Network Entropy measures applied to different systemic perturbations of cell basal state

TL;DR

This paper introduces a network entropy measure to characterize cell states related to cancer and aging by integrating gene expression data and protein interaction networks, revealing insights into cellular plasticity and regulation.

Contribution

It presents a novel entropy-based approach to analyze biological networks at multiple scales, combining experimental data with biological knowledge for deeper system understanding.

Findings

Entropy correlates with cell plasticity and state

Method distinguishes different cell phenotypes

Applicable at various biological scales

Abstract

We characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression values and protein interaction networks. The entropy measure estimates the parameter space available to the network ensemble, that can be interpreted as the level of plasticity of the system for high entropy values (the ability to change its internal parameters, e.g. in response to environmental stimuli), or as a fine tuning of the parameters (that restricts the range of possible parameter values) in the opposite case. This approach can be applied at different scales, from whole cell to single biological functions, by defining appropriate subnetworks based on a priori biological knowledge, thus allowing a deeper understanding of the cell processes involved. In our analysis we used specific network features (degree sequence, subnetwork structure and distance between gene profiles) to obtain informations at different biological scales, providing a novel point of view for the integration of experimental transcriptomic data and a priori biological knowledge, but the entropy measure can also highlight other aspects of the biological systems studied depending on the constraints introduced in the model (e.g. community structures).