Antoine Frénoy
Paris Descartes University
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Publication
Featured researches published by Antoine Frénoy.
PLOS Computational Biology | 2013
Antoine Frénoy; François Taddei; Dusan Misevic
When cooperation has a direct cost and an indirect benefit, a selfish behavior is more likely to be selected for than an altruistic one. Kin and group selection do provide evolutionary explanations for the stability of cooperation in nature, but we still lack the full understanding of the genomic mechanisms that can prevent cheater invasion. In our study we used Aevol, an agent-based, in silico genomic platform to evolve populations of digital organisms that compete, reproduce, and cooperate by secreting a public good for tens of thousands of generations. We found that cooperating individuals may share a phenotype, defined as the amount of public good produced, but have very different abilities to resist cheater invasion. To understand the underlying genetic differences between cooperator types, we performed bio-inspired genomics analyses of our digital organisms by recording and comparing the locations of metabolic and secretion genes, as well as the relevant promoters and terminators. Association between metabolic and secretion genes (promoter sharing, overlap via frame shift or sense-antisense encoding) was characteristic for populations with robust cooperation and was more likely to evolve when secretion was costly. In mutational analysis experiments, we demonstrated the potential evolutionary consequences of the genetic association by performing a large number of mutations and measuring their phenotypic and fitness effects. The non-cooperating mutants arising from the individuals with genetic association were more likely to have metabolic deleterious mutations that eventually lead to selection eliminating such mutants from the population due to the accompanying fitness decrease. Effectively, cooperation evolved to be protected and robust to mutations through entangled genetic architecture. Our results confirm the importance of second-order selection on evolutionary outcomes, uncover an important genetic mechanism for the evolution and maintenance of cooperation, and suggest promising methods for preventing gene loss in synthetically engineered organisms.
Artificial Life | 2012
Dusan Misevic; Antoine Frénoy; David P. Parsons; François Taddei
Cooperation is a still unsolved and ever-controversial topic in evolutionary biology. Why do organisms engage in activities with long-term communal benefits but short-term individual cost? A general answer remains elusive, suggesting many important factors must still be examined and better understood. Here we study cooperation based on the secretion of a public good molecule using Aevol, a digital platform inspired by microbial cooperation systems. Specifically, we focus on the environmental and physical properties of the public good itself, its mobility, durability, and cost. The intensity of cooperation that evolves in our digital populations, as measured by the amount of the public good molecule organisms secrete, strongly depends on the properties of such a molecule. Specifically, and somewhat counter intuitively, digital organisms evolve to secrete more when public good degrades or diffuses quickly. The evolution of secretion also depends on the interactions between the population structure and public good properties, not just their individual values. Environmental factors affecting population diversity have been extensively studied in the past, but here we show that physical aspects of the cooperation mechanism itself may be equally if not more important. Given the wide range of substrates and environments that support microbial cooperation in nature, our results highlight the need for careful consideration of public good properties when studying the evolution of cooperation in bacterial or computational models.
Artificial Life | 2012
Antoine Frénoy; François Taddei; Dusan Misevic
Robustness and evolvability are indirectly selected properties of biological systems that still play a significant role in determining evolutionary trajectories. Understanding such second order evolution is even more challenging when considering traits related to cooperation, as the evolution of cooperation itself is governed by indirect selection. To examine the robustness and evolvability of cooperation, we used an agent-based model of digital evolution, Aevol. In Aevol individuals capable of cooperating via costly public good secretion evolve for thousands of generations in a classical tragedy of the commons scenario. We varied the cost of secreting the public good molecule between and within individual experiments and constructed and evaluated millions of mutants to quantify the organisms’ position in the fitness landscape. Populations initially evolved at different regimes selecting against secretion, and then continued the evolution at a reasonably low cost of secretion. The populations that experienced a very strong selection against cooperation evolved less secretion than the ones that initially experienced a less drastic selection against cooperation via a high secretion cost. The mutational analysis revealed a correlation between the number of mutants with increased secretion and the secretion level across all costs of secretion. We also evolved several clones of each population to highlight a strong effect of history in general on cooperation. Our work shows that the history of cooperative interactions has an effect on evolutionary dynamics, a result likely to be relevant in any cooperative systems that are frequently experiencing changes in cost and benefit of cooperation.
Evolution | 2015
Dusan Misevic; Antoine Frénoy; Ariel B. Lindner; François Taddei
Natural cooperative systems take many forms, ranging from one‐dimensional cyanobacteria arrays to fractal‐like biofilms. We use in silico experimental systems to study a previously overlooked factor in the evolution of cooperation, physical shape of the population. We compare the emergence and maintenance of cooperation in populations of digital organisms that inhabit bulky (100 × 100 cells) or slender (4 × 2500) toroidal grids. Although more isolated subpopulations of secretors in a slender population could be expected to favor cooperation, we find the opposite: secretion evolves to higher levels in bulky populations. We identify the mechanistic explanation for the shape effect by analyzing the lifecycle and dynamics of cooperator patches, from their emergence and growth, to invasion by noncooperators and extinction. Because they are constrained by the population shape, the cooperator patches expand less in slender than in bulky populations, leading to fewer cooperators, less public good secretion, and generally lower cooperation. The patch dynamics and mechanisms of shape effect are robust across several digital cooperation systems and independent of the underlying basis for cooperation (public good secretion or a cooperation game). Our results urge for a greater consideration of population shape in the study of the evolution of cooperation across experimental and modeling systems.
