on artificial intelligence and machine learning (2012).
7. Schaub, T. & Thiele, S. Lecture Notes in Computer Science. 312–326 (2009) doi:10.1007/978-3-642-02846-5_27.

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