CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks
CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks
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DOI：
10.3389/fphys.2018.01046
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发表时间：
2018-08-14
影响因子：
4
通讯作者：
Rocha, Luis M.
中科院分区：
文献类型：
--
作者：
Correia, Rion B.;Gates, Alexander J.;Rocha, Luis M.
逻辑模型提供了一种简单但强大的方法来理解生化调节的复杂动力学，而无需估计动力学参数。然而，即使是简单的自动机组件也可能导致集体动态，当聚合到网络中时，这种集体动态在计算上是难以处理的。在之前的工作中，我们证明了生化调节的自动机网络模型是高度渠道化的，其中许多变量状态及其分组是冗余的（Marques-Pita 和 Rocha，2013）。对此类运河的精确制图和测量简化了这些模型，甚至使非常大的网络也能进行分析。此外，运河化在布尔网络动力学的控制、鲁棒性、模块化和关键性中发挥着重要作用，特别是用于模拟生化调节的网络动力学（Gates and Rocha，2016；Gates et al.，2016；Manicka，2017）。在这里，我们描述了一个新的公开可用的 Python 包，它提供了提取、测量和可视化布尔网络模型中存在的运河冗余所需的工具。它提取了在这些模型中控制动力学最有效的路径，包括它们的有效图和动力学疏导图，以及发现最小控制变量集的其他工具。
Logical models offer a simple but powerful means to understand the complex dynamics of biochemical regulation, without the need to estimate kinetic parameters. However, even simple automata components can lead to collective dynamics that are computationally intractable when aggregated into networks. In previous work we demonstrated that automata network models of biochemical regulation are highly canalizing, whereby many variable states and their groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting and measurement of such canalization simplifies these models, making even very large networks amenable to analysis. Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation (Gates and Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new publicly-available Python package that provides the necessary tools to extract, measure, and visualize canalizing redundancy present in Boolean network models. It extracts the pathways most effective in controlling dynamics in these models, including their effective graph and dynamics canalizing map, as well as other tools to uncover minimum sets of control variables.