Publications
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Gouy et al., Multiple Sequence Alignment, 2021
Seaview Version 5: A Multiplatform Software for Multiple Sequence Alignment, Molecular Phylogenetic Analyses, and Tree Reconciliation
We present Seaview version 5, a multiplatform program to perform multiple alignment and phylogenetic tree building from molecular sequence data. Seaview provides network access to sequence databases, alignment with arbitrary algorithm, parsimony, distance and maximum likelihood tree building with PhyML, and display, printing, and copy-to-clipboard or to SVG files of rooted or unrooted, binary or multifurcating phylogenetic trees. While Seaview is primarily a program providing a graphical user interface to guide the user into performing desired analyses, Seaview possesses also a command-line mode adequate for user-provided scripts. Seaview version 5 introduces the ability to reconcile a gene tree with a reference species tree and use this reconciliation to root and rearrange the gene tree. Seaview is freely available at http://doua.prabi.fr/software/seaview.
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Evolutionary Systems Biology II, 2021
Of Evolution, Systems and Complexity
The question of complexity in biological systems is recurrent in evolutionary biology and is central in complex systems science for obvious reasons. But this question is surprisingly overlooked by Evolutionary Systems Biology. This comes unexpected given the roots of systems biology in complex systems science but also given that a proper understanding of the origin and evolution of complexity would provide clues for a better understanding of extant biological systems. In this chapter we will explore the links between evolutionary systems biology and biological systems complexity, in terms of concepts, tools, and results. In particular, we will show how complex models can be used to explore this question and show that complexity can spontaneously accumulate even in simple conditions owing to a “complexity ratchet” fuelled by sign-epistasis.
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Gaubert et al., Comptes Rendus. Mathématique, 2020
Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: the example of the Paris area
We portray the evolution of the Covid-19 epidemic during the crisis of March-April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four central departments of this area (Centre 15 of SAMU 75, 92, 93 and 94). Our study reveals strong dissimilarities between these departments. We show that the logarithm of each epidemic observable can be approximated by a piecewise linear function of time. This allows us to distinguish the different phases of the epidemic, and to identify the delay between sanitary measures and their influence on the load of EMS. This also leads to an algorithm, allowing one to detect epidemic resurgences. We rely on a transport PDE epidemiological model, and we use methods from Perron-Frobenius theory and tropical geometry.
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Comte et al., Bioinformatics, 2020
Treerecs: an integrated phylogenetic tool, from sequences to reconciliations
Motivation: Gene and species tree reconciliation methods are used to interpret gene trees, root them and correct uncertainties that are due to scarcity of signal in multiple sequence alignments. So far, reconciliation tools have not been integrated in standard phylogenetic software and they either lack performance on certain functions, or usability for biologists. Results: We present Treerecs, a phylogenetic software based on duplication-loss reconciliation. Treerecs is simple to install and to use. It is fast and versatile, has a graphic output, and can be used along with methods for phylogenetic inference on multiple alignments like PLL and Seaview. Availability: Treerecs is open-source. Its source code (C ++, AGPLv3) and manuals are available from https://project.inria.fr/treerecs/
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Liard et al., Artificial Life, 2020
The Complexity Ratchet: Stronger than selection, weaker than robustness
Using the in silico experimental evolution platform Aevol, we have tested the existence of a "complexity ratchet" by evolving populations of digital organisms under environmental conditions in which simple organisms can very well thrive and reproduce. We observed that in most simulations, organisms become complex although such organisms are a lot less fit than simple ones and have no robustness or evolvability advantage. This excludes selection from the set of possible explanations for the evolution of complexity. However, complementary experiments showed that selection is nevertheless necessary for complexity to evolve, also excluding non-selective effects. Analyzing the long-term fate of complex organisms, we showed that complex organisms almost never switch back to simplicity despite the potential fitness benefit. On the contrary, they consistently accumulate complexity on the long term, meanwhile slowly increasing their fitness but never overtaking that of simple organisms. This suggests the existence of a complexity ratchet powered by negative epistasis: mutations leading to simple solutions, that are favourable at the beginning of the simulation, become deleterious after other mutations-leading to complex solutionshave been fixed. This also suggests that this complexity ratchet cannot be beaten by selection, but that it can be overthrown by robustness because of the constraints it imposes on the coding capacity of the genome.
