Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms

Wiqvist, Samuel; Golightly, Andrew; McLean, Ashleigh T.; Picchini, Umberto

doi:10.1016/j.csda.2020.107151

Cited by 21 publications

(37 citation statements)

References 34 publications

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“…The feasibility of this approach is an area of future research. Unlike Wiqvist et al (2019), we have not explored different strategies to update the random numbers; the approach we use in our example in Sections 5-7 most closely follows their naive Gibbs approach.…”

Section: Discussionmentioning

confidence: 99%

“…To further improve efficiency, we exploit bridge proposals in the particle filter rather than proposing directly from the (approximate) transition density as in the standard bootstrap filter used by Wiqvist et al (2019). By including the IAPM and MPM methods, our paper provides a more comprehensive suite of particle methods for application to general state-space SDEMEMs.…”

Section: Discussionmentioning

confidence: 99%

“…By including the IAPM and MPM methods, our paper provides a more comprehensive suite of particle methods for application to general state-space SDEMEMs. Wiqvist et al (2019) allow the number of particles to vary between individuals, which is also straightforward to implement in our methods; see also Tran et al (2013).…”

Section: Discussionmentioning

confidence: 99%

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Particle Methods for Stochastic Differential Equation Mixed Effects Models

2021

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Parameter inference for stochastic differential equation mixed effects models (SDEMEMs) is challenging. Analytical solutions for these models are rarely available, which means that the likelihood is also intractable. In this case, exact inference (up to the discretisation of the stochastic differential equation) is possible using particle MCMC methods. Although the exact posterior is targeted by these methods, a naive implementation for SDEMEMs can be highly inefficient. Our article develops three extensions to the naive approach which exploit specific aspects of SDEMEMs and other advances such as correlated pseudo-marginal methods. We compare these methods on simulated data and data from a tumour xenography study on mice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Particle Methods for Stochastic Differential Equation Mixed Effects Models

2021

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“…More formally, PEPSDI produces Bayesian inference for state-space models with latent dynamics incorporating mixed-effects, that is state-space mixed-effects model (SSMEM). It builds upon the schemes previously proposed for SDEMEMs [10].…”

Section: Inference Framework For Stochastic Dynamic Single-cell Modelsmentioning

confidence: 99%

“…However, in practice, steps 1-2 cannot trivially be sampled from, due to the intractability of the likelihood for the i-th individual π(y (i) |κ, ξ, c (i) ), and here sampling is performed using a pseudo-marginal approach following [10]. The posterior targeted in step 3 is tractable, and thus η is sampled using Hamiltonian Monte Carlo [17,53,54].…”

Section: Stochastic Simulationsmentioning

confidence: 99%

PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models

Persson

Welkenhuysen

Shashkova

et al. 2021

Preprint

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Mathematical modelling is an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic and extrinsic noise. Here we present PEPSDI, a scalable and flexible framework for Bayesian inference in state-space mixed-effects stochastic dynamic single-cell models. Unlike previous frameworks, PEPSDI imposes a few modelling assumptions when inferring unknown model parameters from time-lapse data. Specifically, it can infer model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. This allowed us to identify hexokinase activity as a source of extrinsic noise, and to deduce that sugar availability dictates cell-to-cell variability in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway.

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