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portrait Olivier MartinOlivier MARTIN


Génétique Quantitative et Évolution - Le Moulon
INRA - Université Paris-Sud - CNRS - AgroParisTech
Ferme du Moulon
F-91190 Gif-sur-Yvette
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(33) 1 69 33 23 36
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> Education and Positions

Currently Head of the RAMDAM team, affiliated with the Labex Saclay Plant Sciences

Currently Head of the research unit Génétique Quantitative et Évolution - Le Moulon

Since 2013: Directeur de Recherche (INRA)

Full Professor of Theoretical Physics (1991-2013) Université Paris-Sud, Orsay, France

Assistant Professor of Theoretical Physics (1987-1991) CUNY, NY, USA

Postdoc in Theoretical Physics (1985-1987) University of Illinois, Urbana, USA

Postdoc in Theoretical Physics (1983-1985) Columbia University, NY, USA

Ph.D. in Theoretical Physics (1983), California Institute of Technology, USA


Research interests: Quantitative biology, meiosis modeling, intracellular molecular networks

2 pathways for crossover formationAt the heart of my research on meiosis modeling is genetic interference, the phenomenon whereby crossovers rarely arise near one another. Although interference was discovered in 1916 and seems to arise in the great majority of organisms undergoing sexual reproduction, its role – physiological or evolutionary – is not understood. Furthermore the mechanistic roots of interference are completely unknown. In the last decade it has been discovered that crossovers form via two pathways, one of which is interfering while the other is not.
With Matthieu Falque, I have been characterizing these pathways in different organisms. For instance in tomato we discovered that even though the second pathway is not self-interfering, it interferes with the first pathway out to distances of about 6 microns. We also have been working on developing tools to better exploit segregation data and genotyping arrays.

intraction network in A. thaliana





My research on intracellular networks considers the relation between structure and function and how such properties are shaped by evolution. These questions are tackled for gene regulatory networks, metabolic networks and also signaling networks.
The associated computational studies often rely on Markov Chain Monte Carlo, a very versatile tool for sampling constrained high-dimensional spaces, as well as tools from mathematics (dynamical systems theory, statistics).




> Current Projects

(1) Quantifying across successive generations how linkage slows down genetic progress and how to best use new technologies that enhance recombination rates.

(2) Demonstrate the usefulness of in silico exploration of biological networks to probe their operating principles.


> Selected publications

Full list available on Google scholar


- G.K. Sidhu, C. Fang, M.A. Olson, M. Falque, O.C. Martin, and W.P. Pawlowski (2015) Recombination patterns in maize reveal limits to crossover homeostasis, PNAS 1514265112 .

- A. Samal, O.C. Martin (2015) Statistical physics methods provide the exact solution to a long-standing problem of genetics, Phys. Rev. Lett. 114, 238101. (Supplemental Material and C source code.) Highlighted as a "Editors' choice", received press coverage in Physics Today and was in the news of several institutions (ICTP, Italy, and INRA, both for the Versailles-Grignon center and nationally).

- L.K. Anderson, L.D. Lohmiller, X. Tang, D.B. Hammond, L. Javernick, L. Shearer, S. Basu-Roy, O.C. Martin, M. Falque (2014) Combined fluorescent and electron microscopic imaging unveils the specific properties of two classes of meiotic crossovers . PNAS 111 (37) 13415-13420. Highlighted in the INRA news.

- E. Bauer, M. Falque, ..., O.C. Martin and C.-C. Schoen (2013)  Intraspecific variation of recombination rate in maize, Genome Biology, 14:R103.

- M.W. Ganal, ..., O.C. Martin and M. Falque (2011) A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome, PLoS ONE 6(12): e28334 .

- Z. Burda, A. Krzywicki, O.C. Martin and M. Zagorski (2011) Motifs emerge from function in model gene regulatory networks, PNAS 108 (42) 17263-17268.