论文标题
通过布尔网络中的陷阱空间进行控制策略识别
Control Strategy Identification via Trap Spaces in Boolean Networks
论文作者
论文摘要
对生物系统的控制提出了有趣的应用，例如细胞重编程或药物靶标识别。一种常见的控制策略包括一组干预措施，这些干预措施通过固定某些变量的值，迫使系统发展到所需的状态。这项工作提出了一种新的方法，可以在布尔网络建模的生物系统中找到控制策略。在这种情况下，我们探讨了陷阱空间的属性，即动态无法离开的状态空间的子空间。通常可以有效地计算生物网络的陷阱空间，并提供有用的吸引盆地近似值。我们的方法为目标表型提供了控制策略，该策略基于干预措施，该干预措施最终可以发布控制。此外，我们的方法可以结合有关吸引子的信息，以找到可以避免基于渗透方法的新控制策略。我们展示了方法对两个细胞命运决策模型的适用性。
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.
