Bayesian inference for stochastic oscillatory systems using the phase-corrected Linear Noise Approximation

TL;DR

This paper introduces a new Bayesian inference method for stochastic oscillatory systems using phase-corrected Linear Noise Approximation, enabling realistic parameter estimation in complex biological and chemical models.

Contribution

The paper develops a novel inference approach for stochastic oscillatory systems leveraging phase correction and analytical approximations, improving feasibility for complex models.

Findings

Parameter sensitivity analysis predicts practical identifiability.

Parallel tempering MCMC outperforms other algorithms.

Method accurately estimates parameters from simulated data.

Abstract

Likelihood-based inference in stochastic non-linear dynamical systems, such as those found in chemical reaction networks and biological clock systems, is inherently complex and has largely been limited to small and unrealistically simple systems. Recent advances in analytically tractable approximations to the underlying conditional probability distributions enable long-term dynamics to be accurately modelled, and make the large number of model evaluations required for exact Bayesian inference much more feasible. We propose a new methodology for inference in stochastic non-linear dynamical systems exhibiting oscillatory behaviour and show the parameters in these models can be realistically estimated from simulated data. Preliminary analyses based on the Fisher Information Matrix of the model can guide the implementation of Bayesian inference. We show that this parameter sensitivity analysis can predict which parameters are practically identifiable. Several Markov chain Monte Carlo algorithms are compared, with our results suggesting a parallel tempering algorithm consistently gives the best approach for these systems, which are shown to frequently exhibit multi-modal posterior distributions.