Starter Grant: Modeling Evolution of Gene and Protein Interactions

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Delineating the mechanisms underlying the evolution of different life forms is essential to current understanding of molecular biology. Indeed, the entire body of research on non-human organisms enhances the knowledge of human physiology because of shared common ancestory. Individual gene duplication and whole gene duplication occur frequently during evolution, and mechanisms for these duplications have been studied and modeled. In recent years, attention has increasingly turned to interactions between genes and proteins as a source of complexity. Graphtheoretic models ("networks") which use nodes to represent genes and links between the nodes to represent interactions have proven useful, for example, to infer protein function, and models of evolution that include gene duplication for such gene networks have been developed. However, these evolutionary models are only theoretical: they have not been mapped to genes of actual organisms. This project will build on recent mathematical analyses of gene and genome duplications in the model plant Arabidopsis thaliana. An evolutionary model will be built of the ancestral gene interactions of Arabidopsis thaliana which leads to the current-day organism. Currently available gene and interaction data from Arabidopsis thaliana will inform the model.Broader Impact: The results of this study will enhance understanding of how gene and protein interactions evolve under both individual gene and whole genome duplication. The project will be undertaken primarily by graduate students, with some subtasks carried out by undergraduates, thus enhancing the interdisciplinary bioinformatics training received by these students. Trainees at all levels, from undergraduate to postdoctoral, will be recruited to work on the broader project of gaining biological insights from gene networks (funded, in part, by start up funds from the host institution), with an emphasis on recruiting from groups underrepresented in computer science. The results of this study will be broadly disseminated, including presentation at interdisciplinary conferences and publication in peer-reviewed journals. Additionally, any software developed through this project will be made available publicly for download as open-source software.