CAREER: Computational Tools for Population Biology

职业：群体生物学的计算工具

• 批准号: 0747369
• 负责人: Tanya Berger-Wolf
• 金额: $ 50.49万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0747369&HistoricalAwards=false
• 关键词: CAREER; Computational; Tools; Population; Biology

Computation has fundamentally changed the way we study nature. Recent breakthroughs in data collection technology, such as GPS and other mobile sensors, gene sequencing, and microsatellite genotyping, are giving biologists access to data about wild populations, from genetic to social interactions, that are orders of magnitude richer than any previously collected. Such data offer the promise of answering some of the big questions in population biology: How do animals form social groups and how do genetic ties affect these processes? Which individuals are leaders and to what degree do they control the behavior of others? How do social interactions affect the survival of a species? Unfortunately,in this domain,our ability to analyze data lags substantially behind our ability to collect it. There are three major drawbacks with currently available techniques for analysis of both genotypic and social structure data. First, most traditional methods are aggregate and numeric, thus they are inappropriate for identifying infrequent yet critical events, such as response to predation. Second, the newer approaches focus on human populations and are not directly applicable in the context of wildlife biology. Finally, current analysis techniques are essentially static in that all information about the time and order of social interactions or the concurrency of gene expressions is discarded. Thus, they lack the expressive and computational power to answer the questions outlined above. Intellectual MeritThe goal of this interdisciplinary research is to develop a robust and scalable computational framework for the emerging field of computational population biology. Ultimately, this research will enable biologists in their scientific inquiry to take advantage of new data by focusing on its underlying qualitative (rather than numerical) and explicitly dynamic structure. This research will use combinatorial techniques to extract that structure. In the scope of this project the following will be developed:1. Techniques for inferring genetic relationships in wildlife populations and using them to predict genetic diversity.2. Novel computational methodologies and tools for analyzing dynamic social interactions, focusing on prediction of interaction patterns and dynamic processes within populations.3. Techniques for combining the genetic and the social structures of a population and across species to identify global ecological processes. Broader ImpactsMany students, especially female, turn away from computer science in part because of the perceived lack of its applicability to real-world issues and impact on the society. This project has the potential to attract those who would otherwise be lost to computing by providing the view of its larger impact and connection to science. A comprehensive interdisciplinary education and outreach plan will be developed which bridges the traditional pipeline from K-12 to graduate education. The standard views of mathematics and computing will be broadened to include "puzzle-solving" combinatorial thinking by introducing hands-on outreach activities. The unique conflation of wild life biology and computing will continue to be presented at various forums aimed at attracting minorities and girls to science and computer science. Finally, through introduction of biological motivation in computer science courses and the computational methodology in biology courses, this research will provide the students in both disciplines with experiences in asking and answering biological questions by developing new applications of computer science. The methodologies, concepts, and tools developed as part of this interdisciplinary research will be useful to scientists in diverse fields such as behavioral ecology, conservation biology, and disease ecology. Techniques for analysis of social structures have broader relevance to human societies, especially in the context of epidemiology, dissemination of ideas, and crisis management.

计算从根本上改变了我们研究自然的方式。最近在数据收集技术方面的突破，如GPS和其他移动传感器、基因测序和微卫星基因分型，使生物学家能够获得关于野生种群的数据，从遗传到社会互动，这些数据比以前收集的任何数据都丰富了几个数量级。这些数据有望回答种群生物学中的一些大问题：动物是如何形成社会群体的，基因联系是如何影响这些过程的？哪些人是领导者？他们在多大程度上控制他人的行为？社会互动如何影响一个物种的生存？不幸的是，在这个领域，我们分析数据的能力远远落后于我们收集数据的能力。目前可用于分析基因和社会结构数据的技术有三个主要缺陷。首先，大多数传统方法是聚集的和数字的，因此它们不适合识别不常见但关键的事件，例如对捕食的反应。其次，新的方法侧重于人类种群，并不直接适用于野生动物生物学的背景。最后，目前的分析技术基本上是静态的，因为所有关于社会互动或基因表达同时发生的时间和顺序的信息都被丢弃了。因此，它们缺乏回答上述问题的表达能力和计算能力。智力价值这项跨学科研究的目标是为计算种群生物学这一新兴领域开发一个健壮且可扩展的计算框架。最终，这项研究将使生物学家在他们的科学研究中，通过关注潜在的定性(而不是数字)和显式的动态结构来利用新数据。这项研究将使用组合技术来提取这种结构。在这个项目的范围内，将开发以下内容：1.推断野生动物种群中的遗传关系并利用它们预测遗传多样性的技术。用于分析动态社会互动的新的计算方法和工具，侧重于预测人口中的互动模式和动态过程。结合种群和跨物种的遗传和社会结构以确定全球生态过程的技术。更广泛的影响许多学生，尤其是女性，对计算机科学敬而远之，部分原因是认为它不适用于现实世界的问题和对社会的影响。这个项目有可能吸引那些本来会迷失在计算中的人，因为它提供了它与科学的更大影响和联系的观点。将制定一项全面的跨学科教育和外联计划，为从K-12到研究生教育的传统渠道架起一座桥梁。数学和计算的标准观点将扩大到包括“解谜”组合思维，通过引入亲身实践推广活动。野生生物生物学和计算机的独特结合将继续在各种论坛上展示，旨在吸引少数群体和女孩学习科学和计算机科学。最后，通过在计算机科学课程中引入生物动机和在生物课程中引入计算方法论，本研究将为这两个学科的学生提供通过开发计算机科学的新应用来提出和回答生物问题的经验。作为这种跨学科研究的一部分而开发的方法、概念和工具将对行为生态学、保护生物学和疾病生态学等不同领域的科学家有用。社会结构分析技术与人类社会有更广泛的相关性，特别是在流行病学、思想传播和危机管理的背景下。