Evolution, Behaviour, Adaptation Group
Maître de conférences
Seeking a postdoc – Modeling the evolution of enzymes along complex metabolic networks
The postdoc is funded until the end of 2024. Please send an email to firstname.lastname@example.org for details / informal inquiries, or to apply (then include a CV, a short description of your research interest / ideas about the project, and the names / emails of 2 references). Evaluation will start in mid-September and continue until the position is filled. Net salary: 1864 euros per month, including health insurance – ideal start date: ASAP.
The living world is spectacularly complex, but the theory of evolution makes it possible to understand, step by step, its intricacies. Modeling is a powerful tool to overcome the limits of our intuition and understand how complex phenomena emerge from simple ingredients. Obviously, when changing the list of ingredients, the outcome can change, making their knowledge essential.
My goal is to integrate information about the mechanisms that underlie phenotypes – the genotype-phenotype map – in order to build realistic evolutionary models. This approach avoids making (many) arbitrary choices, in particular on the distribution of mutational effects, and on the constraints that may govern evolution: there may be pleiotropy, robustness, trade-offs, and these descriptors of the distribution of mutational effects may themselves evolve.
Remarkably, these mechanisms that underlie the phenotypes form networks, the evolution of which (or in which) I study. Three currently occupy me: gene networks, endocrine networks and metabolic networks.
Gene networks are formed by the relationships governing gene expression. Some genes code for regulators (for example transcription factors) that interact with certain small sequences surrounding other genes and modify their transcription. Notice that this is a good example of the interest of mechanistic modeling; we can make simple models where these relationships appear or disappear, with certain probabilities, but in reality the regulatory sequence(s) can be more or less distant to the “right” sequence, so that these probabilities themselves change over time, which cannot be accounted for without explicitly modeling changes in regulatory sequences.
Gene networks are at the origin of many (all?) phenotypes. But their functioning is subject to noise, the number of copies of elements produced by each of these genes (RNA and proteins) necessarily varying from one cell to another. I take into account the presence of this noise (thesis of Florian Labourel) to model its exploitation in generating strategies of diversifying bet-hedging (the random expression of various phenotypes by a single genotype) and of multicellularity (several cell types by a single genotype).
My current projects on this theme (still) concern the evolution of bet-hedging and multicellularity. I am also interested in the emergence and evolution of non-genetic inheritance systems (Rajon and Charlat, 2019), for which these networks seem to be an ideal breeding ground: oddly enough, a vast majority of the mechanisms of non-genetic inheritance described take part in gene networks (small RNAs, methylation, etc.).
Physical pairings between hormones and receptors are at the origin of many phenotypes observed in multicellular organisms, and especially of the relationships between these phenotypes. It is in this context that I became interested in their evolution (thesis by Salomé Bourg), in order to model the evolution of the form of trade-offs (Bourg et al, 2019).
On this theme, my current project is to build a model to understand the evolution of temporal dynamics (over the course of life) of energy allocation to life history traits.
Without enzymes, life cannot be sustained: the biochemical reactions that provide living beings with energy and building materials, from what is in their environment, would be much too slow to sustain the life of self-replicating organisms. However, the archetype of hyper-efficient enzymes, operating at the limits of their physical limits, does not stand up to the analysis of their kinetic constants.
To understand this, we (thesis by Florian Labourel) have built models of enzyme evolution integrating an essential characteristic of living beings based on enzymatic efficiency (competition for resources) and details of the environment of a enzyme (catalyzed reaction, characteristics of the metabolite produced, etc.). This work has shown that the observed inefficiency can be understood by a plateau beyond which increasing efficiency does not increase fitness much (Labourel and Rajon, 2021).
On the other hand, the integration of the cellular constraints underlying the (expensive) expression of these enzymes has made it possible to understand why, sometimes, organisms release metabolites which still make it possible to generate energy, for the benefit of others, making thus shedding light on the evolution of common crossover interactions in microbial communities (Labourel et al, 2021).
Display of 1 to 17 publications on 17 in total
Proteome allocation and the evolution of metabolic cross-feeding
Preprintsee the publication
Natural Selection beyond Life? A Workshop Report
Life . 11 ( 10 ) : 1051
Resource uptake and the evolution of moderately efficient enzymes
Molecular Biology and Evolution .
Hormonal pleiotropy and the evolution of allocation trade‐offs
Evolution - International Journal of Organic Evolution . 73 ( 4 ) : 661-674
(In)exhaustible Suppliers for Evolution? Epistatic Selection Tunes the Adaptive Potential of Nongenetic Inheritance
American Naturalist . 194 ( 4 ) : 470-481
The Biased Evolution of Generation Time
American Naturalist . 190 : E28-E39
Chapter eight - How host plant and fluctuating environments affect insect reproductive strategies?
Insect-Plant Interactions in a Crop Protection Perspective . 81 ( 1.ed. ) : 259-287
Book chaptersee the publication
The Evolution of Bet Hedging in Response to Local Ecological Conditions
The American Naturalist . 184 : E1-E15
The role of epistasis in protein evolution
Nature . 497 : E1--E2
Compensatory evolution and the origins of innovations
GENETICS . 193 : 1209--1220
The evolution of genetic architectures underlying quantitative traits
Proceedings of the Royal Society B: Biological Sciences . 280 : 20131552
Influence of vector spatial dispersal on virulence evolution and prevalence of pathogen in stochastic environment, example of Chagas disease
Modelling Biological Evolution 2013 : Recent Progress, Current Challenges and Future Directions .
Conference papersee the publication
Coexistence of Insect Species competing for a pulsed resource: toward a unified theory of biodiversity in fluctuating environments
PLoS ONE . 6 ( 3 ) : e18039
Evolution of molecular error rates and the consequences for evolvability
Proceedings of the National Academy of Sciences of the United States of America . 108 ( 3 ) : 1082-1087
Adaptive Developmental Delay in Chagas Disease Vectors: An Evolutionary Ecology Approach
PLoS Neglected Tropical Diseases . 4 ( 5 ) : e691
Spatially heterogeneous stochasticity and the adaptive diversification of dormancy
Journal of Evolutionary Biology . 22 : 2094-2103
Adaptation locale et optimalité des stratégies de dormance en environnement imprévisible : Développements théoriques et validation expérimentale chez le balanin de la chataîgne Curculio Elephas
incollection . -- : 321-328