布尔网络中基于陷阱空间的控制策略辨识
Control Strategy Identification via Trap Spaces in Boolean Networks
摘要：
生物系统的控制提出了有趣的应用，如细胞重编程或药物靶点识别。一种常见的控制策略是一组干预措施，通过固定某些变量的值，迫使系统发展到期望的状态。这项工作提出了一种新的方法，在布尔网络建模的生物系统中寻找控制策略。在这种情况下，我们探索的陷阱空间，子空间的状态空间的动力学不能离开的属性。生物网络的陷阱空间通常可以有效地计算，并提供有用的近似吸引盆地。我们的方法为目标表型提供了控制策略，这些策略基于允许最终释放控制的干预措施。此外，我们的方法可以将吸引子的信息，以找到新的控制策略，将逃脱通常的基于简化的方法。我们展示了我们的方法的适用性，两个细胞命运决策模型。
英文摘要：
The control of biological systems presents interesting applications such as cell reprogramming or drug target identification. A common type of control strategy consists in a set of interventions that, by fixing the values of some variables, force the system to evolve to a desired state. This work presents a new approach for finding control strategies in biological systems modeled by Boolean networks. In this context, we explore the properties of trap spaces, subspaces of the state space which the dynamics cannot leave. Trap spaces for biological networks can often be efficiently computed, and provide useful approximations of attraction basins. Our approach provides control strategies for a target phenotype that are based on interventions that allow the control to be eventually released. Moreover, our method can incorporate information about the attractors to find new control strategies that would escape usual percolation-based methods. We show the applicability of our approach to two cell fate decision models.
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