Starter Grant: Modeling Evolution of Gene and Protein Interactions

描述不同生命形式的进化机制对当前分子生物学的理解至关重要。事实上，对非人类生物的整个研究体系，由于拥有共同的祖先，增强了对人类生理学的认识。个体基因复制和全基因复制在进化过程中经常发生，人们对这些复制的机制进行了研究和建模。近年来，人们越来越多地将注意力转向基因和蛋白质之间的相互作用作为复杂性的来源。图论模型（“网络”）使用节点来表示基因，节点之间的连接来表示相互作用，这已被证明是有用的，例如，用于推断蛋白质功能，并且已经开发了包括基因复制在内的基因网络的进化模型。然而，这些进化模型只是理论上的：它们还没有被映射到实际生物体的基因上。该项目将建立在最近对模式植物拟南芥基因和基因组复制的数学分析基础上。一个进化模型将建立祖先基因相互作用的拟南芥，导致目前的生物体。目前可获得的拟南芥基因和相互作用数据将为该模型提供信息。更广泛的影响：本研究的结果将加强对基因和蛋白质相互作用在个体基因和全基因组复制下如何进化的理解。本项目将主要由研究生承担，部分子任务由本科生承担，从而加强这些学生所接受的跨学科生物信息学训练。从本科生到博士后的各级受训者将被招募参与从基因网络中获得生物学见解的更广泛的项目（部分由主办机构的启动资金资助），重点是从计算机科学中代表性不足的群体中招募人员。这项研究的结果将广泛传播，包括在跨学科会议上发表和在同行评议的期刊上发表。此外，通过该项目开发的任何软件都将作为开源软件公开下载。

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.