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

Sebastian Persson, Niek Welkenhuysen , Sviatlana Shashkova , Samuel Wiqvist, Patrick Reith , Gregor W. Schmidt , Umberto Picchini, Marija Cvijovic

Research output: Working paper/PreprintPreprint (in preprint archive)


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.
Original languageEnglish
Number of pages24
Publication statusPublished - 2021 Jul 2

Subject classification (UKÄ)

  • Probability Theory and Statistics


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