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  1. Holler, Silvia ; Löffler, Richard ; Bartlett, Stuart (Ed.)
    The major evolutionary transition to multicellularity shifted the unit of selection from individual cells to multicellular organisms. Constituent cells must regulate their growth and cooperate to benefit the whole organism, even when such behaviors would have been maladaptive were they free living. Mutations that disrupt cellular cooperation can lead to various ailments, including physical deformities and cancer. Organisms therefore employ mechanisms to enforce cooperation, such as error correction, policing, and genetic robustness. We built a simulation to study this last mechanism under a range of evolutionary conditions. Specifically, we asked: How does genetic robustness against cellular cheating evolve in multicellular organisms? We focused on early multicellular organisms (with only one cell type) where cells must control their growth to avoid overwriting each other. In our model, unrestrained cells will outcompete restrained cells within an organism, but restrained cells alone will result in faster reproduction for the organism. Ultimately, we demonstrate a clear selective pressure for genetic robustness in multicellular organisms and show that this pressure increases with the total number of cells in the organism.
    Free, publicly-accessible full text available July 18, 2023
  2. Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypicmore »plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution.« less
  3. 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 andmore »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.« less