In Silico Genetics - CNRS URA2171  


  HEADMassimo Vergassola / massimo@pasteur.fr
  MEMBERSMarc Bailly-Bechet , Ph.D. student, Defended his Ph.D. in July 2007 and he is now Post-Doc at Politecnico, Turin
Samuel Bottani, Maître de Conférences Université Paris VII En détachement to CNRS for 2007-2008
Antonio Celani Chargé de Recherche 1st class CNRS / Aymeric Fouquier d’Herouel Ph.D. student
Jean-Baptiste Masson Post-Doc / Agnieszka Sekowska Post-Doc / Massimo Vergassola Directeur de Recherche CNRS
Guillaume Voisinne Ph.D. Student, Paris VI


  Annual Report

Our group investigates the extent to which quantitative modeling is relevant to the understanding of living matter. We tackle this general issue focusing on specific examples, analyzed by a combination of analytical and computational tools, in close collaboration with experimental groups.

1. In silico methods for translation regulation

1.1 Codon bias To analyze biological causes of biases in codon usage, we have developed a novel clustering method, specifically designed to avoid limitations of general-purpose methods, e.g. on the arbitrariness in the choice of the number of clusters. Applications to E. coli and B. subtilis show that correlations in codon usage are more extended than what could be accounted by operons, i.e. genes with similar usages tend to be close on the chromosomes. A contribution to those correlations might come from selective pressure acting at the translation level on rare codons.

1.2 Non-coding RNAs Bioinformatics screens have led us to the identification of 9 novel ncRNAs in the bacterial pathogen Listeria monocytogenes, confirmed by Northern blots and 5’ RACE experiments. We further developed a new computational method for the prediction of messenger RNA targets of ncRNAs. Its motivation is the presence of bulges and internal loops that prevents using standard alignment methods, e.g. BLAST. The energetic scoring system that we have employed allows dealing with genomes with high AT content, e.g. > 60% in Listeria. Predictions have been successfully confirmed experimentally.

2. Motility in living organisms

Moths responding to pheromones provide a classical example of living organism performing a search exploiting only sporadic cues and partial information. The same problem arises also in the design of sniffers, i.e. robots that track chemicals emitted by drugs, chemical leaks, explosives and mines. Existing search strategies for sniffers mostly mimic bacteria, employing search strategies inspired to chemotaxis. Typical physical conditions for sniffers and insects are however quite different from those of bacteria. We have developed a novel search strategy and demonstrated its superiority to existing methods by numerical experiments. The method, dubbed infotaxis, is based on the idea that the rate of acquisition of information on the location of the source plays the same role as concentration in chemotaxis. Infotaxis strategies of motion locally maximize the expected rate of information gain.

Keywords: Individual and Collective Motility of Biological Systems; Bacterial Colonies; Chemotaxis; Computational Biology; Modelling



  Publications

Infotaxis as a strategy for searching without gradients.M. Vergassola, E. Villermaux & B.I. Shraiman Nature, 445, 406-409, 2007.

Causes for the intriguing presence of tRNAs in phages.M. Bailly-Bechet, M. Vergassola & EPC Rocha 2007 Genome Research, 17, 1486-1495, 2007.

Identification of new noncoding RNAs in Listeria monocytogenes and prediction of mRNA targets. P. Mandin*, F. Repoila*, M. Vergassola*, T. Geissmann & P. Cossart Nucleic Acids Research,35:962-742007 (*Equal contributions).

Highly variable rates of genome rearrangements between hemiascomycetous yeast lineages.G. Fischer, EPC Rocha, F. Brunet, M. Vergassola & B. Dujon PLoS Genetics2 (3):253-261, 2006.

Codon usage domains over bacterial chromosomes. M. Bailly-Bechet, A. Danchin, M. Iqbal, M. Marsili & M. Vergassola PLoS Computational Biology2 (4):263-275, 2006.





Activity Reports 2007 - Institut Pasteur
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