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A practical guide to pseudo-marginal methods for computational inference in systems biology.

David J Warne1, Ruth E Baker2, Matthew J Simpson1

  • 1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland 4001, Australia.

Journal of Theoretical Biology
|April 1, 2020
PubMed
Summary

This study introduces the pseudo-marginal approach for parameter inference in systems biology models. It offers exact inference and uncertainty quantification for complex biochemical reaction networks.

Keywords:
Bayesian inferenceBiochemical reaction networksMarkov chain Monte CarloPseudo-marginal methodsStochastic differential equations

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Statistical Inference

Background:

  • Exact parameter inference is often intractable for stochastic models in systems biology, like biochemical reaction networks.
  • Likelihood-free methods, such as approximate Bayesian computation, are used when likelihood functions are intractable but simulations are feasible.
  • Approximate Bayesian computation's accuracy can be sensitive to threshold and discrepancy function choices.

Purpose of the Study:

  • To provide a practical introduction to the pseudo-marginal approach for likelihood-free inference.
  • To demonstrate its application in parameter inference for biochemical reaction networks.
  • To highlight its advantages for noisy, partially observed, time-course data.

Main Methods:

  • Utilizes a Monte Carlo estimate of the likelihood function.
  • Combines pseudo-marginal approach with particle filters for efficient, high-accuracy likelihood estimation.
  • Presents case studies and implementations in the Julia programming language.

Main Results:

  • The pseudo-marginal approach facilitates exact inference and uncertainty quantification.
  • It offers advantages over approximate Bayesian computation for complex biological systems.
  • Efficient combination with particle filters enables low-variance, high-accuracy likelihood estimation.

Conclusions:

  • The pseudo-marginal approach is a powerful tool for parameter inference in systems biology.
  • It addresses limitations of existing likelihood-free methods, particularly for complex network models.
  • Practical implementations are provided for accessibility and further research.