CANA：用于布尔网络中控制和通道化量化的一个Python包
CANA: A python package for quantifying control and canalization in Boolean Networks
AI总结：
本文介绍CANA包，用于量化布尔网络中的控制与通道化，通过提取和可视化通道化冗余，简化模型分析。
摘要：
逻辑模型提供了一种简单但强大的方法来理解生物调节复杂动态，无需估计速率参数。然而，即使简单的自动机组件在聚合为网络时也可能导致计算上不可行的集体动态。在以前的工作中，我们证明了生物调节自动机网络模型高度通道化，其中许多变量状态及其分组是冗余的（Marques-Pita和Rocha，2013）。精确绘制和测量此类通道化简化了这些模型，使即使非常大的网络也适合分析。此外，通道化在布尔网络动态的控制、鲁棒性、模块性和临界性中起重要作用，特别是用于建模生物调节的动态（Gates和Rocha，2016；Gates等，2016；Manicka，2017）。本文描述了一个新的公开可用的Python包，提供了提取、测量和可视化布尔网络模型中通道化冗余的必要工具。它提取了这些模型中控制动态最有效的路径，包括其有效图和动态通道化图，以及其他揭示最小控制变量集的工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