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Lehman et al., Artificial Life, 2020
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.
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Lalejini et al., Int. Conf. Artif. Life, 2019
Data Standards for Artificial Life Software
As the field of Artificial Life advances and grows, we find ourselves in the midst of an increasingly complex ecosystem of software systems. Each system is developed to address particular research objectives, all unified under the common goal of understanding life. Such an ambitious endeavor begets a variety of algorithmic challenges. Many projects have solved some of these problems for individual systems, but these solutions are rarely portable and often must be re-engineered across systems. Here, we propose a community-driven process of developing standards for representing commonly used types of data across our field. These standards will improve software re-use across research groups and allow for easier comparisons of results generated with different artificial life systems. We began the process of developing data standards with two discussion-driven workshops (one at the 2018 Conference for Artificial Life and the other at the 2018 Congress for the BEACON Center for the Study of Evolution in Action). At each of these workshops, we discussed the vision for Artificial Life data standards, proposed and refined a standard for phylogeny (ancestry tree) data, and solicited feedback from attendees. In addition to proposing a general vision and framework for Artificial Life data standards, we release and discuss version 1.0.0 of the standards. This release includes the phylogeny data standard developed at these workshops and several software resources under development to support our proposed phylogeny standards framework.
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Liard et al., Int. Conf. Artif. Life, 2018
<Best Paper Award>The Complexity Ratchet: Stronger than selection, weaker than robustness
Using the in silico experimental evolution platform Aevol, we evolved populations of digital organisms in conditions where a simple functional structure is best. Strikingly, we observed that in a large fraction of the simulations, organisms evolved a complex functional structure and that their complexity increased during evolution despite being a lot less fit than simple organisms in other populations. However, when submitted to a harsh mutational pressure, we observed that a significant proportion of complex individuals ended up with a simple functional structure. Our results suggest the existence of a complexity ratchet that is powered by epistasis and that cannot be beaten by selection. They also show that this ratchet can be overthrown by robustness because of the strong constraints it imposes on the coding capacity of the genome.
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Knibbe et al., Int. Conf. Artif. Life, 2014
What happened to my genes? Insights on gene family dynamics from digital genetics experiments
Gene families are sets of homologous genes formed by duplications of a single original gene. Inferring their history in terms of gene duplications, gene losses and gene mutations yields fundamental insights into the molecular basis of evolution. However, phylogenetic inference of gene family evolution faces two difficulties: (i) the delimitation of gene families based on sequence similarity, and (ii) the fact that the models of evolution used for reconstruction are tested against simulated data that are produced by the model itself. Here, we show that digital genetics, or in silico experimental evolution, can provide thought-provoking synthetic gene family data, robust to rearrangements in gene sequences and, most importantly, not biased by where and how we think natural selection should act. Using aevol, a digital genetics model with an abstract phenotype but a realistic genome structure, we analyzed the evolution of 3,512 synthetic gene families under directional selection. The turnover of gene families in evolutionary runs was such that only 21% of those families would be accessible for classical phylogenetic inference. Extinct families showed patterns different from the final, observable ones, both in terms of dynamics of gene gains and losses and in terms of gene sequence evolution. This study also reveals that gene sequence evolution, and thus evolutionary innovation, occurred not only through local mutations, but also through chromosomal rearrangements that re-assembled parts of existing genes.
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Batut et al., BMC Bioinfo., 2013
In silico experimental evolution: a tool to test evolutionary scenarios
Comparative genomics has revealed that some species have exceptional genomes, compared to their closest relatives. For instance, some species have undergone a strong reduction of their genome with a drastic reduction of their genic repertoire. Deciphering the causes of these atypical trajectories can be very difficult because of the many phenomena that are intertwined during their evolution (e.g. changes of population size, environment structure and dynamics, selection strength, mutation rates...). Here we propose a methodology based on synthetic experiments to test the individual effect of these phenomena on a population of simulated organisms. We developed an evolutionary model - aevol - in which evolutionary conditions can be changed one at a time to test their effects on genome size and organization (e.g. coding ratio). To illustrate the proposed approach, we used aevol to test the effects of a strong reduction in the selection strength on a population of (simulated) bacteria. Our results show that this reduction of selection strength leads to a genome reduction of ~35% with a slight loss of coding sequences (~15% of the genes are lost - mainly those for which the contribution to fitness is the lowest). More surprisingly, under a low selection strength, genomes undergo a strong reduction of the noncoding compartment (~55% of the noncoding sequences being lost). These results are consistent with what is observed in reduced Prochlorococcus strains (marine cyanobacteria) when compared to close relatives.
