Bayesian Inference for Duplication-Mutation with Complementarity Network Models

TL;DR

This paper develops a Bayesian inference framework for the duplication-mutation with complementarity (DMC) model of protein-protein interaction networks, using a particle marginal Metropolis-Hastings algorithm to estimate model parameters with high accuracy.

Contribution

It introduces a Bayesian approach with a novel sampling strategy for inferring DMC model parameters from network data and duplication history.

Findings

High accuracy in parameter inference demonstrated on numerical examples

Effective Bayesian sampling strategy using PMMH algorithm

Accurate estimation of mutation and homodimerization parameters

Abstract

We observe an undirected graph $G$ without multiple edges and self-loops, which is to represent a protein-protein interaction (PPI) network. We assume that $G$ evolved under the duplication-mutation with complementarity (DMC) model from a seed graph, $G_0$, and we also observe the binary forest $\Gamma$ that represents the duplication history of $G$. A posterior density for the DMC model parameters is established, and we outline a sampling strategy by which one can perform Bayesian inference; that sampling strategy employs a particle marginal Metropolis-Hastings (PMMH) algorithm. We test our methodology on numerical examples to demonstrate a high accuracy and precision in the inference of the DMC model's mutation and homodimerization parameters.