An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks

TL;DR

This paper introduces a novel network reduction method for large discrete dynamic networks that identifies stable activity patterns and predicts attractors, aiding understanding of complex biological systems.

Contribution

The authors present a topological criterion-based reduction approach that can predict the dynamical repertoire of nodes in large networks, improving analysis efficiency.

Findings

Successfully applied to a cytotoxic T cell cancer model

Effective in predicting attractors in random Boolean networks

Reduces network complexity while preserving key dynamics

Abstract

Discrete dynamic models are a powerful tool for the understanding and modeling of large biological networks. Although a lot of progress has been made in developing analysis tools for these models, there is still a need to find approaches that can directly relate the network structure to its dynamics. Of special interest is identifying the stable patterns of activity, i.e., the attractors of the system. This is a problem for large networks, because the state space of the system increases exponentially with network size. In this work we present a novel network reduction approach that is based on finding network motifs that stabilize in a fixed state. Notably, we use a topological criterion to identify these motifs. Specifically, we find certain types of strongly connected components in a suitably expanded representation of the network. To test our method we apply it to a dynamic network model for a type of cytotoxic T cell cancer and to an ensemble of random Boolean networks of size up to 200. Our results show that our method goes beyond reducing the network and in most cases can actually predict the dynamical repertoire of the nodes (fixed states or oscillations) in the attractors of the system.