Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions
Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions
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DOI：
10.1002/wsbm.1273
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发表时间：
2014-09-01
影响因子：
7.9
通讯作者：
Thakar, Juilee
中科院分区：
文献类型：
--
作者：
Albert, Reka;Thakar, Juilee
细胞内或细胞附近的生物分子形成一个复杂的相互作用的系统。细胞的表型和行为产生于这个系统的组成部分之间的相互作用。建模相互作用的生物分子系统的有效方法是基于网络的动态模型，该模型通过状态变量表征每个组件，并描述由于系统中相互作用而导致的状态变量的变化。动态模型可以捕获这个相互作用系统的稳定状态模式，并可以将它们与不同的细胞命运或行为联系起来。布尔或逻辑模型通过一个二元状态变量来表征每个生物分子，该状态变量将该分子的丰度与下游过程所需的阈值丰度联系起来。该状态变量的规律以参数自由的方式描述，使得布尔建模成为动力学参数尚未确定的系统的一个实用选择。布尔模型整合了关于生物分子系统的组成和相互作用的知识体系，并捕获了系统的动态曲目，例如多种细胞命运的存在。这些模型被用于各种系统，并导致了重要的见解和预测。布尔模型是一种有效的探索性模型，是后续实验的指导，也是建立更多定量模型的基础。(C) 2014 Wiley期刊公司
The biomolecules inside or near cells form a complex interacting system. Cellular phenotypes and behaviors arise from the totality of interactions among the components of this system. A fruitful way of modeling interacting biomolecular systems is by network-based dynamic models that characterize each component by a state variable, and describe the change in the state variables due to the interactions in the system. Dynamic models can capture the stable state patterns of this interacting system and can connect them to different cell fates or behaviors. A Boolean or logic model characterizes each biomolecule by a binary state variable that relates the abundance of that molecule to a threshold abundance necessary for downstream processes. The regulation of this state variable is described in a parameter free manner, making Boolean modeling a practical choice for systems whose kinetic parameters have not been determined. Boolean models integrate the body of knowledge regarding the components and interactions of biomolecular systems, and capture the system's dynamic repertoire, for example the existence of multiple cell fates. These models were used for a variety of systems and led to important insights and predictions. Boolean models serve as an efficient exploratory model, a guide for follow-up experiments, and as a foundation for more quantitative models. (C) 2014 Wiley Periodicals, Inc.