Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection

Froehlich, Fabian; Reiser, Anita; Fink, Laura; Woschée, Daniel; Ligon, Thomas; Theis, Fabian J.; Raedler, Joachim; Hasenauer, Jan

doi:10.1101/285478

Cited by 3 publications

(14 citation statements)

References 60 publications

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“…Considering that parameter estimation for single-cell time-lapse data is challenging, we explored and compared the performance of standard two stage (STS) (Karlsson et al, 2015 ) and non-linear mixed-effect (NLME) (Almquist et al, 2015 ; Karlsson et al, 2015 ; Llamosi et al, 2016 ; Fröhlich et al, 2019 ; Marguet et al, 2019 ) approaches. NLME is considered superior to STS when the data is not rich (Karlsson et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop

et al. 2020

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Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast, Saccharomyces cerevisiae, the sucrose non-fermenting protein kinase complex SNF1 is a master regulator of energy homeostasis. It is affected by multiple inputs, among which energy levels is the most prominent. Cells which are exposed to a switch in carbon source availability display a change in the gene expression machinery. It has been shown that the magnitude of the change varies from cell to cell. In a glucose rich environment Snf1/Mig1 pathway represses the expression of its downstream target, such as SUC2. However, upon glucose depletion SNF1 is activated which leads to an increase in SUC2 expression. Our single cell experiments indicate that upon starvation, gene expression pattern of SUC2 shows rapid increase followed by a decrease to initial state with high cell-to-cell variability. The mechanism behind this behavior is currently unknown. In this work we study the long-term behavior of the Snf1/Mig1 pathway upon glucose starvation with a microfluidics and non-linear mixed effect modeling approach. We show a negative feedback mechanism, involving Snf1 and Reg1, which reduces SUC2 expression after the initial strong activation. Snf1 kinase activity plays a key role in this feedback mechanism. Our systems biology approach proposes a negative feedback mechanism that works through the SNF1 complex and is controlled by energy levels. We further show that Reg1 likely is involved in the negative feedback mechanism.

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Section: Resultsmentioning

confidence: 99%

Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop

et al. 2020

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“…The released mRNA was translated into a fluorescent protein which caused the cells to fluoresce. For each image taken during the experiment, the fluorescence intensity is integrated over squares occupied by one cell in order to obtain one value for the mean fluorescence intensity per cell and time point (see Fröhlich et al, 2018, for further details about the image analysis).…”

Section: Experimental Datamentioning

confidence: 99%

“…Since our results in the previous section have shown that the SDE model yields more reliable parameter estimates (assuming an MJP is an adequate description for the translation kinetics after mRNA transfection) than the ODE model even for those parameters that are identifiable for both model types, we reanalyze the experimental data published in Fröhlich et al (2018) and described in Section 2. For each type of GFP (eGFP and d2eGFP), we randomly select 100 observed trajectories for our analysis and again use Stan to sample from the posterior distributions of the ODE model ( 6) and the SDE model ( 7) for each of the trajectories using the same priors as stated in Section 6.2.1.…”

Section: Estimation Based On Experimental Datamentioning

confidence: 99%

“…Another drawback of both simulation-based approaches is the fact that the analysis is based on a finite set of parameter combinations that can be considered; and thus, drawing general conclusions for the entire parameter space may be problematic. Moreover, we have assessed the practical parameter identifiability for both model types by sampling from the parameter posterior distribution given simulated data with and without measurement error and the experimental data published in Fröhlich et al (2018). While our focus was on inference from individual fluorescence trajectories, using SDE mixed-e↵ect modeling as done in Wiqvist et al (2021) would be a meaningful extension, especially e. g. to obtain a common estimate for the parameter scale.…”

Section: Estimation Based On Experimental Datamentioning

confidence: 99%

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Identifiability analysis for models of the translation kinetics after mRNA transfection

Pieschner

Hasenauer

Fuchs

2021

Preprint

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Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter identifiability. Previous studies have considered ordinary differential equation (ODE) models of the process, and here we formulate a stochastic differential equation (SDE) model. For both model types, we consider structural identifiability based on the model equations and practical identifiability based on simulated as well as experimental data and find that the SDE model provides better parameter identifiability than the ODE model. Moreover, our analysis shows that even for those parameters of the ODE model that are considered to be identifiable, the obtained estimates are sometimes unreliable. Overall, our study clearly demonstrates the relevance of considering different modeling approaches and that stochastic models can provide more reliable and informative results.

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“…In the ME approach, response variability over different individuals is captured by the variability of the parameters of a structurally identical response model. A population model describes these parameters as random outcomes of a common probability distribution estimated from the data (Dharmarajan et al , 2019; Fröhlich et al , 2018; Llamosi et al , 2016). Crucially, individuals are assumed to be statistically independent.…”

Section: Introductionmentioning

confidence: 99%

Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data

2019

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Motivation Modern experimental technologies enable monitoring of gene expression dynamics in individual cells and quantification of its variability in isogenic microbial populations. Among the sources of this variability is the randomness that affects inheritance of gene expression factors at cell division. Known parental relationships among individually observed cells provide invaluable information for the characterization of this extrinsic source of gene expression noise. Despite this fact, most existing methods to infer stochastic gene expression models from single-cell data dedicate little attention to the reconstruction of mother–daughter inheritance dynamics. Results Starting from a transcription and translation model of gene expression, we propose a stochastic model for the evolution of gene expression dynamics in a population of dividing cells. Based on this model, we develop a method for the direct quantification of inheritance and variability of kinetic gene expression parameters from single-cell gene expression and lineage data. We demonstrate that our approach provides unbiased estimates of mother–daughter inheritance parameters, whereas indirect approaches using lineage information only in the post-processing of individual-cell parameters underestimate inheritance. Finally, we show on yeast osmotic shock response data that daughter cell parameters are largely determined by the mother, thus confirming the relevance of our method for the correct assessment of the onset of gene expression variability and the study of the transmission of regulatory factors. Availability and implementation Software code is available at https://github.com/almarguet/IdentificationWithARME. Lineage tree data is available upon request. Supplementary information Supplementary material is available at Bioinformatics online.

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Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection

Cited by 3 publications

References 60 publications

Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop

Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop

Identifiability analysis for models of the translation kinetics after mRNA transfection

Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data

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