gMCS: fast computation of genetic minimal cut sets in large networks
gMCS: fast computation of genetic minimal cut sets in large networks
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DOI：
10.1093/bioinformatics/bty656
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发表时间：
2019-02-01
期刊：
影响因子：
5.8
通讯作者：
Planes, Francisco J.
中科院分区：
文献类型：
--
作者：
Apaolaza, Inigo;Valcarcel, Luis Vitores;Planes, Francisco J.
动机：确定最小的基因敲除策略来设计代谢系统是基于约束的重建和分析(COBRA)框架最相关的应用之一。在过去的几年里，最小割集(MCSs)方法已经成为一种很有前途的工具来执行这一任务。然而，MCSs定义了反应敲除策略，这些策略不一定在基因水平上转化为可行的策略。结果：我们给出了一个更通用、易用和高效的算法来计算MCSs到基因水平(GMCSs)。将我们的工具与现有的方法进行比较，以计算不同复杂程度的代谢网络中的必需基因和合成致死数，显示出模型规模和计算时间的显著减少。
Motivation: The identification of minimal gene knockout strategies to engineer metabolic systems constitutes one of the most relevant applications of the COnstraint-Based Reconstruction and Analysis (COBRA) framework. In the last years, the minimal cut sets (MCSs) approach has emerged as a promising tool to carry out this task. However, MCSs define reaction knockout strategies, which are not necessarily transformed into feasible strategies at the gene level.Results: We present a more general, easy-to-use and efficient computational implementation of a previously published algorithm to calculate MCSs to the gene level (gMCSs). Our tool was compared with existing methods in order to calculate essential genes and synthetic lethals in metabolic networks of different complexity, showing a significant reduction in model size and computation time.