Networking genetic regulation and neural computation: Directed network topology and its effect on the dynamics

TL;DR

This paper compares the topological and dynamic effects of directed network structures in transcriptional regulation and neural networks, revealing distinct influence distributions and responses to vertex removal.

Contribution

It provides a comparative analysis of directed network topologies and their impact on dynamics, highlighting differences in influence distribution and robustness.

Findings

Transcriptional networks have few influential vertices, limiting impact of random removal.

Neural networks exhibit high influence across most vertices, but localize changes upon removal.

Network topology affects the system's robustness and dynamic response to vertex removal.

Abstract

Two different types of directed networks are investigated, transcriptional regulation networks and neural networks. The directed network structure are studied and also shown to reflect the different processes taking place on the networks. The distribution of influence, identified as the the number of downstream vertices, are used as a tool for investigating random vertex removal. In the transcriptional regulation networks we observe that only a small number of vertices have a large influence. The small influences of most vertices limit the effect of a random removal to in most cases only a small fraction of vertices in the network. The neural network has a rather different topology with respect to the influence, which are large for most vertices. To further investigate the effect of vertex removal we simulate the biological processes taking place on the networks. Opposed to the presumpted large effect of random vertex removal in the neural network, the high density of edges in conjunction with the dynamics used makes the change in the state of the system to be highly localized around the removed vertex.