Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.
Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.
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DOI：
10.1186/1478-811x-11-43
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发表时间：
2013-06-26
期刊：
影响因子：
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通讯作者：
Klamt S
中科院分区：
文献类型：
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作者：
Samaga R;Klamt S
系统生物学的一个中心目标是构建生物分子网络的预测模型。中等规模的细胞网络已经成功地建立了基于微分方程的定量模型。然而，在大规模网络中，机械细节和动力学参数的知识往往是有限的，允许建立预测定量模型。在这里，我们回顾定性和半定量建模的细胞信号转导网络的方法。特别是，我们专注于三个不同的，但相关的形式主义促进建模的信令过程与不同层次的细节：交互图，逻辑/布尔网络，和基于逻辑的常微分方程（ODE）。尽管交互图是最简单的模型，但它允许识别重要的网络属性，如信令路径、反馈回路或全局相互依赖性。通过约束边的逻辑组合，可以从交互图中导出逻辑或布尔模型。逻辑模型可用于研究所研究系统的基本输入-输出行为，并通过离散模拟分析其定性动态特性。他们还提供了一个合适的框架，以确定适当的干预战略，强制或抑制某些行为。最后，作为第三种形式主义，布尔网络可以转换为基于逻辑的常微分方程，使研究的基本定量和动态特征的信号网络，其中时间和状态是连续的。我们描述和说明不同的建模形式主义的关键方法和应用，并讨论它们之间的关系。特别是，作为模型重用的一个重要方面，我们将展示如何将这三种建模方法结合到建模管道（或模型层次结构）中，从而允许从最简单的信令网络表示（交互图）开始，然后可以将其细化为逻辑模型，并最终细化为基于逻辑的ODE模型。重要的是，在粗糙表示中确定的系统和网络属性在这些变换期间是守恒的。
A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models. Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input–output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous. We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical and eventually to logic-based ODE models. Importantly, systems and network properties determined in the rougher representation are conserved during these transformations.