Modelling Evolution

Modeling evolutionary processes goes way beyond the Hardy-Weinberg Equilibrium we all learned in biology class. Natural selection comes from many sources like resources availability, mate preferences, competition. Modeling entire populations of organisms of different species is the holy grail of digital evolution. Join our discussion with evolutionary biologist and software engineer Ben Haller to learn about his work on SLiM and how it helps other biologists model population genetics over time.

Guest

Ben Haller: I finished my PhD at McGill with Andrew Hendry in June 2013. At that time, my research interests were in the details of the process of speciation: how do new species develop, what drives or inhibits that, and what theoretical models of speciation best fit nature? I was particularly interested in the early stages of speciation: gene flow, adaptive divergence, and the ecological speciation model. In my research, I developed computational simulations of eco-evolutionary processes, using Mac OS X, Objective-C, C++, Cocoa, and R. I used those simulations to observe speciation as an ongoing process, in order to better understand its dynamics. This work led rather naturally into my present work on SLiM, which I began with Philipp at Cornell in 2014.