Carleton College

Collaborative Research: Fet: Small: Rui: Leveraging Symbiotic Co-evolution for Improved Problem Solving -evolutionary Algorithms Are a Powerful Tool for Solving Many Important Computational Problems, Such as Robot Navigation in Complex Environments, Automated Analysis of Medical Data, and Design of Scientific Instruments. While These Algorithms Have Been Highly Effective, Even State-of-the-art Applications Neglect Processes Believed to Be Key to the Evolution of Complex Biological Life, Such as Co-evolution Between Species Living in Close Association With Each Other (i.e. Symbionts). the Goal of This Project Is to Learn How to Leverage This Co-evolution to Improve Evolutionary Algorithms and Better Control the Evolution of Microbial Symbiotic Relationships in Biological Laboratories. With This Award, the Team of Researchers Will Implement and Systematically Evaluate the Effect of Six Forms of Symbiosis in a Computational Platform. Additionally, They Will Test How These Effects Change Under Three Common Environmental Variations. the Researchers Will Determine Which Combinations of Symbiosis and Environment Have the Most Potential to Solve Real-world Problems in Both Computational and Biological Contexts. During the Course of This Project, the Researchers Will Mentor Ten Undergraduate and One Graduate Student in Conducting Research at the Intersection of Computer Science and Biology, Contributing to the Next Generation of Scientists. the Investigators Propose to Advance Society?s Understanding of How Symbiotic Co-evolution Impacts Evolution?s Problem-solving, Specifically Considering the Parasitism-mutualism Spectrum, the Type of Benefit/harm, and the Environment. They Will Develop and Test Novel Techniques for Leveraging Symbiotic Co-evolution to Improve Evolutionary Search Algorithms, and They Will Model How These Techniques Could Be Used to Steer the Evolution of Microbial Communities in the Laboratory. Overall, This Work Will Revolutionize Society?s Ability to Solve Problems Using Evolution, Both in Computers and Beyond. in Addition to Identifying Powerful Algorithmic Innovations, the Investigators Will Identify Rules for When These Innovations Are Most Likely to Be Effective, a Critical Goal That Is Often Neglected in Both Evolutionary Computation and Directed Evolution Research. More Broadly, the Project Will Inform the Design of Novel Laboratory Protocols for Directed Microbial Evolution, Enabling the Improved Efficacy of Directed Evolution in Far Reaching Applications Such as Biodegrading Environmental Contaminants and Production of Biofuels. Further, the Investigators Will Mentor Ten Undergraduate Research Students (from Primarily Undergraduate Institutions) and One Graduate Student in Conducting Research at This Intersection of Computer Science and Biology, Aiming to Recruit Students From Underrepresented Groups. Students Will Be Exposed to a Broader Research Community and Interact With Researchers at a Variety of Institution Types (research 1, Small Liberal Arts College, and Public Regional University), Further Broadening Their Career Horizons. This Award Reflects NSF'S Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.- Subawards Are Planned for This Award.