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

计算机科学和生物学几十年来一直保持着长期而富有成效的关系。生物学家依靠计算方法来分析和整合大型数据集，而一些计算方法的灵感来自生物系统的高级设计原则。计算系统和生物系统的几个共同的方面和目标表明，我们可以使用一个作为另一个的研究来源，反之亦然。随着我们生成和分析生物数据能力的最新进展，现在有可能首次设计新的双向研究，将生物学和计算机科学直接联系起来。这种形式的耦合实验和计算思维，这将在这个项目中使用，可以大大有利于生物学和计算机科学。该提案还试图帮助建立这种方法的有用性，以提高公众对科学和工程的兴趣，并为不同人群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来研究大肠杆菌所利用的网络的设计，通信和协调。总体目标是确定生物系统如何在不同的规模，环境和不同的通信策略中利用分布式网络。该项目将解决生物学问题，从如何在信号网络中进行信息处理到E.大肠杆菌网络的协调在细菌细胞的群体。除了解决生物学问题，这些研究旨在提供深入的设计和使用的分布式计算系统，可以容忍恶劣的环境，故障和有限的资源，使它们适用于广泛的真实的世界的应用网络。从单细胞生物到哺乳动物的物种都利用分布式网络。该提案旨在确定关于E.杆菌这些发现也可以用于了解其他物种的类似系统。除了生物建模和算法开发的直接影响之外，计算机科学家、生物学家和公众对计算系统和生物系统之间的协同作用非常感兴趣。 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。作为该项目的一部分，资助的学生将花时间在其他学科的合作者实验室进行跨学科培训，研究将支持并为代表性不足的群体的本科生和研究生提供培训机会。PI和co-PI计划开发和提供一个关于生物启发计算方法的新课程，并在相关的国际会议上组织关于该提案主题的研讨会和教程。 项目成果将在http://www.algorithmsinnature.org上公布。

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.