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A Graph-theoretic Analysis Of The Human Protein-interaction Network Using Multicore Parallel Algorithms

机译:基于多核并行算法的人蛋白质相互作用网络图论分析

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Due to fundamental physical limitations and power constraints, we are witnessing a paradigm shift in commodity microprocessor architecture to multicore designs. Continued performance now requires the exploitation of concurrency at the algorithm level. In this article, we demonstrate the application of high performance computing techniques in systems biology and present multicore algorithms for the important research problem of protein-interaction network (PIN) analysis. PINs play an important role in understanding the functional and organizational principles of biological processes. Promising computational techniques for key systems biology research problems such as identification of signaling pathways, novel protein function prediction, and the study of disease mechanisms, are based on topological characteristics of the protein interactome. Several complex network models have been proposed to explain the evolution of protein networks, and these models primarily try to reproduce the topological features observed in yeast, the model eukaryote interactome. In this article, we study the structural properties of a high-confidence human interaction network, constructed by assimilating recent experimentally derived interaction data. We identify topological properties common to the yeast and human protein networks. Betweenness is a quantitative measure of centrality of an entity in a complex network, and is based on computing all-pairs shortest paths in the graph. A novel contribution of our work is the analysis of the degree-betweenness centrality correlation in the human PIN. Jeong et al. empirically showed that betweenness is positively correlated with the essentiality and evolutionary age of a protein. We observe that proteins with high betweenness, but low degree (or connectivity) are abundant in the human PIN. We have designed efficient and portable parallel implementations for the exact calculation of betweenness and other compute-intensive centrality metrics relevant to interactome analysis. For example, on the Sun Fire T2000 server with the UltraSparc T1 (Niagara) processor, we achieve a relative speedup of about 16 using 32 threads for a typical instance of betweenness centrality on a PIN, reducing the running time from nearly 3 1/2 min to 13 s.
机译:由于基本的物理限制和功率限制,我们目睹了商品微处理器体系结构向多核设计的转变。持续的性能现在需要在算法级别利用并发性。在本文中,我们演示了高性能计算技术在系统生物学中的应用,并提出了针对蛋白质相互作用网络(PIN)分析这一重要研究问题的多核算法。 PIN在理解生物过程的功能和组织原理方面起着重要作用。关键系统生物学研究问题的有前途的计算技术,例如信号通路的识别,新型蛋白质功能预测以及疾病机理的研究,都是基于蛋白质相互作用组的拓扑特征。已经提出了几种复杂的网络模型来解释蛋白质网络的进化,这些模型主要尝试重现在酵母(真核生物相互作用模型)中观察到的拓扑特征。在本文中,我们研究了通过吸收最新的实验得出的交互数据而构建的高可信度人类交互网络的结构特性。我们确定了酵母和人类蛋白质网络共有的拓扑特性。中间性是对复杂网络中实体中心性的定量度量,它基于计算图中所有对的最短路径。我们工作的一个新颖贡献是分析了人类PIN中的度间中心度相关性。 Jeong等。实验表明,中间性与蛋白质的必需性和进化年龄呈正相关。我们观察到,人PIN中具有较高中间性但较低程度(或连通性)的蛋白质。我们已经设计了高效且可移植的并行实现,以精确计算中间性和其他与交互组分析相关的计算密集型中心度指标。例如,在配备UltraSparc T1(Niagara)处理器的Sun Fire T2000服务器上,对于PIN居中性的典型实例,我们使用32个线程实现了约16的相对加速,将运行时间从近3 1/2减少了分钟至13 s。

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