论文标题
用Bonesis的大多数宽松布尔网络和合奏的标记和源标记重编程
Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis
论文作者
论文摘要
布尔网络（BNS）是离散的动态系统，具有用于蜂窝行为建模的应用。在本文中，我们演示了如何使用软件玻璃体来详尽地识别扰动的组合，从而在其固定点和吸引子上实现属性。我们考虑标记属性，该属性指定某些组件固定为特定值。我们研究标记重编程问题的4个变体：固定点的重编程，最小陷阱空间以及固定点和最小陷阱空间的重新编程，可从给定的初始配置（具有最宽松的更新模式）到达。扰动包括将一组组件固定为固定值。他们可以摧毁并创建新的吸引者。在每种情况下，我们都会在其理论计算复杂性上给出一个上限，并使用Bonesis Python框架对分辨率进行实现。最后，我们将重编程问题提升为Bonesis的支持，将重新编程的问题带到BNS的集合，从而深入了解可能的和普遍的重编程策略。本文可以进行交互执行和修改。
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.
