Any process can be framed as an algorithm; its power and its limits can then be analysed with the techniques of theoretical computer science. To analyse algorithms, we divide the world in two (i) the problem space that shapes what might happen and (ii) the dynamics of what does happen. If we fix an idealized framework for one of the two, then we can obtain powerful general results by abstracting over the other. This “algorithmic lens” can be used to view both artificial and natural processes, including the natural processes of biological evolution.
We can view the problem faces by evolving populations as a game between the population and the environment with the distribution of different phenotypes as the strategy and the evolutionary dynamics as specifying the strategy update rule. This holistic approach towards the complex interaction within biological systems allows us to make global conclusions about evolution without knowing all the reductive details of population structure. I want to provide two examples of how this is useful for both experiment and theory.
JSMF Post Doctoral Fellow, Department of Biology, University of Pennsylvania