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Bayesian inference for duplication-mutation with complementarity network models.

Ajay Jasra1, Adam Persing2, Alexandros Beskos2

  • 11 Department of Statistics & Applied Probability, National University of Singapore , Singapore, Singapore .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 11, 2015
PubMed
Summary
This summary is machine-generated.

This study infers protein-protein interaction (PPI) network evolution using the duplication-mutation with complementarity (DMC) model. Bayesian inference with a particle marginal Metropolis-Hastings (PMMH) algorithm accurately estimates model parameters.

Keywords:
duplication–mutation with complementarity (DMC) modelparticle marginal Metropolis–Hastings (PMMH)protein–protein interaction (PPI) networksequential Monte Carlo (SMC)

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Area of Science:

  • Computational Biology
  • Network Science
  • Bioinformatics

Background:

  • Protein-protein interaction (PPI) networks are crucial for cellular functions.
  • Understanding the evolutionary processes that shape PPI networks is a key challenge.
  • The duplication-mutation with complementarity (DMC) model is a proposed mechanism for PPI network evolution.

Purpose of the Study:

  • To develop a Bayesian inference framework for estimating parameters of the DMC model.
  • To apply this framework to infer the evolutionary history of observed PPI networks.
  • To validate the accuracy and precision of the proposed inference methodology.

Main Methods:

  • Representing PPI networks as undirected graphs.
  • Utilizing a binary forest to capture the duplication history of networks.
  • Establishing a posterior density for DMC model parameters.
  • Implementing a particle marginal Metropolis-Hastings (PMMH) algorithm for Bayesian inference.

Main Results:

  • Accurate and precise inference of DMC model parameters, including mutation and homodimerization rates.
  • Demonstrated effectiveness of the PMMH sampling strategy on numerical examples.
  • Successful application of the methodology to infer evolutionary parameters from network data.

Conclusions:

  • The developed Bayesian inference framework provides a robust method for studying PPI network evolution under the DMC model.
  • The PMMH algorithm is effective for parameter estimation in this context.
  • This work offers valuable tools for understanding the evolutionary dynamics of biological networks.