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, G0, and we also observe the binary forest Γ 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.
Highlights
A s a result of breakthroughs in biotechnology and high-throughput experiments thousands of regulatory and protein–protein interactions have been revealed, and genome-wide protein–protein interaction (PPI) data are available
We assume that G evolved under the duplication–mutation with complementarity (DMC) model from a seed graph, G0, and we observe the binary forest G that represents the duplication history of G
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
Summary
A s a result of breakthroughs in biotechnology and high-throughput experiments thousands of regulatory and protein–protein interactions have been revealed, and genome-wide protein–protein interaction (PPI) data are available. To gain a better understanding of why these interactions take place, it is necessary to view them from an evolutionary perspective. The evolutionary history of PPI networks can help answer many questions about how present-day networks have evolved and provide valuable insight into molecular mechanisms of network growth (Kreimer et al, 2008; Pereira-Leal et al, 2007). Inferring network evolution history is a statistical and computational challenging problem as PPI networks of extant organisms provide only snapshots in time of the network evolution. The main growth mechanism of PPI network is gene duplication and divergence (mutations) (Wagner, 2001); all proteins in a family evolve from a common ancestor through gene duplications and mutations, and the protein network reflects the entire history of the JASRA ET AL
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