Dynamical Methods for Target Control of Biological Networks

TL;DR

This paper compares graph-theoretic and mean-field methods for estimating node influence in biological networks, highlighting their performance, computational efficiency, and suitability based on network density.

Contribution

It provides a systematic comparison of state-of-the-art methods for target control in biological networks, revealing their relative strengths and limitations.

Findings

All methods underestimate true influence.

Mean-field approaches have higher recall but lower precision.

Graph-theoretic methods are faster in sparse networks.

Abstract

Estimating the influence that individual nodes have on one another in a Boolean network is essential to predict and control the system's dynamical behavior, for example, detecting key therapeutic targets to control pathways in models of biological signaling and regulation. Exact estimation is generally not possible due to the fact that the number of configurations that must be considered grows exponentially with the system size. However, approximate, scalable methods exist in the literature. These methods can be divided in two main classes: (i) graph-theoretic methods that rely on representations of Boolean dynamics into static graphs, (ii) and mean-field approaches that describe average trajectories of the system but neglect dynamical correlations. Here, we compare systematically the performance of these state-of-the-art methods on a large collection of real-world gene regulatory networks. We find comparable performance across methods. All methods underestimate the ground truth, with mean-field approaches having a better recall but a worse precision than graph-theoretic methods. Computationally speaking, graph-theoretic methods are faster than mean-field ones in sparse networks, but are slower in dense networks. The preference of which method to use, therefore, depends on a network's connectivity and the relative importance of recall vs. precision for the specific application at hand.