Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on their fixed points and attractors. We consider marker properties, which specify that some components are fixed to a specific value. We study 4 variants of the marker reprogramming problem: the reprogramming of fixed points, of minimal trap spaces, and of fixed points and minimal trap spaces reachable from a given initial configuration with the most permissive update mode. The perturbations consist of fixing a set of components to a fixed value. They can destroy and create new attractors. In each case, we give an upper bound on their theoretical computational complexity, and give an implementation of the resolution using the BoNesis Python framework. Finally, we lift the reprogramming problems to ensembles of BNs, as supported by BoNesis, bringing insight on possible and universal reprogramming strategies. This paper can be executed and modified interactively.

翻译：布尔网络（Boolean networks，简称BNs）是用于模拟细胞行为的离散动力系统。本文展示了如何利用BoNesis软件全面识别能够强制固定点与吸引子满足特定属性的扰动组合。我们研究了标记属性（即指定某些组分固定为特定值），并探讨了四种标记重编程问题变体：包含固定点重编程、最小陷阱空间重编程，以及采用最容许更新模式下从给定初始构型可达的固定点与最小陷阱空间重编程。扰动形式为固定一组组分至特定值，这些扰动可能破坏原有吸引子并生成新吸引子。针对每种情形，我们给出了理论计算复杂度的上界分析，并基于BoNesis Python框架实现了求解方法。最后，我们将重编程问题拓展至BoNesis支持的布尔网络系综研究，揭示了可行与通用重编程策略的深层规律。本文支持交互式执行与修改。