PLOS Computational Biology | 2017
Burcu Tepekule; Hildegard Uecker; Isabel Derungs; Antoine Frénoy; Sebastian Bonhoeffer
Multiple treatment strategies are available for empiric antibiotic therapy in hospitals, but neither clinical studies nor theoretical investigations have yielded a clear picture when which strategy is optimal and why. Extending earlier work of others and us, we present a mathematical model capturing treatment strategies using two drugs, i.e the multi-drug therapies referred to as cycling, mixing, and combination therapy, as well as monotherapy with either drug. We randomly sample a large parameter space to determine the conditions determining success or failure of these strategies. We find that combination therapy tends to outperform the other treatment strategies. By using linear discriminant analysis and particle swarm optimization, we find that the most important parameters determining success or failure of combination therapy relative to the other treatment strategies are the de novo rate of emergence of double resistance in patients infected with sensitive bacteria and the fitness costs associated with double resistance. The rate at which double resistance is imported into the hospital via patients admitted from the outside community has little influence, as all treatment strategies are affected equally. The parameter sets for which combination therapy fails tend to fall into areas with low biological plausibility as they are characterised by very high rates of de novo emergence of resistance to both drugs compared to a single drug, and the cost of double resistance is considerably smaller than the sum of the costs of single resistance.
PLOS Biology | 2018
Antoine Frénoy; Sebastian Bonhoeffer
The stress-induced mutagenesis hypothesis postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to better withstand the stress. This has implications for antibiotic treatment: exposure to subinhibitory doses of antibiotics has been reported to increase bacterial mutation rates and thus probably the rate at which resistance mutations appear and lead to treatment failure. More generally, the hypothesis posits that stress increases evolvability (the ability of a population to generate adaptive genetic diversity) and thus accelerates evolution. Measuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet subinhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus providing more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress. We developed a system based on plasmid segregation that allows us to measure death and division rates simultaneously in bacterial populations. Using this system, we found that a substantial death rate occurs at the tested subinhibitory concentrations previously reported to increase mutation rate. Taking this death rate into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover, even when antibiotics increase mutation rate, we show that subinhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics. We conclude that antibiotic-induced mutagenesis is overestimated because of death and that understanding evolvability under stress requires accounting for the effects of stress on population dynamics as much as on mutation rate. Our goal here is dual: we show that population dynamics and, in particular, the numbers of cell divisions are crucial but neglected parameters in the evolvability of a population, and we provide experimental and computational tools and methods to study evolvability under stress, leading to a reassessment of the magnitude and significance of the stress-induced mutagenesis paradigm.
Evolution | 2017
Antoine Frénoy; François Taddei; Dusan Misevic
Switching rate between cooperating and non‐cooperating genotypes is a crucial social evolution factor, often neglected by game theory‐inspired theoretical and experimental frameworks. We show that the evolution of alleles increasing the mutation or phenotypic switching rates toward cooperation is in itself a social dilemma. Although cooperative offspring are often unlikely to reproduce, due to high cost of cooperation, they can be seen both as a living public good and a part of the extended parental phenotype. The competition between individuals that generate cooperators and ones that do not is often more relevant than the competition between cooperators and non‐cooperators. The dilemma of second‐order cooperation we describe relates directly to eusociality, but can be also interpreted as a division of labor or a soma‐germline distinction. The results of our simulations shine a new light on what Darwin had already termed a “special difficulty” of evolutionary theory and describe a novel type of cooperation dynamics.
european conference on artificial life | 2013
Dusan Misevic; Antoine Frénoy; François Taddei
Plasmids are an integral and essential factor in microbial biology and evolution, with broad implications ranging from antibiotic resistance to research tools. Much has been done to describe, quantify, and modify properties of transferable plasmids, including the extensive theoretical work using simulations and models. However, a wide gap between theory and experiments still remains, especially relating to the underlying genetic architecture of transfer as well as coevolutionary dynamics of the plasmid infectivity and susceptibility. Large-scale genomic studies and more biologically accurate models are among different approaches working towards narrowing this gap. Here we describe how Aevol, a digital evolution system, can be effectively used to study plasmid and quantify various aspects of their evolution and its outcomes. Specifically, we find that plasmid maintenance is extremely sensitive to the direct fitness cost of expressing transfer genes. In our study, the genes for donor ability and recipient immunity (which additively describe the probability of plasmid transfer) typically, but not exclusively, evolved on the plasmid itself. Additionally, we find epistatic interactions between genes on plasmids and the chromosome may evolve, a new aspect of their interaction and struggle for control over each other. There is a strong coevolutionary link between donor ability and recipient immunity, with their values tracking and being driven by one another. While plasmids seem to largely behave as selfish genetic elements, they occasionally may also carry metabolic genes and directly increase individual’s fitness. With a number of concise questions and results, this initial study of plasmids in Aevol establishes the baseline and opens possibilities for future work, while simultaneously uncovering and describing novel evolutionary trajectories taken by the transferable genetic elements.
Cell Reports | 2016
Aurélie Mathieu; Sébastien Fleurier; Antoine Frénoy; Julien Dairou; Marie-Florence Bredeche; Pilar Sanchez-Vizuete; Xiaohu Song; Ivan Matic
arXiv: Neural and Evolutionary Computing | 2018
Joel Lehman; Jeff Clune; Dusan Misevic; Christoph Adami; Julie Beaulieu; Peter J. Bentley; Samuel Bernard; Guillaume Beslon; David M. Bryson; Nick Cheney; Antoine Cully; Stephane Donciuex; Fred C. Dyer; Kai Olav Ellefsen; Robert Feldt; Stephan Fischer; Stephanie Forrest; Antoine Frénoy; Christian Gagneé; Leni Le Goff; Laura M. Grabowski; Babak Hodjat; Laurent Keller; Carole Knibbe; Peter Krcah; Richard E. Lenski; Hod Lipson; Robert MacCurdy; Carlos Maestre; Risto Miikkulainen