CAREER: Computational Tools for Population Biology

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Computation has fundamentally changed the way we study nature. Recent breakthroughs in data collection technology, such as GPS and other mobile sensors, gene sequencing, and microsatellite genotyping, are giving biologists access to data about wild populations, from genetic to social interactions, that are orders of magnitude richer than any previously collected. Such data offer the promise of answering some of the big questions in population biology: How do animals form social groups and how do genetic ties affect these processes? Which individuals are leaders and to what degree do they control the behavior of others? How do social interactions affect the survival of a species? Unfortunately,in this domain,our ability to analyze data lags substantially behind our ability to collect it. There are three major drawbacks with currently available techniques for analysis of both genotypic and social structure data. First, most traditional methods are aggregate and numeric, thus they are inappropriate for identifying infrequent yet critical events, such as response to predation. Second, the newer approaches focus on human populations and are not directly applicable in the context of wildlife biology. Finally, current analysis techniques are essentially static in that all information about the time and order of social interactions or the concurrency of gene expressions is discarded. Thus, they lack the expressive and computational power to answer the questions outlined above. Intellectual MeritThe goal of this interdisciplinary research is to develop a robust and scalable computational framework for the emerging field of computational population biology. Ultimately, this research will enable biologists in their scientific inquiry to take advantage of new data by focusing on its underlying qualitative (rather than numerical) and explicitly dynamic structure. This research will use combinatorial techniques to extract that structure. In the scope of this project the following will be developed:1. Techniques for inferring genetic relationships in wildlife populations and using them to predict genetic diversity.2. Novel computational methodologies and tools for analyzing dynamic social interactions, focusing on prediction of interaction patterns and dynamic processes within populations.3. Techniques for combining the genetic and the social structures of a population and across species to identify global ecological processes. Broader ImpactsMany students, especially female, turn away from computer science in part because of the perceived lack of its applicability to real-world issues and impact on the society. This project has the potential to attract those who would otherwise be lost to computing by providing the view of its larger impact and connection to science. A comprehensive interdisciplinary education and outreach plan will be developed which bridges the traditional pipeline from K-12 to graduate education. The standard views of mathematics and computing will be broadened to include "puzzle-solving" combinatorial thinking by introducing hands-on outreach activities. The unique conflation of wild life biology and computing will continue to be presented at various forums aimed at attracting minorities and girls to science and computer science. Finally, through introduction of biological motivation in computer science courses and the computational methodology in biology courses, this research will provide the students in both disciplines with experiences in asking and answering biological questions by developing new applications of computer science. The methodologies, concepts, and tools developed as part of this interdisciplinary research will be useful to scientists in diverse fields such as behavioral ecology, conservation biology, and disease ecology. Techniques for analysis of social structures have broader relevance to human societies, especially in the context of epidemiology, dissemination of ideas, and crisis management.