Florian Labourel

Florian Labourel


  • Milner Centre for Evolution

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Research interests

My research aims at building genotype-phenotype-fitness maps and understand how these latter interact with multilevel evolution, i.e. evolution acting at different biological scales. Noticeably, there is a wide array of ways in which loci may contribute to phenotypes and eventually combine to produce complex traits such as fitness. Meanwhile, the link between phenotypes and fitness also relies on the environment, including its unpredictability and its biotic component, while the evolutionary process constantly feeds back on the G-P-F map, often biasing it in certain directions. To make sense of this complexity, I develop a broad range of models from mechanistic to more theoretical ones, and contrast their outcomes with biological data using for instance molecular data analysis.


My main research focus at the present time is on the genetic dynamics behind kind recognition where organisms preferentially cooperate with those who « look like » them. This process also known under the name of greenbeard effect raises several questions: noticeably, the Crozier’s paradox states that if organisms benefit from cooperation and know how to recognise cheaters, these latter should go extinct and, because the greenbeard locus encoding recognition has thus invaded the population, it should be undetectable and lose its « colour ». However, greenbeards loci typically display high levels of polymorphism. Combining population genetics with the Collective Investment Game developed by the team, where organisms modulate their investment in cooperation according to their relatedness within a group, we first aim to determine generic rules about how polymorphic greenbeards loci should be. Then, as greenbeards may rely on two loci (like in ligand-receptor systems), we project to tackle how this changes predictions and to eventually compare these outcomes to the genetic dynamics found in greenbeards loci of Dictyostelium Discoideum.


In parallel, I am studying how Evolution shapes genome and develop, amongst other things, population genetics model to understand how complexity, when combined with random genetic drift, impedes the power of Natural Selection. I currently work on a theoretical model of genetic interactions where the weakest trait of a given phenotype determines fitness. I also have a longstanding interest in the evolution of biological networks, especially metabolism. Using mechanistic models, I work on several distinct questions, such as when an organism should prefer to take nutrients instead of processing them, how community assemblages may evolve for metabolic reasons or how the trade-off between noise in gene expression and the protein burden (of extra expression) can explain otherwise counterintuitive cellular features. As a corollary, I have also recently started to look at the patterns of molecular evolution and what may explain them for coding sequences.


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