PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/224fb752-996c-44b1-a151-a8151105afae
- author
- Persson, Sebastian ; Welkenhuysen, Niek ; Shashkova, Sviatlana ; Wiqvist, Samuel LU ; Reith, Patrick ; Schmidt, Gregor W. ; Picchini, Umberto and Cvijovic, Marija
- organization
- publishing date
- 2021-07-02
- 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}}, }