|In Silico Genetics - CNRS URA 2171|
|HEAD||Dr VERGASSOLA Massimo / email@example.com|
|MEMBERS||BAILLY-BECHET Marc / Dr BOTTANI Samuel / Dr CELANI Antonio
FOUQUIER d’HEROUEL Aymeric / Dr SEKOWSKA Agnieszka
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.
|Publications 2006 of the unit on Pasteur's references database|
Activity Reports 2006 - Institut Pasteur
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