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

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of s...

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Autores principales: Persson, Sebastian, Welkenhuysen, Niek, Shashkova, Sviatlana, Wiqvist, Samuel, Reith, Patrick, Schmidt, Gregor W., Picchini, Umberto, Cvijovic, Marija
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159578/
https://www.ncbi.nlm.nih.gov/pubmed/35588132
http://dx.doi.org/10.1371/journal.pcbi.1010082
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author Persson, Sebastian
Welkenhuysen, Niek
Shashkova, Sviatlana
Wiqvist, Samuel
Reith, Patrick
Schmidt, Gregor W.
Picchini, Umberto
Cvijovic, Marija
author_facet Persson, Sebastian
Welkenhuysen, Niek
Shashkova, Sviatlana
Wiqvist, Samuel
Reith, Patrick
Schmidt, Gregor W.
Picchini, Umberto
Cvijovic, Marija
author_sort Persson, Sebastian
collection PubMed
description Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers 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. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.
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spelling pubmed-91595782022-06-02 Scalable and flexible inference framework for stochastic dynamic single-cell models Persson, Sebastian Welkenhuysen, Niek Shashkova, Sviatlana Wiqvist, Samuel Reith, Patrick Schmidt, Gregor W. Picchini, Umberto Cvijovic, Marija PLoS Comput Biol Research Article Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers 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. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability. Public Library of Science 2022-05-19 /pmc/articles/PMC9159578/ /pubmed/35588132 http://dx.doi.org/10.1371/journal.pcbi.1010082 Text en © 2022 Persson et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Persson, Sebastian
Welkenhuysen, Niek
Shashkova, Sviatlana
Wiqvist, Samuel
Reith, Patrick
Schmidt, Gregor W.
Picchini, Umberto
Cvijovic, Marija
Scalable and flexible inference framework for stochastic dynamic single-cell models
title Scalable and flexible inference framework for stochastic dynamic single-cell models
title_full Scalable and flexible inference framework for stochastic dynamic single-cell models
title_fullStr Scalable and flexible inference framework for stochastic dynamic single-cell models
title_full_unstemmed Scalable and flexible inference framework for stochastic dynamic single-cell models
title_short Scalable and flexible inference framework for stochastic dynamic single-cell models
title_sort scalable and flexible inference framework for stochastic dynamic single-cell models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159578/
https://www.ncbi.nlm.nih.gov/pubmed/35588132
http://dx.doi.org/10.1371/journal.pcbi.1010082
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