Quantifying the total effect of edge interventions in discrete multistate networks
Quantifying the total effect of edge interventions in discrete multistate networks
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DOI：
10.1016/j.automatica.2020.109453
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
D. Murrugarra;Elena S. Dimitrova
中科院分区：
文献类型：
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作者：
D. Murrugarra;Elena S. Dimitrova
开发有效的计算方法来评估外部干预对网络模型动力学的影响是系统生物学中的一个重要问题。本文着重于量化的全球变化，从应用的干预措施，以产生预期的效果，我们定义为干预的总效果。我们将考虑的数学模型类型是离散动力系统，包括广泛使用的布尔网络及其推广。潜在的干预可以由一组节点和边表示，可以操纵这些节点和边以在系统上产生期望的效果。我们使用一类称为嵌套渠道化函数的监管规则，这些规则经常出现在已发表的模型中，并受到进化生物学中渠道化概念的启发。本文基于调节函数的渠化性质，给出了一种多项式规范形。使用这个多项式范式，我们给出了一组公式计算的最大数量的转换，将改变在状态空间的边删除后的布线图。这些公式依赖于目标函数的管道化结构，因为改变的转换的数量取决于包括要删除的输入的管道化层。我们还提出了随机网络的计算，以比较我们的公式提供的上限的变化的确切数量。最后，我们提供了统计的锐度，这些上界在随机网络。
Developing efficient computational methods to assess the impact of external interventions on the dynamics of a network model is an important problem in systems biology. This paper focuses on quantifying the global changes that result from the application of an intervention to produce a desired effect, which we define as thetotal effectof the intervention. The type of mathematical models that we will consider are discrete dynamical systems which include the widely used Boolean networks and their generalizations. The potential interventions can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. We use a class of regulatory rules called nested canalizing functions that frequently appear in published models and were inspired by the concept of canalization in evolutionary biology. In this paper, we provide a polynomial normal form based on the canalizing properties of regulatory functions. Using this polynomial normal form, we give a set of formulas for counting the maximum number of transitions that will change in the state space upon an edge deletion in the wiring diagram. These formulas rely on the canalizing structure of the target function since the number of changed transitions depends on the canalizing layer that includes the input to be deleted. We also present computations on random networks to compare the exact number of changes with the upper bounds provided by our formulas. Finally, we provide statistics on the sharpness of these upper bounds in random networks.