Starter Grant: Modeling Evolution of Gene and Protein Interactions

描绘不同生命形式进化的潜在机制对于目前对分子生物学的理解至关重要。事实上，由于有着共同的祖先，对非人类生物体的整体研究增强了对人类生理学的了解。个体基因复制和全基因复制在进化过程中经常发生，这些复制的机制已经被研究和建模。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，认为这是复杂性的一个来源。使用节点来表示基因以及节点之间的链接来表示相互作用的图论模型(网络)已经被证明是有用的，例如，用于推断蛋白质功能，并且已经开发了包括这种基因网络的基因复制的进化模型。然而，这些进化模型仅仅是理论上的：它们还没有被映射到实际生物体的基因。该项目将建立在对模式植物拟南芥基因和基因组复制的最新数学分析的基础上。将建立一个关于拟南芥祖先基因相互作用的进化模型，这将导致今天的生物体。目前从拟南芥获得的基因和相互作用数据将为该模型提供信息。广泛影响：这项研究的结果将增强对基因和蛋白质相互作用如何在单个基因和全基因组复制下进化的理解。该项目将主要由研究生承担，一些子任务由本科生完成，从而加强这些学生所接受的跨学科生物信息学培训。各级受训人员，从本科生到博士后，将被招募参与更广泛的项目，即从基因网络获得生物学见解(部分资金来自主办机构的启动资金)，重点是从计算机科学中代表性较低的群体招聘。这项研究的结果将广泛传播，包括在跨学科会议上的介绍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.