CAREER: Digital Evolution and Biocomplexity - From Biological Theory to Computational Applications

Michigan State University, East Lansing MI

Investigators

Abstract

Natural evolution has an amazing ability to produce elegant solutions to the challenges faced by living systems. As these challenges become more complex, so too must the organisms that respond to them. Evolution is clearly a powerful force, yet it is difficult to gather sufficient data to understand how complex traits arise and are incorporated into the working whole. The twin goals for this project are to understand how evolution designs complex functions, and to apply this knowledge to solving complicated computational problems. To accomplish this, the investigators will use "digital organisms" as a model system. These are self-replicating computer programs, subject to mutations and selection, that evolve complex features de novo in a natural evolutionary process. The investigators will use this system both for research purposes and as the basis of a teaching platform in biology classrooms and museum kiosks. Populations of digital organisms are tractable and transparent, allowing researchers to study all aspects of the evolutionary design process. For example, the lineage of an evolved individual can easily be traced and the contributions of each mutation along the way can be quantified. The investigators will use these techniques to study several fundamental questions: How contingent are the outcomes of adaptive evolution on small random events? When populations reach evolutionary dead-ends, is it due to a single, severe 'wrong turn' in multidimensional genotypic space, or is this a more gradual process? How vital are deleterious mutations to promoting adaptive evolution? Are neutral mutations more important? How can these forces be harnessed to solve computational design problems? Finally, can harmful populations ( e.g., pathogens or evolving computer viruses) be forced into evolutionary dead-ends?