Boolean Networks as Predictive Models of Emergent Biological Behaviors

TL;DR

Boolean networks serve as a versatile modeling approach for understanding and predicting emergent behaviors in complex biological systems across various organizational levels.

Contribution

This paper provides a comprehensive overview of constructing, analyzing, and validating Boolean network models for biological systems, highlighting their predictive and control capabilities.

Findings

Boolean networks can predict system responses to perturbations.

Structural and dynamical properties are key to understanding biological behaviors.

Case studies demonstrate applicability across biological scales.

Abstract

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e.g., the unknown values of kinetic parameters for biochemical reactions). Boolean networks have emerged as a powerful tool for modeling these systems. We provide a methodological overview of Boolean network models of biological systems. After a brief introduction, we describe the process of building, analyzing, and validating a Boolean model. We then present the use of the model to make predictions about the system's response to perturbations and about how to control (or at least influence) its behavior. We emphasize the interplay between structural and dynamical properties of Boolean networks and illustrate them in three case studies from disparate levels of biological organization.