I am interested in the interface between experimental, theoretical, and computational approaches to the study of evolution. In my research I use digital organisms, short computer programs that replicate, mutate, and evolve in complex environments maintained by the software platforms Aevol and Avida.
Aevol and Avida provides an unmatched level of biological complexity in silico. More than an analytical model or a simulation, a population of digital organisms is an instantiation of evolution that we can observe and study in real time. Flexibility of conditions, speed of evolution, and ease of data collection make them an ideal systems for answering many classical evolutionary questions. Some of the projects I've been involved in are listed below
Evolution of sex: While virtually omnipresent in the biological world, the origins and maintenance of sex and recombination are not well understood. I am interested in shortening the list of proposed theories about the evolution of sex by testing them in Avida. In particular I studied the Muller's ratchet hypothesis and found that sex does contribute to survival under strong genetic drift for a narrow range of conditions (Misevic, 2004). I am currently working on a hypothesis relating sex to changing environmental conditions.
Sex and cooperation: Recently I became quite interested in a specific type of gentic information exchange, plasmid conjugation. What makes plasmids particulary unique is the type of information the often carry: traits related to production of common good or cooperation of individuals. What is the connection between plasmids and cooperation? Cooperation and sex? I am using a digital system Aevol to further investigate these question.
Evolution of genetic architecture: Modules as the units of biological organization have been studied in diverse contexts such as development, metabolic networks, and genome organization. However, the origins and causes of modularity have been explored much less then its consequences. Using Avida, I qualify the influence of mode of reproduction on modularity and epistasis in digital organisms (Misevic, 2006).
Evolution of mutation rates: The rate of mutations is a crucial driving force of evolution but is also under selection itself. I combine analytical modeling and Avida experiments to describe the interaction of costs and benefits of high mutation rates, and predict the course of mutation rate evolution in simple and complex environments (Clune, 2009).
Group selection: In a very colorful example, artificial selection on groups of chickens living in the same cage resulted in a dramatic increase in individual productivity and survival (Craig&Muir, Poultry Science, 75:294-302, 1996). Using digital organisms instead of poultry (luckily!), I explore how selection on the level of a group can interact with individual selection and potentially explain the evolution of sex or mutation rates, traits often detrimental for the individual, but benefitial for the group.