SGER: NET HMMs and Their Applications to Biological Network Alignment

SGER：NET HMM 及其在生物网络对齐中的应用

• 批准号: 0829276
• 负责人: Luay Nakhleh
• 金额: --
• 依托单位: William Marsh Rice University
• 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 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0829276&HistoricalAwards=false
• 关键词: SGER; NET; HMMs; Their; Applications

Biological interaction networks are graphs in which nodes represent molecules, such as genes or proteins, and edges represent interactions among the molecules. These networks underly and govern the mechanisms of cellular decision-making, and play major roles in understanding causes of different diseases, as well as designing effective, targeted therapeutics. Advances in biotechnologies are amassing large amounts of these types of data from multiple organisms. One powerful tool for analyzing such data and elucidating functional components in them is through comparative analysis by means of network alignment. Roughly speaking, aligning a pair of networks, typically from two different organisms, entail finding the ?evolutionary correspondence? between the nodes of the networks. This pairwise alignment is also generalizable to multiplenetworks. The rationale behind aligning a set of networks is identifying subsets of these networks that are conserved across organisms, which are natural candidates for functional molecular components.This research will develop models and algorithms for aligning interaction networks and identifying conserved elements via a probabilistic framework that uses hidden Markov models (HMMs). Despite the success of HMMs in a variety of biological applications, mainly in the sequence analysis area, they have not been considered in the realm of network alignment. The reason for this has been that while HMMs naturally apply to one-dimensional data, such as sequences or columns of a matrix, they do not apply to multidimensional data, such as graphs. To achieve this goal, the investigator will conduct research in two areas. First, the investigator will formulate an HMM-based framework for alignment multiple biological interaction networks. This part entails devising strategies for identifying a mapping among nodes of the networks and devising schemes for handling matches, mismatches, insertions, and deletions in these networks. Second, the investigator will design novel algorithms that enable the HMM to ?operate? in a multi-dimensional space.

生物相互作用网络是一种图形，其中节点表示分子，如基因或蛋白质，而边缘表示分子之间的相互作用。这些网络是细胞决策机制的基础和控制，在理解不同疾病的原因以及设计有效的靶向治疗方面发挥着重要作用。生物技术的进步正在从多种生物体中收集大量这类数据。分析此类数据并阐明其中功能组件的一个强大工具是通过网络对齐的比较分析。粗略地说，调整一对网络，通常来自两个不同的生物体，需要找到？进化的信件吗?在网络节点之间。这种成对对齐也可推广到多网络。对齐一组网络的基本原理是识别这些网络的子集，这些子集在生物体中是保守的，它们是功能性分子成分的天然候选物。本研究将开发模型和算法，通过使用隐马尔可夫模型（hmm）的概率框架来对齐交互网络和识别保守元素。尽管hmm在多种生物学应用中取得了成功，主要是在序列分析领域，但它们还没有被考虑到网络比对领域。这样做的原因是，虽然hmm自然适用于一维数据，如矩阵的序列或列，但它们不适用于多维数据，如图。为了实现这一目标，研究者将在两个方面进行研究。首先，研究者将制定一个基于hmm的框架来校准多个生物相互作用网络。这部分需要设计策略来识别网络节点之间的映射，并设计方案来处理这些网络中的匹配、不匹配、插入和删除。其次，研究人员将设计新颖的算法，使HMM能够运行。在多维空间中。