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

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:
$30.0万
依托单位:
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
项目状态:
已结题
起止时间:
2008-09-01 至 2012-08-31

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中文摘要
翻译
生物网络显示了生物化学实体之间复杂的相互作用,这些相互作用往往对生物体的生存至关重要。实体之间相互通信以协作并执行它们单独无法完成的复杂功能。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,因此利用生物网络对这些应用中的通信模式进行建模是非常有前途的。生物网络有效地适应基因或蛋白质的变化,通过寻找替代方法尽可能地保持网络稳定,从而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具有复杂通信网络的应用时,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交互实体的对齐。两个网络的对齐是它们的节点子集(即实体)之间的一对一映射。本研究开发了一个通用框架,使基因相互作用和代谢网络的两个网络有效对齐。这两个网络涵盖了从布尔模型到化学计量模型的广泛通信模型。与现有的网络对齐方法不同,该建议除了考虑相互作用实体的结构和拓扑相似性之外,还考虑了它们的功能相似性。本研究还为网络数据库的标引提供了新的方法。这些索引结构允许在网络数据库上有效地回答范围查询和top-k查询。
英文摘要
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.
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CIF: Small: Novel biologically inspired methods for analyzing multilayer networks
  • 批准号:
    2111679
  • 项目类别:
    Standard Grant
  • 资助金额:
    $33.12万
  • 财政年份:
    2021
  • 负责人:
    Tamer Kahveci
  • 依托单位:
ABI Innovation: Querying Massive Dynamic Biological Network Databases
  • 批准号:
    1262451
  • 项目类别:
    Standard Grant
  • 资助金额:
    $49.26万
  • 财政年份:
    2013
  • 负责人:
    Tamer Kahveci
  • 依托单位:
CIF: EAGER: Modeling and Querying of Probabilistic Biological Networks
  • 批准号:
    1251599
  • 项目类别:
    Standard Grant
  • 资助金额:
    $17.49万
  • 财政年份:
    2013
  • 负责人:
    Tamer Kahveci
  • 依托单位:
CAREER: New Technologies for Querying Pathway Databases
  • 批准号:
    0845439
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2009
  • 负责人:
    Tamer Kahveci
  • 依托单位:
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