布尔调节函数集上的偏序
Partial Order on the set of Boolean Regulatory Functions
摘要：
逻辑模型已成功地用于描述监管和信号网络，而不需要定量数据。然而，现有的数据不足以充分定义一个独特的模型，使得给定模型的参数化成为一项困难的任务。在这里，我们专注于与给定的监管结构，即所有单调的非退化布尔函数的集合兼容的布尔函数集的表征。然后，我们提出了一套原始的规则，以本地探索在这个集合中的任何函数的直接相邻函数，而不显式地生成整个集合。此外，我们提供的监管职能和相应的动态之间的关系。最后，我们通过重新审视Mendoza和Xenarios的T辅助细胞分化模型的概率布尔网络来说明这种方法的有用性。
英文摘要：
Logical models have been successfully used to describe regulatory and signaling networks without requiring quantitative data. However, existing data is insufficient to adequately define a unique model, rendering the parametrization of a given model a difficult task. Here, we focus on the characterization of the set of Boolean functions compatible with a given regulatory structure, i.e. the set of all monotone nondegenerate Boolean functions. We then propose an original set of rules to locally explore the direct neighboring functions of any function in this set, without explicitly generating the whole set. Also, we provide relationships between the regulatory functions and their corresponding dynamics. Finally, we illustrate the usefulness of this approach by revisiting Probabilistic Boolean Networks with the model of T helper cell differentiation from Mendoza & Xenarios.
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