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实验的重建正在揭示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。这些相互作用随着时间的推移而发展，从而导致了我们今天看到的交互网络。但是，通常，我们对现在灭绝的祖先物种的这种网络的样子感兴趣。该项目将开发新算法，用于从当今的网络中恢复丢失的古老生物网络。为此，我们将重建问题视为一项网络设计任务，在该任务中，根据网络演化的模型对设计的网络进行随机修改。然后，我们寻求设计一个可能已演变为当前观察到的网络的祖传网络。开发的技术将用于在各个时间点找到单个网络的前体网络，并找到几种现存生物的共同祖先网络。与重建任务一致，我们将通过有效地搜索可能的进化模型的大量空间来创建更现实的网络演化计算模型。我们对回收的古代网络的检查将提高我们对生物相互作用如何发展以及该进化如何塑造细胞功能的理解。还将探讨算法在检索社交网络历史的应用。该项目进一步旨在通过扩大成功的生物信息学暑期实习计划来激发科学生涯的本科生和高中生。该计划将在计算机科学和分子生物学的界面上主持其他实习生，以进行有关系统生物学，基因组学和其他主题的研究。我们还将创建改进的在线资源，以将对生物信息学研究感兴趣的大学生与教师导师联系起来。祖先网络重建的研究将纳入几个本科和研究生生物信息学和算法课程中。