Collaborative Research: ABI Innovation: BCSP: Understanding the design and usage of distributed biological networks

合作研究：ABI 创新：BCSP：了解分布式生物网络的设计和使用

• 批准号: 1356505
• 负责人: Ziv Bar-Joseph
• 金额: $ 84.78万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1356505&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; BCSP

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Several common aspects and goals of computational and biological systems suggest that we can use one as a source for studies of the other and vice versa. With recent advances in our ability to generate and analyze biological data it is now possible, for the first time, to design new, bi-directional studies that directly link biology and computer science. This form of coupled experimental and computational thinking, which will be utilized in this project, can greatly benefit both biology and computer science. The proposal also seeks to help establish the usefulness of this approach to increase public interest in science and engineering and to provide interdisciplinary educational and research experiences for a diverse population of students.This joint experimental-computational project will use a bi-directional approach to study the design, communication and coordination of networks utilized by Escherichia coli. The overall goal is to determine how biological systems utilize distributed networks over different scales, environments and varying communication strategies. The project will address biological questions ranging from how information processing is performed in signaling networks to the importance of various topological features of E. coli networks to coordination in a population of bacterial cells. In addition to addressing the biological questions these studies seek to provide insights into the design and usage of networks for distributed computational systems that can tolerate harsh environments, failures and limited resources making them applicable to a wide range of real world applications. Distributed networks are utilized by species ranging from single cell organisms to mammals. The proposal seeks to determine shared principles regarding the design and usage of such networks in E. coli. and the findings can be applied to understand similar systems in other species, as well. Beyond the immediate impact of the biological modeling and the algorithms developed, the synergy between computational and biological systems is of great interest to computer scientists, biologists and the general public. The proposal includes an interdisciplinary collaboration between computer scientists, engineers and biologists. Students funded as part of this project will spend time at collaborators' labs from other disciplines leading to interdisciplinary training and the research will support and provide training opportunities for undergraduate and graduate students from underrepresented groups. The PI and co-PIs plan to develop and offer a new class on biologically inspired computational methods and to organize workshops and tutorials in relevant international meetings about the topic of this proposal. Project outcomes will be disseminated at http://www.algorithmsinnature.org.

数十年来，计算机科学和生物学一直保持着长期且富有成效的关系。生物学家依靠计算方法来分析和整合大型数据集，而一些计算方法则受到生物系统高级设计原理的启发。计算和生物系统的几个共同方面和目标表明我们可以使用其中一个作为研究另一个的来源，反之亦然。随着我们生成和分析生物数据的能力的最新进展，现在第一次有可能设计直接连接生物学和计算机科学的新的双向研究。该项目将采用这种耦合实验和计算思维的形式，可以极大地有益于生物学和计算机科学。该提案还旨在帮助确定这种方法的有效性，以提高公众对科学和工程的兴趣，并为不同群体的学生提供跨学科的教育和研究经验。这个联合实验计算项目将使用双向方法进行研究大肠杆菌利用的网络的设计、通信和协调。总体目标是确定生物系统如何在不同规模、环境和不同的通信策略上利用分布式网络。该项目将解决生物学问题，从信号网络中信息处理的方式到大肠杆菌网络各种拓扑特征的重要性，再到细菌细胞群体的协调。除了解决生物学问题之外，这些研究还试图为分布式计算系统的网络设计和使用提供见解，这些系统可以承受恶劣的环境、故障和有限的资源，使其适用于广泛的现实世界应用。从单细胞生物到哺乳动物，分布式网络被各种物种所利用。该提案旨在确定有关大肠杆菌中此类网络的设计和使用的共同原则。这些发现也可以应用于理解其他物种的类似系统。除了生物建模和开发的算法的直接影响之外，计算系统和生物系统之间的协同作用引起了计算机科学家、生物学家和公众的极大兴趣。 该提案包括计算机科学家、工程师和生物学家之间的跨学科合作。作为该项目一部分资助的学生将在其他学科的合作者实验室度过一段时间，从而进行跨学科培训，并且该研究将为来自代表性不足群体的本科生和研究生提供支持和培训机会。 PI 和 co-PI 计划开发并提供一门有关受生物学启发的计​​算方法的新课程，并在相关国际会议上组织有关该提案主题的研讨会和教程。 项目成果将在 http://www.algorithmsinnature.org 上传播。