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Génétique Quantitative et Évolution - Le Moulon
Olivier MARTIN

Olivier MARTIN

Senior Investigator, INRAE

Genetic recombination analysis

  • meiosis,
  • crossovers,
  • genetic interference,
  • gene networks,
  • MCMC

olivier.c.martin@inrae.fr

01 69 33 23 36

orcid.org/0000-0002-5295-5963

Publications

  • Génétique Quantitative et Évolution - Le Moulon
  • INRA - Université Paris-Sud - CNRS - AgroParisTech
  • Ferme du Moulon
  • F-91190 Gif-sur-Yvette
    Équipe :

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

2 pathways for crossover formation

2 pathways for crossover formation

At 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

Interaction network in A. thaliana flower formation

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.

Publications