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Beslon et al., Europ. Conf. Artif. Life, 2013
An alife game to teach evolution of antibiotic resistance
The emergence of antibiotic resistant bacteria is a major threat to public health and there is a constant need for education to limit dangerous practices. Here, we propose to use alife software to develop training media for the public and the physicians. On the basis of the Aevol model we have been developing for more than six years, we built a game in which players fight bacterial infections using antibiotics. In this game the bacteria can evolve resistance traits, making the infection more and more difficult to cure. The game has been tested with automatic treatment procedures, showing that it behaves correctly. It has been demonstrated during the French "Nuit des Chercheurs" in October 2012.
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Julien-Laferrière et al., JOBIM, 2013
New developments in KisSplice: Combining local and global transcriptome assemblers to decipher splicing in RNA-seq data
RNA-seq is deeply changing our way to study transcriptomes. The ultimate goal is to be able to identify and quantify all RNAs present in a sample, even without any prior knowledge of the reference genome, which enables to apply this technology to both model and non model species. However, transcriptome assembly is a difficult task, in particular in the presence of alternative splicing. Two main routes have been followed so far. On the one hand, general purpose transcriptome assemblers aim at reconstructing all alternative transcripts, but, in order to cope with the inherent combinatorial explosion of this problem, they introduce heuristics which lead them to output only a subset of them (the longest ones). On the other hand, local transcriptome assemblers aim at cataloguing systematically and exactly all the splicing events of a gene but do not provide the full length transcripts. In this work, we propose a pipeline that combines the advantages of both. In practice, we map the output of our local assembler KisSplice to the output of Trinity using GEM and propose a visualisation of the results using IGV. We also report a major improvement of the memory performances of KisSplice upon its previous release, thanks to the integration of Minia for the construction of the de Bruijn graph.
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Parsons et al., Int. Conf. Artif. Life, 2012
The Paradoxical Effects of Allelic Recombination on Fitness
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Misevic et al., Int. Conf. Artif. Life, 2012
<Best Paper Award>Effects of public good properties on the evolution of cooperation
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.
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Parsons, PhD Thesis, INSA-Lyon, 2011
Indirect Selection in Darwinian Evolution:
Mechanisms and Implications -
Parsons et al., Europ. Conf. Artif. Life, 2011
Homologous and Nonhomologous Rearrangements: Interactions and Effects on Evolvability
By using Aevol, a simulation framework designed to study the evolution of genome structure, we investigate the effect of homologous rearrangements on the course of evolution. We designed an efficient model of rearrangements based on an intermittent search algorithm. Then, using experimental in silico evolution, we explore the effect of rearrangement rates on the genome structure. We show that the effect of homologous rearrangements is quite complex. At first glance they appear to be dangerous enough to trigger an indirect selective pressure leading to short genomes when the rearrangement rate is high. However, by analyzing the successful lineage in the best runs, we found that there is a positive correlation between the number of homologous rearrangements and the fitness improvement in these lineages. Thus the impact of homologous rearrangements on evolution is rather complex: dangerous on the one hand but necessary on the other hand, to ensure a sufficient level of evolvability to the organisms. Moreover, our results show that the spontaneous rate of small mutations influences the relative proportions of homologous versus nonhomologous rearrangements.
