Nested Canalyzing Depth and Network Stability
Nested Canalyzing Depth and Network Stability
复制标题
嵌套分析深度和网络稳定性
DOI：
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发表时间：
2011
影响因子：
3.5
通讯作者：
M. Macauley
中科院分区：
文献类型：
--
作者：
Lori Layne;Elena S. Dimitrova;M. Macauley
我们引入了函数的嵌套分析深度，它衡量了函数保留嵌套分析结构的程度。我们刻画了给定深度的函数的结构，并计算了变量的预期活动性和敏感度。这种分析量化了canalyization如何在布尔网络中带来更高的稳定性。它推广了嵌套分析函数(NCFs)的概念，NCFs恰恰是具有最大深度的函数。NCFs已被提出作为基因调控网络模型，但它们的结构往往过于严格，而且极其稀疏。我们发现，随着分析深度的增加，函数对输入扰动的敏感度降低，但稳定性回报迅速减小。此外，我们还发现，随着深度的增加，使用这些函数的网络的动力学迅速接近临界区域，这表明真实网络表现出一定程度的分析深度，对于生物网络的建模和逆向工程的许多应用而言，NCF并不比足够深度的函数更好。
We introduce the nested canalyzing depth of a function, which measures the extent to which it retains a nested canalyzing structure. We characterize the structure of functions with a given depth and compute the expected activities and sensitivities of the variables. This analysis quantifies how canalyzation leads to higher stability in Boolean networks. It generalizes the notion of nested canalyzing functions (NCFs), which are precisely the functions with maximum depth. NCFs have been proposed as gene regulatory network models, but their structure is frequently too restrictive and they are extremely sparse. We find that functions become decreasingly sensitive to input perturbations as the canalyzing depth increases, but exhibit rapidly diminishing returns in stability. Additionally, we show that as depth increases, the dynamics of networks using these functions quickly approach the critical regime, suggesting that real networks exhibit some degree of canalyzing depth, and that NCFs are not significantly better than functions of sufficient depth for many applications of the modeling and reverse engineering of biological networks.
影响因子：
8.6
作者：
Shmulevich, I;Kauffman, SA
通讯作者：
Kauffman, SA
影响因子：
2
作者：
Albert, R;Othmer, HG
通讯作者：
Othmer, HG