Brian D. Connelly

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.

Evolving cooperative pheromone usage in digital organisms

The use of chemicals to communicate among organisms has enabled countless species, from microorganisms, to colonies of insects, to mammals, to survive and flourish in their respective environments. Ants, arguably natures most successful exploiters of this behavior, have evolved the use of pheromones to communicate in a wide range of situations, including mating, colony recognition, territory marking, and recruitment to new nest sites and food sources. We examine the evolution of the use of pheromones to aid in the location of, and migration to, a target area by groups of digital organisms. In an initial set of experiments, these organisms evolved efficient patterns of exploration that obviated the need for pheromones. When evolved in a more adverse environment, organisms again evolved effective search strategies, but also evolved the use of pheromones to enable the task to be completed by group members more quickly and with fewer movements. We also show that evolved organisms are more robust and better able to react to a change in the environment than a handbuilt solution. This work demonstrates the complexities that exist in the evolution of pheromone-enabled cooperation and provides insight into the behaviors executed by seemingly simple organisms in nature.

Adaptively Routing P2P Queries Using Association Analysis

Unstructured peer-to-peer networks have become a very popular method for content distribution in the past few years. By not enforcing strict rules on the networks topology or content location, such networks can be created quickly and easily. Unfortunately, because of the unstructured nature of these networks, in order to find content, query messages are flooded to nodes in the network, which results in a large amount of traffic. This work borrows the technique of association analysis from the data mining community and extends it to intelligently forward queries through the network. Because only a small subset of a nodes neighbors are forwarded queries, the number of times those queries are propagated is also reduced, which results in considerably less network traffic. These savings enable the networks to scale to much larger sizes, which allows for more content to be shared and more redundancy to be added to the system, as well as allowing more users to take advantage of such networks

Modeling the evolutionary dynamics of plasmids in spatial populations

One of the processes by which microorganisms are able to rapidly adapt to changing conditions is horizontal gene transfer, whereby an organism incorporates additional genetic material from sources other than its parent. These genetic elements may encode a wide variety of beneficial traits. Under certain conditions, many computational models capture the evolutionary dynamics of adaptive behaviors such as toxin production, quorum sensing, and biofilm formation, and have even provided new insights into otherwise unknown or misunderstood phenomena. However, such models rarely incorporate horizontal gene transfer, so they may be incapable of fully representing the vast repertoire of behaviors exhibited by natural populations. Although models of horizontal gene transfer exist, they rarely account for the spatial structure of populations, which is often critical to adaptive behaviors. In this work we develop a spatial model to examine how conjugation, one mechanism of horizontal gene transfer, can be maintained in populations. We investigate how both the costs of transfer and the benefits conferred affect evolutionary outcomes. Further, we examine how rates of transmission evolve, allowing this system to adapt to different environments. Through spatial models such as these, we can gain a greater understanding of the conditions under which horizontally-acquired behaviors are evolved and are maintained.

Evolving social behavior in adverse environments

Cooperative behaviors are pervasive in the natural world. How organisms evolve stable cooperative strategies, specifically how selection can favor such costly behaviors, is a difficult problem for which several theories exist. In this work, we use digital evolution to explore the evolution of the production of a public resource that enables populations of organisms to survive in an adverse environment. Kin selection and limited dispersal are shown to promote cooperative acts, and evolved organisms stave off invasion by cheaters and survive in increasingly-adverse environments. Further, we observe how populations react to the disappearance and later re-emergence of adversity in the environment.

Resource abundance promotes the evolution of public goods cooperation

Understanding the evolution of cooperation as part of an evolutionary stable strategy (ESS) is a difficult problem that has been the focus of much work. The associated costs of cooperation may lower the fitness of an organism below that of its non-cooperating counterpart, allowing the more fit organism to persist and outcompete the cooperator. Insight into these behaviors can help provide a better understanding of many aspects of the natural world, as well as provide future avenues for fighting disease. In this study, we use digital evolution to examine how the abundance of a required resource affects the cooperative production of a public good in an adverse environment. Evolutionary computation is an excellent tool for examining these problems, as it offers researchers complete access to organisms and total control over their environment. We find that stable cooperation can occur in otherwise competitive environments at discrete levels corresponding to the availability of a required resource. When resource levels are low, organisms focus solely on competitive behaviors. However, once resource levels cross a critical threshold, cooperation persists in populations. Further, this cooperation occurs in patches, where it is most likely to benefit relatives. Finally, we find that in some cases this cooperative behavior allows organisms to increase their competitive abilities as well.

Threshold for cooperation on irregular spatial networks.

Cooperation is a defining attribute of life as we know it, from the delicate interactions of intracellular components to social behavior in groups. However, defection and exploitation are at least as ubiquitous. Evolutionary game theory is a successful tool for investigating how cooperation may be maintained despite large advantages for defection. The Prisoners Dilemma is one such game where spatial structure can maintain cooperation, but only if the benefit-to-cost ratio (b/c) is greater than some threshold, which appears to be the average number of neighbors (k). However, this inequality was tested only for regular spatial and irregular non-spatial networks. In this paper, we use networks in Cartesian space that are based on radii of interactions. We investigate whether the b/c > k threshold holds for these irregular spatial networks, and we use a much broader range of k than previously studied. We find that this rule, and other related inequalities, hold well for the larger radii even when there is noi...

Resource Abundance and the Critical Transition to Cooperation

Cooperation is abundant in nature, occurring at all levels of biological complexity. Yet cooperation is continually threatened by subversion from noncooperating cheaters. Previous studies have shown that cooperation can nevertheless be maintained when the benefits that cooperation provides to relatives outweigh the associated costs. These fitness costs and benefits are not fixed properties, but can be affected by the environment in which populations reside. Here, we describe how one environmental factor, resource abundance, decisively affects the evolution of cooperative public goods production in two independent evolving systems. In the Avida digital evolution platform, populations evolved in environments with different levels of a required resource, whereas populations of Vibrio cholerae evolved in the presence of different nutrient concentrations. In both systems, cooperators and cheaters co‐existed stably in resource‐rich environments, whereas cheaters dominated in resource‐poor environments. These two outcomes were separated by a sharp transition that occurred at a critical level of resource. These results offer new insights into how the environment affects the evolution of cooperation and highlight the challenges that populations of cooperators face when they experience environmental change.