Network class superposition analyses.
Network class superposition analyses.
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DOI：
10.1371/journal.pone.0059046
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Simha R
中科院分区：
文献类型：
--
作者：
Pearson CA;Zeng C;Simha R
网络通常用于通过对各个部分之间的交互进行建模来理解整个系统。例子包括细胞中的生物分子相互作用以提供一些主要功能，或在环境中形成稳定群落的物种。然而，这些相互作用通常是未知的;相反，这些片段的动态状态是已知的，并且必须推断网络结构。因为观察到的函数可以由许多不同的网络来解释（例如， 对于酵母细胞周期过程），考虑超出该主要功能的动态意味着选择单个网络或合适的样本：测量所有表现出主要功能的网络在计算上是不可行的。我们通过计算网络类集合来绕过这个障碍。我们表示的合奏由一个随机矩阵，这是一个过渡的过渡叠加的系统动力学类的每个成员。我们提出了具体的结果来自布尔时间序列动力学的网络遵守强抑制规则，通过应用到几个传统的问题，网络动力学。我们表明，点吸引子的数量分布可以准确地估计与。我们展示了如何生成德里达图的基础上。我们表明，基于香农熵优于其他方法在选择实验，以进一步缩小网络结构。我们还概述了一个实验测试的预测的基础上。我们激励所有这些结果在一个流行的分子生物学布尔网络模型的酵母细胞周期，但我们介绍的方法和分析是一般的。我们的结论与开放的问题，例如，应用到其他模型，计算考虑时，扩大到更大的系统，和其他潜在的分析。
Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., for the yeast cell cycle process), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix , which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for derived from Boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with . We show how to generate Derrida plots based on . We show that -based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on . We motivate all of these results in terms of a popular molecular biology Boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for , for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.
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影响因子：
64.8
作者：
Alon, U;Surette, MG;Leibler, S
通讯作者：
Leibler, S
DOI：
10.1209/0295-5075/2/10/001
发表时间：
1986-11-15
期刊：
EUROPHYSICS LETTERS
影响因子：
--
作者：
DERRIDA, B;STAUFFER, D
通讯作者：
STAUFFER, D
影响因子：
2
作者：
GLASS, L;KAUFFMAN, SA
通讯作者：
KAUFFMAN, SA
影响因子：
56.9
作者：
Milo, R;Shen-Orr, S;Alon, U
通讯作者：
Alon, U
DOI：
10.1073/pnas.93.1.397
发表时间：
1996-01-09
影响因子：
11.1
作者：
Huynen, MA;Stadler, PF;Fontana, W
通讯作者：
Fontana, W