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arXiv 1406.6557q-bio.MN
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Fast-SL:一种识别代谢网络中合成致死反应集的有效算法

Fast-SL: An efficient algorithm to identify synthetic lethal reaction sets in metabolic networks

Aditya Pratapa, Shankar Balachandran, Karthik Raman

摘要:

合成致死反应/基因组是反应/基因组,其中只有同时去除该组中的所有反应/基因才能消除生物体的生长。在计算机上,可以通过模拟从生物体的重建的基因组规模代谢网络中去除基因集的效果来识别合成的致死集。基于线性规划的通量平衡分析(FBA)已成为代谢网络计算机分析的有力工具。为了识别所有可能的合成致死反应组合,对所有可能的组合进行穷举采样在计算上是昂贵的。我们克服了穷举搜索的计算复杂性,通过迭代限制搜索的反应组合的样本空间,从而大大减少了运行时间。在这里,我们提出了一个算法,快速SL,它提供了一种有效的方法来分析代谢网络的高阶致死反应集。Fast-SL通过大规模减少合成致命武器的搜索空间提供了显著的加速; Fast-SL将合成致死三联体的搜索空间减少了4000倍以上。Fast-SL还可以与Suthers等人(2009)的SL算法(用于识别合成致死集的算法)相媲美,该算法涉及双层混合线性规划问题的解决方案。我们在MATLAB中实现了Fast-SL算法,建立在COBRA工具箱v2.0的基础上。

英文摘要:

Synthetic lethal reaction/gene-sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. In silico, synthetic lethal sets can be identified by simulating the effect of removal of gene sets from the reconstructed genome-scale metabolic network of an organism. Flux balance analysis (FBA), based on linear programming, has emerged as a powerful tool for the in silico analyses of metabolic networks. To identify all possible synthetic lethal reactions combinations, an exhaustive sampling of all possible combinations is computationally expensive. We surmount the computational complexity of exhaustive search by iteratively restricting the sample space of reaction combinations for search, resulting in a substantial reduction in the running time. We here propose an algorithm, Fast-SL, which provides an efficient way to analyse metabolic networks for higher order lethal reaction sets. Fast-SL offers a substantial speed-up through a massive reduction in the search space for synthetic lethals; in the case of E. coli, Fast-SL reduces the search space for synthetic lethal triplets by over 4000-fold. Fast-SL also compares favourably with SL Finder, an algorithm for identifying synthetic lethal sets, by Suthers et al (2009), which involves the solution of a bi-level Mixed Integer Linear Programming problem. We have implemented the Fast-SL algorithm in MATLAB, building upon COBRA toolbox v2.0.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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