The basis of easy controllability in Boolean networks

TL;DR

This paper demonstrates that controlling biological Boolean networks to reach desired states is generally easy, as the number of nodes needed scales logarithmically with the number of attractors, supported by empirical and theoretical analysis.

Contribution

It provides a formal analysis and empirical evidence showing that controlling biological networks requires relatively few nodes, with a theoretical explanation for the observed scaling behavior.

Findings

Number of control nodes scales logarithmically with attractors

Empirical analysis of 49 biological network models supports this scaling

Theoretical framework classifies controlling nodes into three types

Abstract

Effective control of biological systems can often be achieved through the control of a surprisingly small number of distinct variables. We bring clarity to such results using the formalism of Boolean dynamical networks, analyzing the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics. Analyzing 49 existing biological network models, we find strong numerical evidence that the average number of nodes that must be forced scales logarithmically with the number of original attractors. This suggests that biological networks may be typically easy to control even when the number of interacting components is large. We provide a theoretical explanation of the scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes. We further identify characteristics of dynamics that can invalidate this scaling, and speculate about how this relates more broadly to non-biological systems.