Exact quantification of cellular robustness in genome-scale metabolic networks.
Exact quantification of cellular robustness in genome-scale metabolic networks.
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DOI：
10.1093/bioinformatics/btv649
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发表时间：
2016-03-01
期刊：
影响因子：
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通讯作者：
Zanghellini J
中科院分区：
文献类型：
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作者：
Gerstl MP;Klamt S;Jungreuthmayer C;Zanghellini J
动机：鲁棒性，即生物网络在受到干扰的情况下保持其功能的能力，是所有生命系统的关键特征。虽然已经开发了几种理论方法来形式化鲁棒性，但它仍然无法精确量化。在这里，我们提出了一个严格的和定量的方法，代谢网络的结构鲁棒性，通过测量他们的能力，容忍随机反应（或基因）敲除。 结果如下：类似于可靠性理论，基于对所有可能的淘汰集的明确考虑，我们精确地量化了给定网络函数（例如增长）的故障概率。如果网络的最小割集（MSC）是已知的，则可以计算该度量。我们表明，即使在基因组规模的代谢网络（网络）失败的概率，可以可靠地估计从最低基数的MSC。我们证明了我们的理论的适用性，通过分析多个肠杆菌科和Blattibacteriaceae的结构鲁棒性，并表现出显着较低的结构鲁棒性，后者。我们发现，结构的鲁棒性发展的能力，在多个生长环境中的扩散与实验发现的知识一致。 结论：因此，（网络）故障的概率提供了（基因组规模）代谢网络中结构鲁棒性和冗余性的可靠且易于计算的测量。 可用性和实现：源代码在GNU通用公共许可证下可在https://github.com/mpgerstl/networkRobustnessToolbox获得。 联系人：juergen. boku.ac.at 补充信息：补充数据可从在线生物信息学获得。
Motivation: Robustness, the ability of biological networks to uphold their functionality in spite of perturbations, is a key characteristic of all living systems. Although several theoretical approaches have been developed to formalize robustness, it still eludes an exact quantification. Here, we present a rigorous and quantitative approach for the structural robustness of metabolic networks by measuring their ability to tolerate random reaction (or gene) knockouts. Results: In analogy to reliability theory, based on an explicit consideration of all possible knockout sets, we exactly quantify the probability of failure for a given network function (e.g. growth). This measure can be computed if the network’s minimal cut sets (MSCs) are known. We show that even in genome-scale metabolic networks the probability of (network) failure can be reliably estimated from MSCs with lowest cardinalities. We demonstrate the applicability of our theory by analyzing the structural robustness of multiple Enterobacteriaceae and Blattibacteriaceae and show a dramatically low structural robustness for the latter. We find that structural robustness develops from the ability to proliferate in multiple growth environments consistent with experimentally found knowledge. Conclusion: The probability of (network) failure provides thus a reliable and easily computable measure of structural robustness and redundancy in (genome-scale) metabolic networks. Availability and implementation: Source code is available under the GNU General Public License at https://github.com/mpgerstl/networkRobustnessToolbox. Contact: juergen.zanghellini@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.