Identification of biological regulatory networks from Process Hitting models
Identification of biological regulatory networks from Process Hitting models
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DOI：
10.1016/j.tcs.2014.12.002
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
M. Folschette;Loïc Paulevé;Katsumi Inoue;Morgan Magnin;O. Roux
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文献类型：
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作者：
M. Folschette;Loïc Paulevé;Katsumi Inoue;Morgan Magnin;O. Roux
定性形式主义提供了一个行之有效的替代更传统的微分方程模型的生物调控网络（BRN）。这些形式主义导致了许多理论著作和实践工具来理解新兴行为。然而，对非常大的模型的动态分析是一个相当困难的问题，这使得我们以前引入了进程命中框架（PH），这是一类特殊的非确定性异步自动机网络（或安全Petri网）。它的主要优点在于最近设计的几个静态分析的效率，以评估动态性能，使它能够处理非常大的models.In本文中，我们解决的形式识别的定性模型的BRN从PH模型。首先，从PH模型的相互作用图的推断总结了对动力学有效的组件之间的有符号影响。其次，我们提供了与给定PH兼容的BRN的所有René托马斯模型的推断。由于PH允许组件之间的非确定性相互作用的规范，我们的推断强调PH处理具有不完整相互作用知识的大型BRN的能力，其中，由于参数的组合，托马斯的方法失败了。使用答案集来实现相应的托马斯模型的推理编程，特别是允许（可能是许多）兼容参数化的有效枚举。
Qualitative formalisms offer a well-established alternative to the more traditionally used differential equation models of Biological Regulatory Networks (BRNs). These formalisms led to numerous theoretical works and practical tools to understand emerging behaviors. The analysis of the dynamics of very large models is however a rather hard problem, which led us to previously introduce the Process Hitting framework (PH), which is a particular class of nondeterministic asynchronous automata network (or safe Petri nets). Its major advantage lies in the efficiency of several static analyses recently designed to assess dynamical properties, making it possible to tackle very large models.In this paper, we address the formal identification of qualitative models of BRNs from PH models. First, the inference of the Interaction Graph from a PH model summarizes the signed influences between the components that are effective for the dynamics. Second, we provide the inference of all René Thomas models of BRNs that are compatible with a given PH. As the PH allows the specification of nondeterministic interactions between components, our inference emphasizes the ability of PH to deal with large BRNs with incomplete knowledge on interactions, where Thomas' approach fails because of the combinatorics of parameters.The inference of corresponding Thomas models is implemented using Answer Set Programming, which allows in particular an efficient enumeration of (possibly numerous) compatible parameterizations.