CIF: EAGER: Modeling and Querying of Probabilistic Biological Networks

CIF：EAGER：概率生物网络的建模和查询

• 批准号: 1251599
• 负责人: Tamer Kahveci
• 金额: $ 17.49万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251599&HistoricalAwards=false
• 关键词: CIF; EAGER; Modeling; Querying; Probabilistic

Many critical functions performed by organisms are governed by a complex network of interactions among various biochemical molecules. Understanding how different functions are served through these interactions is of utmost importance. Like many processes in the biological realm, interactions are probabilistic events. An interaction may or may not happen with some probability, depending on a variety of factors such as the size, abundance or proximity of the interacting molecules. The probabilistic nature of the interactions introduces significant computational challenges in studying biological networks.Intellectual Merit:This project develops novel computational techniques that characterize and compare probabilistic networks. More specifically, this proposal addresses the following problems. - (Modeling) It will develop novel mathematical models that characterize topological properties of probabilistic networks precisely and efficiently.- (Comparative analysis) It will develop a framework that allows comparing biological networks when at least one of them is probabilistic.Characterizing the implications of uncertainties in interactions of biological networks is a computationally interesting and challenging problem. The main difficulty is that probabilistic interactions yield an exponential number of alternative network topologies. At the heart of this project lies a novel mathematical technique based on probability generating functions. This technique reduces a broad set of questions on the network structure to operations on polynomials resulting in very efficient algorithms. This project will use this technique to address the problem of aligning probabilistic biological networks. Broader Impact:Numerous applications follow an interaction pattern that resembles biological networks. Wireless networks, sensor networks, social networks and homeland security are just a few examples. A critical common property of these applications is that the interactions that define them are probabilistic events. This project will enable studying such networks and thus will help answering fundamental queries such as: What are the similar patterns between two social networks?, How fast do we expect a virus spread through a given wireless network? precisely and efficiently even when interactions are probabilistic.This project will also have educational impact. The PIs will recruit and train a graduate student as a part of this project. Finally, the code developed in this project will serve as an excellent educational tool to analyze and query data for a broad spectrum of applications, where the database consists of a set of interacting entities.

生物体执行的许多关键功能受各种生化分子之间复杂的相互作用网络的控制。了解如何通过这些互动来提供不同的功能至关重要。像生物领域中的许多过程一样，相互作用是概率事件。相互作用可能会或可能不会发生某种概率，具体取决于各种因素，例如相互作用分子的大小，丰度或接近性。相互作用的概率性质在研究生物网络时引入了重大的计算挑战。智能优点：该项目开发了表征和比较概率网络的新型计算技术。更具体地说，该提案解决了以下问题。 - （建模）它将开发出新的数学模型，以精确有效地表征概率网络的拓扑特性。-（比较分析）它将开发一个框架，该框架可以比较生物网络时，当其中至少一个是概率是概率的。将不确定性在生物学网络相互作用中的含义是一个计算有趣且挑战性的问题。主要困难是概率相互作用产生了指数级的替代网络拓扑数。该项目的核心是一种基于概率生成函数的新型数学技术。该技术将有关网络结构的广泛问题减少到对多项式的操作，从而产生非常有效的算法。该项目将使用此技术来解决使概率生物网络保持一致的问题。更广泛的影响：众多应用遵循类似于生物网络的相互作用模式。无线网络，传感器网络，社交网络和国土安全性只是几个示例。这些应用程序的关键共同属性是定义它们的相互作用是概率事件。该项目将使研究此类网络能够帮助回答基本查询，例如：两个社交网络之间的类似模式是什么？我们期望病毒通过给定的无线网络传播的速度如何？即使相互作用是概率的，也可以精确有效地。该项目也会产生教育影响。 PI将作为该项目的一部分招募和培训研究生。最后，该项目中开发的代码将作为一个出色的教育工具，可以分析和查询广泛的应用程序，该应用程序由一组交互实体组成。