If mutation is the ultimate source of genetic novelty, can a bias of mutation inflow alter the evolutionary trajectory of a population? On the surface, it would appear the answer should be yes. Intuitively, the path through genotype space should be influenced by the manner in which mutations are introduced into the population. For an easy to digest analogy, consider a rephrased thought experiment proposed by Stoltzfus and Yampolsky called “Climbing Mount Probable” [Stoltzfus, A., & Yampolsky, L. Y. (2009). Climbing mount probable: Mutation as a cause of nonrandomness in evolution. Journal of Heredity, 100(5), 637–647]. Beginning with the 80 year old fitness-landscape as a mountain analogy, we can envision a population of haploid organisms clustered about the face of a mountain with the organisms’ elevation representing their absolute fitnesses. Genetic novelty introduced through mutation during […]
Laboratory components are often integral parts of both K-12 and college science courses. I certainly had a lot over the course of my science education; 5 courses with labs in high school, 8 in college. But for the overwhelming majority of them, I was essentially following a recipe and doing by rote things which had already been done and where the answers were already known. It was only in science-fair-style projects that I typically had any control over the questions I was asking, or how I would go about trying to answer them. But science education doesn’t have to be like that. Inquiry-based science practice is a growing part of the recommendations for science education1 2. Thankfully, computational tools are making these practices more accessible. NGSS Lead States. (2013). Next Generation Science Standards: For States, By […]
Stommel plots are a popular tool in ecology for thinking about the spatial and temporal scales at which processes and patterns occur. Named for Henry Stommel, who created the first such plot (shown below), these plots generally have spatial scale on the x axis, temporal scale on the y axis, and some variable indicating strength of effect on the z axis. More recently, the z axis has often been replaced with colored circles, but the concept remains the same. For more information about the history of Stommel plots, see this post. These plots are often helpful in figuring out appropriate scales for data collection and analysis, as well as for conceptualizing how processes and patterns interact. But I have yet to see one that incorporates evolution.
Previously, I talked about what fitness means. Now, it’s worth taking a moment to talk about how we calculate fitness in a few of our experimental systems, both biological and computational. Because what exactly we choose to measure can result in different outcomes, the way in which we measure something is important and therefore valuable to think critically about.
At ECAL 2015, Tim Taylor, Mark Bedau, and Alastair Channon organized a fascinating workshop on Open-Ended Evolution, which I presented at (you can watch the video here, but this post will basically cover the same points). Several of us in the Devolab have been thinking about this topic for a while; below is a collection of our thoughts for the sake of continuing this discussion. The question of open-ended evolution emerged from a practical place: organisms and ecosystems in computational evolutionary systems were far less diverse, complex, and interesting than those that seen in nature. The people studying these systems were concerned that this was the result of a fundamental limitation to the systems (although some have also argued that this is just an issue of scale). They began characterizing the dynamics of these systems in […]