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

Persson, Sebastian; Welkenhuysen, Niek; Shashkova, Sviatlana; Wiqvist, Samuel; Reith, Patrick; Gw, Schmidt; Picchini, Umberto; Cvijović, Marija

doi:10.1101/2021.07.01.450748

Cited by 3 publications

(4 citation statements)

References 65 publications

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“…We emphasize, however, that more accurate SM than the HMMs introduced in this paper could be used especially for strong time correlations between measurements; for closely spaced observations leading to such correlations sequential Monte Carlo methods such as [ 53 ] are likely to provide better results. It should be remarked that the improvement in accuracy that can be obtained using our approach is probably not uniform in parameter space.…”

Section: Discussionmentioning

confidence: 99%

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Öcal

Gutmann

Sanguinetti

et al. 2022

J. R. Soc. Interface.

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Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model , to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.

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Section: Discussionmentioning

confidence: 99%

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Öcal

Gutmann

Sanguinetti

et al. 2022

J. R. Soc. Interface.

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“…To circumvent this, a Bayesian framework for estimating kinetic parameters in models that account for both intrinsic and extrinsic noise has been recently introduced (Persson et al . 2021 ).…”

Section: Single-cell Dynamic Modelling—a Tale Of Individual Cellsmentioning

confidence: 99%

“…Using this framework, Persson et al . ( 2021 ) studied Mig1 localization upon fructose addition to starved cells. To build four small but not minimalistic models with interpretable kinetic parameters, the authors included prior information about potential values into their Bayesian parameter estimation.…”

Section: Single-cell Dynamic Modelling—a Tale Of Individual Cellsmentioning

confidence: 99%

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

Persson

Shashkova

Österberg

et al. 2022

FEMS Yeast Research

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Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in cellular adaptation to changing environments. The SNF1 pathway regulates energy homeostasis upon glucose derepression, hence, plays an important role in various processes, such as metabolism, cell cycle, autophagy. To unravel its behaviour, SNF1 signalling has been extensively studied. However, the pathway components are strongly interconnected and inconstant; therefore, elucidating its dynamic behaviour based on experimental data only is challenging. To tackle this complexity, systems biology approaches have been successfully employed. This review summarises the progress, advantages and disadvantages of the available mathematical modelling frameworks covering Boolean, dynamic kinetic, single-cell models, which have been used to study processes and phenomena ranging from crosstalks to sources of cell-to-cell variability in the context of SNF1 signalling. Based on the lessons from existing models, we further discuss how to develop a consensus dynamic mechanistic model of the entire SNF1 pathway that can provide novel insights into the dynamics of nutrient signalling.

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“…Parameter inference has provided insight into clonal relationships of single cells [25,26] and stem cell differentiation/cell state transitions [27,28]. Inference methods have also been applied to single-cell data for the discovery of new properties of single-cell oscillations [29,30] and cell-cell variability [31][32][33], as well as to study cell-cell communication [34]. New methods to infer the parameters of models of stochastic gene expression provide means to study single-cell dynamics in greater depth [35,36].…”

Section: Introductionmentioning

confidence: 99%

Single-cell Ca ²⁺ parameter inference reveals how transcriptional states inform dynamic cell responses

Wollman

MacLean

2023

J. R. Soc. Interface.

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Single-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca 2+ dynamics in single cells. We propose to share information between cells via transfer learning: for a sequence of cells, the posterior distribution of one cell is used to inform the prior distribution of the next. In application to intracellular Ca 2+ signalling dynamics, we fit the parameters of a dynamical model for thousands of cells with variable single-cell responses. We show that transfer learning accelerates inference with sequences of cells regardless of how the cells are ordered. However, only by ordering cells based on their transcriptional similarity can we distinguish Ca 2+ dynamic profiles and associated marker genes from the posterior distributions. Inference results reveal complex and competing sources of cell heterogeneity: parameter covariation can diverge between the intracellular and intercellular contexts. Overall, we discuss the extent to which single-cell parameter inference informed by transcriptional similarity can quantify relationships between gene expression states and signalling dynamics in single cells.

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

Cited by 3 publications

References 65 publications

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

Single-cell Ca ²⁺ parameter inference reveals how transcriptional states inform dynamic cell responses

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

Cited by 3 publications

References 65 publications

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

Single-cell Ca 2+ parameter inference reveals how transcriptional states inform dynamic cell responses

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Product

Resources

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Single-cell Ca ²⁺ parameter inference reveals how transcriptional states inform dynamic cell responses