CAREER: Model-based Reconstruction of Ancient Biological Networks

职业：基于模型的古代生物网络重建

• 批准号: 1053918
• 负责人: Carleton Kingsford
• 金额: $ 40万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1053918&HistoricalAwards=false
• 关键词: CAREER; Model; based; Reconstruction; Ancient

CAREER: Model-based Reconstruction of Ancient Biological NetworksNew experiments are revealing interactions between many molecules, such as proteins, in present-day bacterial and animal cells. These interactions have evolved over time, resulting in the networks of interactions we see today. Often, however, we are interested in what such a network looked like in the past in a now-extinct ancestral species. This project will develop new algorithms for recovering lost, ancient biological networks from present-day networks. To do this, we view the reconstruction problem as a network design task where the designed network is subjected to random modifications according to a model of network evolution. We then seek to design an ancestral network that is likely to have evolved into the observed, present-day networks. The developed techniques will be applied to find precursor networks of a single network at various points in time and to find the common ancestor network of several extant organisms. In concert with the reconstruction task, we will create more realistic computational models of network evolution by efficiently searching a large space of possible evolutionary models. Our examination of recovered ancient networks will advance our understanding of how biological interactions evolve and how that evolution has shaped the function of the cell. The application of the algorithms to retrieve the history of social networks will also be explored.The project further aims to excite undergraduate and high school students about a career in science by expanding a successful summer internship program in bioinformatics. The program will host additional interns to conduct research on systems biology, genomics, and other topics at the interface of computer science and molecular biology. We will also create improved online resources to connect undergraduates interested in bioinformatics research with faculty mentors. The research on ancestral network reconstruction will be incorporated into several undergraduate and graduate bioinformatics and algorithms courses.

职业：基于模型的古代生物网络重建新实验揭示了当今细菌和动物细胞中许多分子（例如蛋白质）之间的相互作用。这些互动随着时间的推移而不断发展，形成了我们今天看到的互动网络。然而，我们常常对现已灭绝的祖先物种过去的这种网络是什么样子感兴趣。该项目将开发新的算法，用于从当今的网络中恢复丢失的古老生物网络。为此，我们将重构问题视为网络设计任务，其中设计的网络根据网络演化模型进行随机修改。然后，我们寻求设计一个很可能演变成观察到的当今网络的祖先网络。所开发的技术将应用于在不同时间点寻找单个网络的前体网络，并寻找几种现存生物体的共同祖先网络。与重建任务相配合，我们将通过有效搜索大量可能的进化模型来创建更现实的网络进化计算模型。我们对恢复的古代网络的研究将增进我们对生物相互作用如何进化以及这种进化如何塑造细胞功能的理解。还将探索应用算法来检索社交网络的历史。该项目进一步旨在通过扩大生物信息学方面成功的暑期实习计划，激发本科生和高中生对科学职业的兴趣。该项目将接待更多的实习生，以进行系统生物学、基因组学以及计算机科学和分子生物学交叉领域的其他主题的研究。我们还将创建改进的在线资源，将对生物信息学研究感兴趣的本科生与教师导师联系起来。祖先网络重建的研究将被纳入本科生和研究生的生物信息学和算法课程中。