Parsimonious computational inference protocol for Boolean networks: Application to osteogenesis

TL;DR

This paper introduces a systematic method to refine Boolean network models of genetic regulation, removing biologically implausible behaviors while preserving known attractors, demonstrated on osteogenesis regulation.

Contribution

A novel computational framework that filters and refines Boolean network models to ensure biological relevance and dynamical stability.

Findings

Effectively reduced candidate models from 51,138 to 6 highly consistent with biological data.

Successfully applied the method to a 9-node osteogenesis regulation network.

Produced parsimonious models that align with current biological understanding.

Abstract

Boolean networks are powerful mathematical tools for modeling the qualitative dynamics of genetic regulation. Yet inferred models often generate spurious attractors that lack biological viability. In this paper, we propose a parsimonious computational framework to systematically refine Boolean network models by eliminating these non-biological asymptotic behaviors while strictly preserving known, biologically relevant attractors. Through an exhaustive exploration of local function substitutions, we generate a comprehensive set of candidate models. To identify the most biologically consistent networks, we implement an incremental pruning protocol that filters candidates based on structural interaction digraph similarity, attraction basin topological organization, trajectorial isomorphism, and the minimization of dynamical instability and frustration. We apply this methodology to a 9-node genetic control model of the osteogenesis regulation network. Our protocol effectively evaluates a syntactic search space of 51,138 potential networks, ultimately narrowing them down to a robust family of 6 parsimonious models that are fully compatible with current biological knowledge.