Frenoy et al., Plos Comp. Biol., 2013

Genetic Architecture Promotes the Evolution and Maintenance of Cooperation

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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.

Batut et al., BMC Bioinfo., 2013

In silico experimental evolution: a tool to test evolutionary scenarios

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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.

Misevic et al., Int. Conf. Artif. Life, 2012<Best Paper Award>

Effects of public good properties on the evolution of cooperation

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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.

Frenoy et al., Int. Conf. Artif. Life, 2012

Robustness and evolvability of cooperation

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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.

Parsons, PhD Thesis, INSA-Lyon, 2011

Indirect Selection in Darwinian Evolution:
Mechanisms and Implications

Beslon et al., Biosystems, 2010

Scaling laws in bacterial genomes: A side-effect of selection of mutational robustness?

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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.

Parsons et al., Int. Conf. Artif. Life, 2010

Importance of the Rearrangement Rates on the Organization of Genome Transcription

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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.

Knibbe et al., Mol. Biol. Evol., 2007

A Long-Term Evolutionary Pressure on the Amount of Noncoding DNA

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A significant part of eukaryotic noncoding DNA is viewed as the passive result of mutational processes, such as the proliferation of mobile elements. However, sequences lacking an immediate utility can nonetheless play a major role in the long-term evolvability of a lineage, for instance by promoting genomic rearrangements. They could thus be subject to an indirect selection. Yet, such a long-term effect is difficult to isolate either in vivo or in vitro. Here, by performing in silico experimental evolution, we demonstrate that, under low mutation rates, the indirect selection of variability promotes the accumulation of noncoding sequences: Even in the absence of self-replicating elements and mutational bias, noncoding sequences constituted an important fraction of the evolved genome because the indirectly selected genomes were those that were variable enough to discover beneficial mutations. On the other hand, high mutation rates lead to compact genomes, much like the viral ones, although no selective cost of genome size was applied: The indirectly selected genomes were those that were small enough for the genetic information to be reliably transmitted. Thus, the spontaneous evolution of the amount of noncoding DNA strongly depends on the mutation rate. Our results suggest the existence of an additional pressure on the amount of noncoding DNA, namely the indirect selection of an appropriate trade-off between the fidelity of the transmission of the genetic information and the exploration of the mutational neighborhood. Interestingly, this trade-off resulted robustly in the accumulation of noncoding DNA so that the best individual leaves one offspring without mutation (or only neutral ones) per generation.

Knibbe, PhD Thesis, INSA-Lyon, 2006

Structuration des génomes par sélection indirecte de la variabilité mutationnelle,
une approche de modélisation et de simulation

Knibbe et al., Int. Conf. Artif. Life, 2014

What happened to my genes? Insights on gene family dynamics from digital genetics experiments

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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.

Beslon et al., Europ. Conf. Artif. Life, 2013

An alife game to teach evolution of antibiotic resistance

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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.

Misevic et al., Europ. Conf. Artif. Life, 2013

In silico evolution of transferable genetic elements

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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.

Parsons et al., Int. Conf. Artif. Life, 2012

The Paradoxical Effects of Allelic Recombination on Fitness

Hindre et al., Nat. Rev. Microbiol., 2012

New insights into bacterial adaptation through in vivo and in silico experimental evolution

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Microbiology research has recently undergone major developments that have led to great progress towards obtaining an integrated view of microbial cell function. Microbial genetics, high-throughput technologies and systems biology have all provided an improved understanding of the structure and function of bacterial genomes and cellular networks. However, integrated evolutionary perspectives are needed to relate the dynamics of adaptive changes to the phenotypic and genotypic landscapes of living organisms. Here, we review evolution experiments, carried out both in vivo with microorganisms and in silico with artificial organisms, that have provided insights into bacterial adaptation and emphasize the potential of bacterial regulatory networks to evolve.

Parsons et al., Europ. Conf. Artif. Life, 2011

Homologous and Nonhomologous Rearrangements: Interactions and Effects on Evolvability

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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.

Knibbe et al., Europ. Conf. Artif. Life, 2011

Parsimonious Modeling of Scaling Laws in Genomes and Transcriptomes

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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).

Parsons et al., MajecSTIC, 2010

Aevol : un modèle individu-centré pour l’étude de la structuration des génomes

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Beslon et al., Intel. Data Anal. J., 2010

From digital genetics to knowledge discovery: Perspectives in genetic network understanding

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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.

Sanchez-Dehesa, PhD Thesis, INSA-Lyon, 2009

RÆvol : un modèle de génétique digitale pour étudier l’évolution des réseaux de régulation génétiques

Knibbe et al., Artif. Life, 2008

The Topology of the Protein Network Influences the Dynamics of Gene Order: From Systems Biology to a Systemic Understanding of Evolution

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Systems biology invites us to consider the dynamic interactions between the components of a living cell. Here, by evolving artificial organisms whose genomes encode protein networks, we show that a coupling emerges at the evolutionary time scale between the protein network and the structure of the genome. Gene order is more stable when the protein network is more densely connected, which most likely results from a long-term selection for mutational robustness. Understanding evolving organisms thus requires a systemic approach, taking into account the functional interactions between gene products, but also the global relationships between the genome and the proteome at the evolutionary time scale.

Knibbe et al., J. Theor. Biol., 2007

Evolutionary coupling between the deleteriousness of gene mutations and the amount of non-coding sequences

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The phenotypic effects of random mutations depend on both the architecture of the genome and the gene-trait relationships. Both levels thus play a key role in the mutational variability of the phenotype, and hence in the long-term evolutionary success of the lineage. Here, by simulating the evolution of organisms with flexible genomes, we show that the need for an appropriate phenotypic variability induces a relationship between the deleteriousness of gene mutations and the quantity of non-coding sequences maintained in the genome. The more deleterious the gene mutations, the shorter the intergenic sequences. Indeed, in a shorter genome, fewer genes are affected by rearrangements (duplications, deletions, inversions, translocations) at each replication, which compensates for the higher impact of each gene mutation. This spontaneous adjustment of genome structure allows the organisms to retain the same average fitness loss per replication, despite the higher impact of single gene mutations. These results show how evolution can generate unexpected couplings between distinct organization levels.

Knibbe et al., Europ. Conf. Artif. Life, 2005

Self-adaptation of genome size in artificial organisms

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In this paper we investigate the evolutionary pressures influencing genome size in artificial organisms. These were designed with three organisation levels (genome, proteome, phenotype) and are submitted to local mutations as well as rearrangements of the genomic structure. Experiments with various per-locus mutation rates show that the genome size always stabilises, although the fitness computation does not penalise genome length. The equilibrium value is closely dependent on the mutational pressure, resulting in a constant genome-wide mutation rate and a constant average impact of rearrangements. Genome size therefore self-adapts to the variation intensity, reflecting a balance between at least two pressures: evolving more and more complex functions with more and more genes, and preserving genome robustness by keeping it small.