布尔网络中基于陷阱空间的节点和边控制策略识别
Node and edge control strategy identification via trap spaces in Boolean networks
摘要：
生物系统的控制机制的研究允许在生物工程和医学中的有趣应用，例如在细胞重编程或药物靶点识别中。控制策略通常由一组干预组成，通过固定某些组件的值，确保受控系统的长期动态处于期望状态。在布尔框架中，一种常见的控制方法是检查固定值如何通过网络传播，以确定干预措施的效果是否足以诱导目标状态。虽然基于值渗透的方法允许有效的计算，但它们可能会错过许多控制策略。另一方面，用于控制策略识别的穷举方法通常需要高计算成本。为了增加确定的控制策略的数量，同时仍然受益于一个有效的实施，我们介绍了一种基于值渗透的方法，该方法使用陷阱空间，相对于动态封闭的状态空间的子空间，通常可以很容易地计算在生物网络。该方法允许节点干预，固定某些组件的值，以及边缘干预，固定一个组件对另一个组件的影响。该方法是使用答案集编程实现的，扩展了现有的有效实现值渗透，允许使用陷阱空间和边缘控制。该方法的适用性研究不同的控制目标在生物案例研究，确定在所有情况下，新的控制策略，将逃脱通常的基于简化的方法。
英文摘要：
The study of control mechanisms of biological systems allows for interesting applications in bioengineering and medicine, for instance in cell reprogramming or drug target identification. A control strategy often consists of a set of interventions that, by fixing the values of some components, ensure that the long term dynamics of the controlled system is in a desired state. A common approach to control in the Boolean framework consists in checking how the fixed values propagate through the network, to establish whether the effect of percolating the interventions is sufficient to induce the target state. Although methods based uniquely on value percolation allow for efficient computation, they can miss many control strategies. Exhaustive methods for control strategy identification, on the other hand, often entail high computational costs. In order to increase the number of control strategies identified while still benefiting from an efficient implementation, we introduce a method based on value percolation that uses trap spaces, subspaces of the state space that are closed with respect to the dynamics, and that can usually be easily computed in biological networks. The approach allows for node interventions, which fix the value of certain components, and edge interventions, which fix the effect that one component has on another. The method is implemented using Answer Set Programming, extending an existing efficient implementation of value percolation to allow for the use of trap spaces and edge control. The applicability of the approach is studied for different control targets in a biological case study, identifying in all cases new control strategies that would escape usual percolation-based methods.
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