Our group investigates the extent to which quantitative modeling is relevant to the understanding of living matter. We tackle this general issue by focusing on specific examples, analyzed by a combination of analytical methods, computational tools and small-scale experiments.

Moths responding to pheromones provide a striking example of living organisms performing the search of a source based on sporadic cues and partial information. Similar problems arise 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 gradient-climbing chemotactic strategies inspired by micro-organisms. Typical physical conditions for sniffers and insects are however different from those of micro-organisms, which severely limits the applicability of chemotaxis. A new method, infotaxis, was introduced and shown to yield zigzagging and casting trajectories very similar to those of insects and to be able to cope with harsh conditions of sporadic cues experienced by insects. The method also lends itself to collective searches, e.g. featuring a swarm of robots. Applications to individual and swarms of robotic sniffers are currently developed. The study of cognitive strategies such as infotaxis for real insects was also recently undertaken.

We have recently proposed a new functional cause for adaptation (or lack thereof) in bacterial chemotaxis. Contrary to arguments focusing on the extension of the dynamic range of response, we have shown that adaptation can be driven by space-time fluctuations in the environment. The mechanism we propose makes closer contact with the environmental conditions experienced by bacteria in the wild; furthermore, it leads to predictions testable in the laboratory, which we are currently pursuing. To that purpose, we analyze the evolution with the environment of the E. coli chemotactic response, as extracted from in vivo images of swimming bacteria using a novel inference method that we are developing.

As for eukaryotic chemotaxis, we have started working on the experimental tracking of D3-phosphoinositide PIP3 patches in Dictyostelium discoideumin order to quantify their space-time dynamics on the cell membrane. The motivation stems from models for directional sensing that we have developed, which predict very similar responses to static gradients yet strongly differ in their kinetics of polarization.

Finally, we develop and apply 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 curves are compared to analytical behaviors expected for different modes of motion, e.g. free Brownian diffusion, directed, confined or anomalous motion. We recently demonstrated 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. We are pursuing the application of similar methods to conditions and situations of interest for neurobiology.

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

Celani A. & Vergassola M., Bacterial strategies for chemotaxis response. Proceed. Nat. Acad. Sciences, 107, 1391-1396, 2010.

Chertkov M., Kroc L., Krzakala F., Vergassola M. & Zdeborova L., Inference in particle tracking experiments by passing messages between images. Proceed. Nat. Acad. Sciences, 107, 7663-7668, 2010

J.-B. Masson, D. Casanova, S. Turkcan, G. Voisinne, M. R. Popoff, M. Vergassola & A. Alexandrou 2009 Inferring maps of forces inside cell membrane microdomains. Physical Review Letters,102(4):048103. Featured in Virtual Journal of Biol. Phys. Research – Feb. 1, 2009, Volume 17, Issue 3; Virtual Journal of Nanoscale Science & Technology – Feb. 9, 2009, Volume 19, issue 6.

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

P. Mandin*, F. Repoila*, M. Vergassola*, T. Geissmann & P. Cossart 2007 Identification of new noncoding RNAs in Listeria monocytogenes and prediction of mRNA targets. Nucleic Acids Research, 35: 962-74 (*Equal contributions).