Synthesis of Boolean Networks from Biological Dynamical Constraints using Answer-Set Programming

TL;DR

This paper presents a method to synthesize Boolean networks from biological dynamical constraints using Answer-Set Programming, enabling modeling of cellular processes with scalable solutions for large networks.

Contribution

It introduces a novel ASP-based approach for Boolean network synthesis from dynamical constraints, providing a complete characterization of solutions and demonstrating scalability.

Findings

Successfully models cellular differentiation processes.

Scales to networks with up to 1,000 nodes.

Provides a complete set of solutions without redundancy.

Abstract

Boolean networks model finite discrete dynamical systems with complex behaviours. The state of each component is determined by a Boolean function of the state of (a subset of) the components of the network. This paper addresses the synthesis of these Boolean functions from constraints on their domain and emerging dynamical properties of the resulting network. The dynamical properties relate to the existence and absence of trajectories between partially observed configurations, and to the stable behaviours (fixpoints and cyclic attractors). The synthesis is expressed as a Boolean satisfiability problem relying on Answer-Set Programming with a parametrized complexity, and leads to a complete non-redundant characterization of the set of solutions. Considered constraints are particularly suited to address the synthesis of models of cellular differentiation processes, as illustrated on a case study. The scalability of the approach is demonstrated on random networks with scale-free structures up to 100 to 1,000 nodes depending on the type of constraints.