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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >The Relevance of Topology in Parallel Simulation of Biological Networks
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The Relevance of Topology in Parallel Simulation of Biological Networks

机译：拓扑在生物网络并行仿真中的相关性

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摘要

Important achievements in traditional biology have deepened the knowledge about living systems leading to an extensive identification of parts-list of the cell as well as of the interactions among biochemical species responsible for cell''s regulation. Such an expanding knowledge also introduces new issues. For example, the increasing comprehension of the interdependencies between pathways (pathways cross-talk) has resulted, on one hand, in the growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge remains unchanged: to be able to assemble the knowledge of every "pieceȁD; of a system in order to figure out the behavior of the whole (integrative approach). In light of these considerations, high performance computing plays a fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high dimensional systems. Here, we introduce and discuss a methodology aimed at alleviating the burden of simulating complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting the computational space of a simulation trace and delegating the computation of fragments to a number of parallel processes.
机译：传统生物学的重要成就已加深了关于生命系统的知识，从而导致了对细胞零件清单以及负责细胞调节的生化物种之间相互作用的广泛鉴定。这种不断扩大的知识也带来了新的问题。例如，对路径之间相互依赖关系（路径串扰）的日益了解，一方面导致信息复杂性的增长，另一方面导致信息缺乏一致性。总体的巨大挑战保持不变：能够汇总系统的每个“零件”知识，以弄清整体的行为（集成方法）。鉴于这些考虑，高性能计算起着根本性的作用在计算机生物学中，随机模拟是一种著名的分析工具，尽管已被广泛使用，但仍要满足严格的计算要求，尤其是在处理异构和高维系统时。这种方法源自图论，其原理是将模拟迹线的计算空间分割成碎片，并将碎片的计算委托给多个并行过程。

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