Correia Rion B, Gates Alexander J, Wang Xuan, Rocha Luis M
School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.
CAPES Foundation, Ministry of Education of Brazil, Brasília, Brazil.
Front Physiol. 2018 Aug 14;9:1046. doi: 10.3389/fphys.2018.01046. eCollection 2018.
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 and , as well as other tools to uncover minimum sets of control variables.
逻辑模型提供了一种简单却强大的方法来理解生化调节的复杂动态，而无需估计动力学参数。然而，即使是简单的自动机组件，在聚合成网络时也可能导致集体动态在计算上难以处理。在之前的工作中，我们证明了生化调节的自动机网络模型具有高度的渠道化特性，即许多可变状态及其分组是冗余的（马克斯 - 皮塔和罗查，2013年）。对这种渠道化进行精确的描绘和测量可以简化这些模型，使得即使是非常大的网络也便于分析。此外，渠道化在布尔网络动态的控制、稳健性、模块化和临界性方面发挥着重要作用，尤其是那些用于模拟生化调节的布尔网络（盖茨和罗查，2016年；盖茨等人，2016年；马尼卡，2017年）。在此，我们描述了一个新的公开可用的Python包，它提供了必要的工具来提取、测量和可视化布尔网络模型中存在的渠道化冗余。它提取了在控制这些模型动态方面最有效的途径，包括它们的 和 ，以及其他用于揭示最小控制变量集的工具。
