More Bang For Your Buck: Quorum-Sensing Capabilities Improve the Efficacy of Suicidal Altruism

Within the context of evolution, an altruistic act that benefits the receiving individual at the expense of the acting individual is a puzzling phenomenon. An extreme form of altruism can be found in colicinogenic E. coli. These suicidal altruists explode, releasing colicins that kill unrelated individuals, which are not colicin resistant. By committing suicide, the altruist makes it more likely that its kin will have less competition. The benefits of this strategy rely on the number of competitors and kin nearby. If the organism explodes at an inopportune time, the suicidal act may not harm any competitors. Communication could enable organisms to act altruistically when environmental conditions suggest that that strategy would be most beneficial. Quorum sensing is a form of communication in which bacteria produce a protein and gauge the amount of that protein around them. Quorum sensing is one means by which bacteria sense the biotic factors around them and determine when to produce products, such as antibiotics, that influence competition. Suicidal altruists could use quorum sensing to determine when exploding is most beneficial, but it is challenging to study the selective forces at work in microbes. To address these challenges, we use digital evolution (a form of experimental evolution that uses self-replicating computer programs as organisms) to investigate the effects of enabling altruistic organisms to communicate via quorum sensing. We found that quorum-sensing altruists killed a greater number of competitors per explosion, winning competitions against non-communicative altruists. These findings indicate that quorum sensing could increase the beneficial effect of altruism and the suite of conditions under which it will evolve.Comment: 8 pages, 8 figures, ALIFE '14 conferenc

Presence of cerebral microbleeds is associated with worse executive function in pediatric brain tumor survivors.

BackgroundA specific form of small-vessel vasculopathy-cerebral microbleeds (CMBs)-has been linked to various types of dementia in adults. We assessed the incidence of CMBs and their association with neurocognitive function in pediatric brain tumor survivors.MethodsIn a multi-institutional cohort of 149 pediatric brain tumor patients who received cranial radiation therapy (CRT) between 1987 and 2014 at age <21 years and 16 patients who did not receive CRT, we determined the presence of CMBs on brain MRIs. Neurocognitive function was assessed using a computerized testing program (CogState). We used survival analysis to determine cumulative incidence of CMBs and Poisson regression to examine risk factors for CMBs. Linear regression models were used to assess effect of CMBs on neurocognitive function.ResultsThe cumulative incidence of CMBs was 48.8% (95% CI: 38.3-60.5) at 5 years. Children who had whole brain irradiation developed CMBs at a rate 4 times greater than those treated with focal irradiation (P < .001). In multivariable analysis, children with CMBs performed worse on the Groton Maze Learning test (GML) compared with those without CMBs (Z-score -1.9; 95% CI: -2.7, -1.1; P < .001), indicating worse executive function when CMBs are present. CMBs in the frontal lobe were associated with worse performance on the GML (Z-score -2.4; 95% CI: -2.9, -1.8; P < .001). Presence of CMBs in the temporal lobes affected verbal memory (Z-score -2.0; 95% CI: -3.3, -0.7; P = .005).ConclusionCMBs are common and associated with neurocognitive dysfunction in pediatric brain tumor survivors treated with radiation

Automatically generating behavioral models of adaptive systems to address uncertainty

Abstract. Increasingly, high-assurance applications rely on dynamically adaptive systems (DASs) to respond to environmental changes, while satisfying functional requirements and non-functional preferences. Examples include critical infrastructure protection and transportation systems. A DAS comprises a collection of (non-adaptive) target systems (represented as UML models) and a set of adaptations that realize transitions among target systems. Two sources of uncertainty inherent to DASs are: (1) predicting the future execution environment, and (2) using functional and non-functional trade-offs to respond to the changing environment. To address this uncertainty, we are inspired by living organisms that are astonishingly adept at adapting to changing environmental conditions using evolution. In this paper, we describe a digital evolution-based approach to generating models that represent possible target systems suitable for different environmental conditions, enabling the developer to identify the functional and non-functional trade-offs between the models, and then assisting the developer in selecting target systems for the DAS.

Data from: Task-switching costs promote the evolution of division of labor and shifts in individuality

From microbes to humans, the success of many organisms is achieved by dividing tasks among specialized group members. The evolution of such division of labor strategies is an important aspect of the major transitions in evolution. As such, identifying specific evolutionary pressures that give rise to group-level division of labor has become a topic of major interest among biologists. To overcome the challenges associated with studying this topic in natural systems, we use actively evolving populations of digital organisms, which provide a unique perspective on the de novo evolution of division of labor in an open-ended system. We provide experimental results that address a fundamental question regarding these selective pressures: Does the ability to improve group efficiency through the reduction of task-switching costs promote the evolution of division of labor? Our results demonstrate that as task-switching costs rise, groups increasingly evolve division of labor strategies. We analyze the mechanisms by which organisms coordinate their roles and discover strategies with striking biological parallels, including communication, spatial patterning, and task-partitioning behaviors. In many cases, under high task-switching costs, individuals cease to be able to perform tasks in isolation, instead requiring the context of other group members. The simultaneous loss of functionality at a lower level and emergence of new functionality at a higher level indicates that task-switching costs may drive both the evolution of division of labor and also the loss of lower-level autonomy, which are both key components of major transitions in evolution