Seeking a postdoc – Modeling the evolution of enzymes along complex metabolic networks

The postdoc is for two years at the LBBE in Lyon. Please send an email to 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 1 or 2 references). Evaluation will start in mid-September and continue until the position is filled. Salary before tax: 1864 euros per month – ideal start date: beginning of 2023.  

What is it about? Metabolic reactions form networks that are essential to transforming nutrients into energy and useful building blocks. As essential are enzymes that catalyze these reactions. Yet, despite their contribution to competition at the most basic level, enzymes are extremely diverse and sometimes only moderately efficient. No less puzzling is the presence of a large amount of redundancy in metabolic networks – meaning that some enzymes are possibly useless. This project aims at understanding this conundrum by studying the joint evolution of enzyme features and the structure of the metabolic network. This involves (i) building and analyzing mechanistic population genetics models to get an idea of what enzymes should look like, and (ii) comparing these predictions to published data.

Who is it for? About anyone who has done modeling, preferably in evolution, and would like to focus on mechanistic aspects. The group is philosophically inclined towards producing models informed by our knowledge of the systems considered, so the ideal candidate should have, on top of mathematical / computational skills, an appetite for reading and discovering how things work. Not having worked on enzymes, or even at this level of biological organisation, is fine; it actually shows how open-minded you are!

What if I have other great ideas? Let’s discuss! We love novel ideas and while we want this project to move forward, we can make some space to explore elsewhere too.

Who would I work with? Etienne Rajon is the main supervisor of the project. Sabine Peres – a specialist of metabolic network architectures – is also involved, mostly in the data analysis part of the project. 

Do I want to live in Lyon for two years? Sometimes science requires that kind of sacrifice…. Just kidding, of course you do! Lyon is a vibrant, human-sized city. It is renowned for its gastronomy, has many festivals, an iconic lightshow, museums, etc. The LBBE is a thematically diverse structure (perfect for the open-minded) with “departments” ranging from genomics to evolutionary ecology. We have external and internal seminars, thematic days, etc. And this is just to learn from each other, because we consider that this is part of (what is interesting in) our jobs.


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).


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