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Knibbe et al., Europ. Conf. Artif. Life, 2011
Parsimonious Modeling of Scaling Laws in Genomes and Transcriptomes
We report here the use of Aevol, a software developed in our team to unravel the indirect selective pressures (i.e. pressures for robustness and/or evolvability) that act on the genome and transcriptome structures. Using Aevol, we have shown that these structures are under strong - although indirect - pressure due to the mutagenic effect of chromosomal rearrangements. Individuals undergoing high spontaneous rearrangement rates show more compact structures than individuals undergoing lower rates. This phenomenon concerns genome size and content (non-coding DNA, presence of operons, number of genes) as well as gene network (number of nodes and links) thus reproducing parsimoniously a large panel of known biological properties. The results reported here have been published in Mol. Biol. Evol. (Knibbe et al., 2007), Biosystems (Beslon et al., 2010) and Alife XII (Parsons et al., 2010).
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Beslon et al., Biosystems, 2010
Scaling laws in bacterial genomes: A side-effect of selection of mutational robustness?
In the past few years, numerous research projects have focused on identifying and understanding scaling properties in the gene content of prokaryotes genomes and the intricacy of their regulation networks. Yet, and despite the increasing amount of data available, the origins of these scalings remain an open question. The RAevol model, a digital genetics model, provides us with an insight into the mechanisms involved in an evolutionary process. The results we present here show that (i ) our model reproduces qualitatively these scaling laws and that (ii ) these laws are not due to differences in lifestyles but to differences in the spontaneous rates of mutations and rearrangements. We argue that this is due to an indirect selective pressure for robustness that constrains the genome size.
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Parsons et al., Int. Conf. Artif. Life, 2010
Importance of the Rearrangement Rates on the Organization of Genome Transcription
The organization of genomes shows striking differences among the different life forms. These differences come along with important variations in the way genomes are transcribed, operon structures being frequent in short genomes and the exception in large ones, while ncRNAs are frequent in large genomes but rare in short ones. Here, we use the digital genetics model «aevol» to explore the influence of the mutation rates on these structures, showing that their diversity can be accurately reproduced when varying the rearrangement rate. This result points us to the mutational burden hypothesis as one of the main explanation. In this view, a specific level of mutational robustness indirectly leads to genome and transcriptome streamlining.
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Beslon et al., Intel. Data Anal. J., 2010
From digital genetics to knowledge discovery: Perspectives in genetic network understanding
In this paper, we propose an original computational approach to assist knowledge discovery in complex biological networks. First, we present an integrated model of the evolution of regulation networks that can be used to uncover organization principles of such networks. Then, we propose to use the results of our model as a benchmark for knowledge discovery algorithms. We describe a first experiment of such benchmarking by using gene knock-out data generated from the modeled organisms.
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Parsons et al., MajecSTIC, 2010
Aevol : un modèle individu-centré pour l’étude de la structuration des génomes
Genome organization shows great variations throughout the different life forms, going from very short and dense genomes in viruses to very long and mostly non-coding genomes in multicellular organisms. In this paper, we present the Aevol model, a digital genetics model developed to study the evolution of genome structure in virtual organisms, and provide an overview of the results obtained with this model. The diversity of genome organizations can be accurately reproduced by the model when varying experimental conditions. The experiments we have conducted point us to a phenomenon of error threshold as one of the main explanations for these structural differences.
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Beslon et al., IPCAT, 2009
Scaling Laws in Digital Organisms
Using an in silico model of the evolution of gene regulation networks, we show that the artificial organisms we obtained respect scaling laws depending on the mutation rates they are submitted to. These laws are observed both on genomic structures and on the main characteristics of gene regulation networks.
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Sanchez-Dehesa et al., Mat. Mod. of Nat. Phen., 2008
Modelling Evolution of Regulatory Networks in Artificial Bacteria
Studying the evolutive and adaptative mechanisms of prokaryotes is a complicated task. As these mechanisms cannot be easily studied «in vivo», it is necessary to consider other methods. We have therefore developed the RAevol model, a model designed to study the evolution of bacteria and their adaptation to the environment. Our model simulates the evolution of a population of artificial bacteria in a changing environment, providing us with an insight into the strategies that digital organisms develop to adapt to new conditions. In this paper we describe the principles and architecture of the model, focusing on the mechanisms of the regulatory networks of artificial organisms. Experiments were conducted on populations of artificial bacteria under conditions of stress. We study the ways in which organisms adapt to environmental changes and examine the strategies they adopt. An analysis of these adaptation strategies is presented and a brief overview was proposed concerning the patterns and topological characteristics of the evolved regulatory networks.
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