The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling
The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling
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DOI：
10.1073/pnas.2022598118
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发表时间：
2021-03-23
影响因子：
11.1
通讯作者：
Rocha, Luis M.
中科院分区：
文献类型：
--
作者：
Gates, Alexander J.;Correia, Rion Brattig;Rocha, Luis M.
绘制遗传控制和细胞信号传导背后的因果相互作用的能力，导致了调节细胞功能的复杂生化网络的日益准确的模型。这些网络模型为生物化学系统的组织、动力学和功能提供了深刻的见解：例如，通过揭示与疾病有关的遗传控制途径。然而，传统的表示生化网络作为二进制相互作用图未能准确地表示这些多变量系统的一个重要的动力学特征：一些途径传播控制信号比其他更有效。这种异质性的相互作用反映了渠道系统是强大的冗余途径的动态干预，但有效的途径的干预。在这里，我们介绍了有效图，一个加权图，捕捉生化网络调节，信号和控制中存在的非线性逻辑冗余。使用来自系统生物学的78个实验验证的模型，我们证明：1）冗余途径在生化调节的生物模型中普遍存在，2）有效图以因果图的形式提供了多变量动力学的概率但精确的表征，3）有效图提供了动力学扰动和控制信号的准确解释，例如由癌症药物治疗诱导的那些，在生物化学途径中传播。总的来说，我们的研究结果表明，有效的图形提供了一个丰富的描述网络的多变量因果相互作用的结构和动力学。我们证明，它提高了复杂的动力系统的可解释性，预测和控制，特别是生化调节。
The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization-the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.