Evolution of Swarming Behavior Is Shaped by How Predators Attack.

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of domains of danger. Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.

Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq.

BACKGROUND: Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events. RESULTS: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for ~25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold). CONCLUSIONS: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.

The effect of conflicting pressures on the evolution of division of labor.

Within nature, many groups exhibit division of labor. Individuals in these groups are under seemingly antagonistic pressures to perform the task most directly beneficial to themselves and to potentially perform a less desirable task to ensure the success of the group. Performing experiments to study how these pressures interact in an evolutionary context is challenging with organic systems because of long generation times and difficulties related to group propagation and fine-grained control of within-group and between-group pressures. Here, we use groups of digital organisms (i.e., self-replicating computer programs) to explore how populations respond to antagonistic multilevel selection pressures. Specifically, we impose a within-group pressure to perform a highly-rewarded role and a between-group pressure to perform a diverse suite of roles. Thus, individuals specializing on highly-rewarded roles will have a within-group advantage, but groups of such specialists have a between-group disadvantage. We find that digital groups could evolve to be either single-lineage or multi-lineage, depending on experimental parameters. These group compositions are reminiscent of different kinds of major evolutionary transitions that occur within nature, where either relatives divide labor (fraternal transitions) or multiple different organisms coordinate activities to form a higher-level individual (egalitarian transitions). Regardless of group composition, organisms embraced phenotypic plasticity as a means for genetically similar individuals to perform different roles. Additionally, in multi-lineage groups, organisms from lineages performing highly-rewarded roles also employed reproductive restraint to ensure successful coexistence with organisms from other lineages.

The evolutionary origin of somatic cells under the dirty work hypothesis.

Reproductive division of labor is a hallmark of multicellular organisms. However, the evolutionary pressures that give rise to delineated germ and somatic cells remain unclear. Here we propose a hypothesis that the mutagenic consequences associated with performing metabolic work favor such differentiation. We present evidence in support of this hypothesis gathered using a computational form of experimental evolution. Our digital organisms begin each experiment as undifferentiated multicellular individuals, and can evolve computational functions that improve their rate of reproduction. When such functions are associated with moderate mutagenic effects, we observe the evolution of reproductive division of labor within our multicellular organisms. Specifically, a fraction of the cells remove themselves from consideration as propagules for multicellular offspring, while simultaneously performing a disproportionately large amount of mutagenic work, and are thus classified as soma. As a consequence, other cells are able to take on the role of germ, remaining quiescent and thus protecting their genetic information. We analyze the lineages of multicellular organisms that successfully differentiate and discover that they display unforeseen evolutionary trajectories: cells first exhibit developmental patterns that concentrate metabolic work into a subset of germ cells (which we call "pseudo-somatic cells") and later evolve to eliminate the reproductive potential of these cells and thus convert them to actual soma. We also demonstrate that the evolution of somatic cells enables phenotypic strategies that are otherwise not easily accessible to undifferentiated organisms, though expression of these new phenotypic traits typically includes negative side effects such as aging.

Predator confusion is sufficient to evolve swarming behaviour.

Swarming behaviours in animals have been extensively studied owing to their implications for the evolution of cooperation, social cognition and predator-prey dynamics. An important goal of these studies is discerning which evolutionary pressures favour the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behaviour in prey. Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator's visual field, favouring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals. Finally, we provide evidence that when prey evolve swarming in response to predator confusion, there is a change in the shape of the functional response curve describing the predator's consumption rate as prey density increases. Thus, we show that a relatively simple perceptual constraint--predator confusion--could have pervasive evolutionary effects on prey behaviour, predator sensory mechanisms and the ecological interactions between predators and prey.

Evolution of resistance to quorum quenching in digital organisms.

Quorum sensing (QS) is a collective behavior whereby actions of individuals depend on the density of the surrounding population. Bacteria use QS to trigger secretion of digestive enzymes, formation and destruction of biofilms, and, in the case of pathogenic organisms, expression of virulence factors that cause disease. Investigations of mechanisms that prevent or disrupt QS, referred to as quorum quenching, are of interest because they provide a new alternative to antibiotics for treating bacterial infections. Traditional antibiotics either kill bacteria or inhibit their growth, producing selective pressures that promote resistant strains. In contrast, quorum quenching and other so-called anti-infective strategies focus on altering behavior. In this article we evolve QS in populations of digital organisms, a type of self-replicating computer program, and investigate the effects of quorum quenching on these populations. Specifically, we injected the populations with mutant organisms that were impaired in selected ways to disrupt the QS process. The experimental results indicate that the rate at which these mutants are introduced into a population influences both the evolvability of QS and the persistence of an existing QS behavior. Surprisingly, we also observed resistance to quorum quenching. Effectively, populations evolved resistance by reaching quorum at lower cell densities than did the parent strain. Moreover, the level of resistance was highest when the rate of mutant introduction increased over time. These results show that digital organisms can serve as a model to study the evolution and disruption of QS, potentially informing wet-lab studies aimed at identifying targets for anti-infective development.

Evolution of synchronization and desynchronization in digital organisms.

We present a study in the evolution of temporal behavior, specifically synchronization and desynchronization, through digital evolution and group selection. In digital evolution, a population of self-replicating computer programs exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. Group selection links the survival of the individual to the survival of its group, thus encouraging cooperation. Previous approaches to engineering synchronization and desynchronization algorithms have taken inspiration from nature: In the well-known firefly model, the only form of communication between agents is in the form of flash messages among neighbors. Here we demonstrate that populations of digital organisms, provided with a similar mechanism and minimal information about their environment, are capable of evolving algorithms for synchronization and desynchronization, and that the evolved behaviors are robust to message loss. We further describe how the evolved behavior for synchronization mimics that of the well-known Ermentrout model for firefly synchronization in biology. In addition to discovering self-organizing behaviors for distributed computing systems, this result indicates that digital evolution may be used to further our understanding of synchronization in biology.