Reducing Boolean Networks with Backward Boolean Equivalence

TL;DR

The paper introduces Backward Boolean Equivalence, a novel reduction method for Boolean Networks that simplifies models while preserving key properties, enabling more efficient analysis of biological systems.

Contribution

It presents a new reduction technique called Backward Boolean Equivalence that preserves properties of Boolean Networks and enables analysis of larger models.

Findings

Effective reduction of Boolean Networks demonstrated on GINsim repository.

Reductions enable analysis of previously intractable models.

Method can be combined with existing reduction techniques.

Abstract

Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium to large BNs due to the state-space explosion problem. We propose a novel reduction technique called \emph{Backward Boolean Equivalence} (BBE), which preserves some properties of interest of BNs. In particular, reduced BNs provide a compact representation by grouping variables that, if initialized equally, are always updated equally. The resulting reduced state space is a subset of the original one, restricted to identical initialization of grouped variables. The corresponding trajectories of the original BN can be exactly restored. We show the effectiveness of BBE by performing a large-scale validation on the whole GINsim BN repository. In selected cases, we show how our method enables analyses that would be otherwise intractable. Our method complements, and can be combined with, other reduction methods found in the literature.