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

Marguet, Aline; Lavielle, Marc; Cinquemani, Eugenio

doi:10.1093/bioinformatics/btz378

Cited by 9 publications

(3 citation statements)

References 31 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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“…Either they (i) track single lineages as observed in the mother machine [4], either (ii) they take population snapshots of descendant cells of a common ancestor, like in flow cytometry [5] experiments. Understanding the differences between these experimental viewpoints [6] has become an expanding area of research [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Cell size statistics in cell lineages and population snapshots with different growth regimes and division strategies

Totis

Nieto

Vargas-García

et al. 2020

Preprint

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Growing populations of bacteria control their growth and division reaching narrow distributions of cellsizes. In this paper we explored how different combinations of growth regimes and division mechanisms lead to different cell-size statistics in these populations. Deterministic and stochastic modeling were used to describe the size distribution of a population of cells that is observed from two different perspectives: as single cell lineages, i.e. random paths in the lineage tree, or as snapshots, at given times, of a population in which all descendants of a single ancestor cell are observed. Our time-dependent approaches allowed us to obtain both the transient dynamics and the steady state values for the main statistical moments of the cell-size distribution. Also, we established mathematical relationships among the statistics in the two considered perspectives, thus improving our knowledge of how cells control their growth and proliferation.

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“…Nonlinear mixed-effects (NLME) models are flexible and powerful for analyzing such data. Examples include tumor growth inhibition modeling, 1 viral dynamic modeling, 2 and pharmacokinetic (PK) modeling, 3 gene expression dynamic modeling, 4 etc. NLME models decompose the source of variability into between-individuals and within-individuals variability, making it easy to handle unbalanced, sparse and missing data in longitudinal data analysis.…”

Section: Introductionmentioning

confidence: 99%

Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model

Yuan

Zhu

Yang

et al. 2020

Stat Methods Med Res

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Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p-values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p-value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes.

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

Cited by 9 publications

References 31 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

Cell size statistics in cell lineages and population snapshots with different growth regimes and division strategies

Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model

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