Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis

TL;DR

This paper presents a software framework, BoNesis, for exhaustively identifying perturbations in Boolean networks that enforce specific properties on their fixed points and attractors, including reprogramming strategies for ensembles of networks.

Contribution

The paper introduces a novel approach using BoNesis to reprogram Boolean networks and their ensembles, providing theoretical bounds and an interactive implementation.

Findings

Successfully identifies perturbations that enforce marker properties.

Provides upper bounds on computational complexity of reprogramming tasks.

Demonstrates reprogramming strategies applicable to network ensembles.

Abstract

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.