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The stability and fragility of biological networks: Eukaryotic model organism Saccharomyces cerevisiae

机译:生物网络的稳定性和脆弱性:真核模型生物酿酒酵母酿酒酵母

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Recent studies of biological networks show that these networks are robust against the random or selective deletion of network nodes and / or edges. Ability to maintain performance of network under mutations is a key feature of live systems that has long been recognized. However, the molecular and cellular basis of this stability has just begun to be understood. Robustness is a key to understanding cellular complexity, illuminating design principles, and encouraging closer interaction between experiment and theory. A biological network mutation can be defined as the creation of a new network with k allowed network change operations for a given G network. While mutating the network, our goal is to observe the change in the measured distance estimate value after k changes of the defined distance measurement method M. In this study, the effects of edge deletion and edge insertion mutations on network topology and diffusion-based function estimation algorithms are investigated by using random mutation model on the protein-protein interaction network of eukaryote Saccharomyces cerevisiae yeast, containing 5936 nodes and 65139 edges. Experimental results shows that Saccharomyces cerevisiae protein-protein interaction network has high robustness against random mutations and that the generated mutations have no significant effect on network topology and estimation techniques.
机译:生物网络的最近的研究表明,这些网络对网络节点和/或边缘的随机或选择性删除强劲。能力下的突变,以保持网络的性能是早已认识活动系统的一个重要特征。然而,这种稳定的分子和细胞基础才刚刚开始被理解。鲁棒性是理解细胞的复杂性,照明设计原则,并鼓励实验和理论之间更密切的互动的关键。生物网络突变可以被定义为建立对于给定的G网ķ允许网络更改操作一个新的网络。而突变的网络,我们的目标是观察在定义的距离测量方法M.在这项研究中,边缘去除和边缘插入突变的网络拓扑和基于扩散的功能的影响中的k的变化之后所测量的距离估计值的变化估计算法通过使用真核生物酿酒酵母的蛋白 - 蛋白相互作用网络的随机突变模型,含有5936级的节点和65139层的边缘的影响。实验结果表明,酿酒酵母蛋白质 - 蛋白质相互作用的网络具有对随机突变和所产生的突变对网络拓扑和估计技术没有显著效果高的鲁棒性。

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