|In Silico Genetics - CNRS URA2171|
|HEAD||Massimo Vergassola / firstname.lastname@example.org|
|MEMBERS||Antonio Celani Directeur de Recherche CNRS / Aymeric Fouquier d’Herouel Ph.D. student. / Jean-Baptiste Masson Chargé de Recherche Institut Pasteur / Agnese Seminara Post-Doc Outgoing Fellowship UE / Massimo Vergassola Directeur de Recherche CNRS / Guillaume Voisinne Ph.D. Student, Paris VI / Jerome Wong Post-Doc ANR PNANO
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 methods, computational tools and small-scale experiments.
1. Motility in macro-organisms
Moths responding to pheromones provide a classical example of macro-organism performing the search of a source based only on sporadic cues and partial information. The same problem arises in the design of sniffers, i.e. robots that track chemicals emitted by drugs, chemical leaks, explosives and mines. Existing search strategies for sniffers mimic micro-organisms and their chemotactic strategies of motility. Typical physical conditions for sniffers and insects are however quite different from those of micro-organisms. A new method, infotaxis, was introduced and shown to yield zigzagging and casting trajectories very similar to those of insects and birds. Infotaxis locally maximizes the expected rate of gain of information on the location of the source The strategy was recently extended to collective searches featuring a swarm of robots. Gains on the search times are impressive and non-trivial interactions within the swarm arise from information sharing.
2. Motility in micro-organisms
We analyze the chemotactic response of E. coliand its diversity within colonies. The response is extracted from in vivo images of swimming bacteria using a novel inference method that we are developing. We model the chemotactic response and the constraints that shape it. We also investigate the chemotactic behavior of E. coli in complex media featuring obstacles and surfaces.
3. Biological inferences
In addition to applications to bacterial chemotaxis, we are developing inference methods to analyze the motion of proteins and lipids (labeled by inorganic nanoparticles or organic phluorophore) in cell membranes. Commonly, the mean-square displacement (MSD) curves are compared to analytical behaviors expected for different modes of motion, e.g. free Brownian diffusion, directed, confined or anomalous motion. We demonstrate that the use of inference methods, exploiting all the information hidden in the trajectories and not just the second-order moment, proves particularly fruitful and allows to measure much more detailed and specific maps of the forces acting within membrane microdomains and get precious clues about the physical mechanisms leading to membrane compartmentation.
Keywords: Individual and Collective Motility of Biological Systems, Chemotaxis, Modelling, Computational Biology
Message-passing algorithms for the prediction of protein domain interactions from protein–protein interaction data. M. Iqbal, A.A. Freitas, C.G. Johnson & M. Vergassola Bioinformatics24(18):2064-70, 2008.
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
Highly variable rates of genome rearrangements between hemiascomycetous yeast lineages. Fischer G, Rocha EP, Brunet F, Vergassola M, Dujon B. PLoS Genet. 2(3):e32 2006.
Activity Reports 2009 - Institut Pasteur
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