An integrative approach to modeling biological networks

TL;DR

This paper presents an integrated method combining multiple network properties and probabilistic techniques to identify the most suitable models for biological networks, particularly showing geometric random graphs fit protein interaction and structure networks well.

Contribution

The paper introduces a novel approach that combines various network properties with probabilistic methods to evaluate and select models for biological networks, addressing limitations of previous heuristics.

Findings

Geometric random graphs best fit residue interaction graphs (RIGs).

The approach successfully models noisy PPI networks.

Integrating multiple properties improves model assessment reliability.

Abstract

Since proteins carry out biological processes by interacting with other proteins, analyzing the structure of protein-protein interaction (PPI) networks could explain complex biological mechanisms, evolution, and disease. Similarly, studying protein structure networks, residue interaction graphs (RIGs), might provide insights into protein folding, stability, and function. The first step towards understanding these networks is finding an adequate network model that closely replicates their structure. Evaluating the fit of a model to the data requires comparing the model with real-world networks. Since network comparisons are computationally infeasible, they rely on heuristics, or "network properties." We show that it is difficult to assess the reliability of the fit of a model with any individual network property. Thus, our approach integrates a variety of network properties and further combines these with a series of probabilistic methods to predict an appropriate network model for biological networks. We find geometric random graphs, that model spatial relationships between objects, to be the best-fitting model for RIGs. This validates the correctness of our method, since RIGs have previously been shown to be geometric. We apply our approach to noisy PPI networks and demonstrate that their structure is also consistent with geometric random graphs.