EMT/BSSE: Biological networks as a communication model for entities with complex interactions

EMT/BSSE：生物网络作为具有复杂交互的实体的通信模型

• 批准号: 0829867
• 负责人: Tamer Kahveci
• 金额: $ 30万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0829867&HistoricalAwards=false
• 关键词: EMT; BSSE; Biological; networks; communication

Biological networks show the complex interactions between bio-chemical entities that are often vital for the survival of organisms. The entities communicate with each other to collaborate and perform complex functions that they can not do individually. Numerous applications follow an interaction pattern that resembles biological networks. Wireless networks, sensor networks and homeland security are just a few examples to these applications. Employing biological networks to model the communication patterns in these applications is very promising as the biological networks are robust and flexible. The biological networks efficiently adapt to the alterations in genes or proteins to minimize the damage done to the network by finding alternative ways to keep the network stable whenever it is possible.One of the critical problems in analysis of biological networks as well as many other applications with complex communication networks is finding similarities between them. To solve this problem, it is necessary to find an alignment of the interacting entities of the input pathways. An alignment of two networks is a one-to-one mapping between a subset of their nodes (i.e., entities). This research develops a generic framework that enables efficient alignment of two networks for gene interaction and metabolic networks. These two networks cover a broad spectrum of communication models ranging from Boolean to stoichiometric models. Unlike existing network alignment methods, this proposal considers the functional similarities of the interacting entities in addition to their structural and topological similarities. This research also develops new methods for indexing network databases. These index structures allow answering range and top-k queries efficiently over a network database.

生物网络显示了生化实体之间复杂的相互作用，这些相互作用通常对生物体的生存至关重要。这些实体相互通信以协作并执行它们无法单独完成的复杂功能。许多应用程序遵循类似于生物网络的交互模式。无线网络、传感器网络和国土安全只是这些应用的几个例子。由于生物网络具有鲁棒性和灵活性，因此采用生物网络来建模这些应用中的通信模式是非常有前途的。生物网络能够有效地适应基因或蛋白质的变化，尽可能地寻找保持网络稳定的替代方法，以最大限度地减少对网络的损害。在分析生物网络以及许多其他复杂通信网络的应用中，关键问题之一是找到它们之间的相似性。为了解决这个问题，有必要找到输入路径的相互作用实体的对齐。两个网络的对准是它们的节点的子集（即，实体）。这项研究开发了一个通用的框架，使基因相互作用和代谢网络的两个网络的有效对齐。这两个网络涵盖了广泛的通信模型，从布尔到化学计量模型。与现有的网络对齐方法不同，该建议除了考虑它们的结构和拓扑相似性之外，还考虑了相互作用实体的功能相似性。本研究也为网络数据库的索引提供了新的方法。这些索引结构允许在网络数据库上有效地回答范围和top-k查询。