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

Persson, Sebastian ; Welkenhuysen, Niek ; Shashkova, Sviatlana ; Wiqvist, Samuel LU ; Reith, Patrick ; Schmidt, Gregor W. ; Picchini, Umberto and Cvijovic, Marija (2021)
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... (More)
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. (Less)
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author
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organization
publishing date
type
Working paper/Preprint
publication status
published
subject
pages
24 pages
publisher
bioRxiv
DOI
10.1101/2021.07.01.450748
language
English
LU publication?
yes
id
224fb752-996c-44b1-a151-a8151105afae
alternative location
https://www.biorxiv.org/content/10.1101/2021.07.01.450748v1
date added to LUP
2021-08-27 13:22:39
date last changed
2022-02-11 11:00:18
@misc{224fb752-996c-44b1-a151-a8151105afae,
  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.}},
  author       = {{Persson, Sebastian and Welkenhuysen, Niek and Shashkova, Sviatlana and Wiqvist, Samuel and Reith, Patrick and Schmidt, Gregor W. and Picchini, Umberto and Cvijovic, Marija}},
  language     = {{eng}},
  month        = {{07}},
  note         = {{Preprint}},
  publisher    = {{bioRxiv}},
  title        = {{PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models}},
  url          = {{http://dx.doi.org/10.1101/2021.07.01.450748}},
  doi          = {{10.1101/2021.07.01.450748}},
  year         = {{2021}},
}