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bioRxiv · 10.1101/2021.07.01.450748

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

Persson, S.·Welkenhuysen, N.·Shashkova, S.·Wiqvist, S.·Reith, P.·Schmidt, G. W.·Picchini, U.·Cvijovic, M.
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Abstract

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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Persson, S., Welkenhuysen, N., Shashkova, S., Wiqvist, S., Reith, P., Schmidt, G. W., Picchini, U., Cvijovic, M.. 2021-07-02. PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models. https://doi.org/10.1101/2021.07.01.450748

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