Validation methods for population models of gene expression dynamics

González-Vargas, Andrés M.; Cinquemani, Eugenio; Ferrari-Trecate, Giancarlo

doi:10.1016/j.ifacol.2016.12.112

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Cell ensemble models can also be formulated as mixedeffects models [93], for which efficient estimation methods can be used when trajectory data for individuals are available.…”

Section: Related Estimation Problemsmentioning

confidence: 99%

Estimation methods for heterogeneous cell population models in systems biology

Waldherr¹

2018

J. R. Soc. Interface.

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Heterogeneity among individual cells is a characteristic and relevant feature of living systems. A range of experimental techniques to investigate this heterogeneity is available, and multiple modelling frameworks have been developed to describe and simulate the dynamics of heterogeneous populations. Measurement data are used to adjust computational models, which results in parameter and state estimation problems. Methods to solve these estimation problems need to take the specific properties of data and models into account. The aim of this review is to give an overview on the state of the art in estimation methods for heterogeneous cell population data and models. The focus is on models based on the population balance equation, but stochastic and individual-based models are also discussed. It starts with a brief discussion of common experimental approaches and types of measurement data that can be obtained in this context. The second part describes computational modelling frameworks for heterogeneous populations and the types of estimation problems occurring for these models. The third part starts with a discussion of observability and identifiability properties, after which the computational methods to solve the various estimation problems are described.

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“…Cell ensemble models can also be formulated as mixedeffects models [93], for which efficient estimation methods can be used when trajectory data for individuals are available.…”

Section: Related Estimation Problemsmentioning

confidence: 99%

Estimation methods for heterogeneous cell population models in systems biology

Waldherr¹

2018

J. R. Soc. Interface.

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“…To evaluate the GTS method on a benchmark problem, we used a published model and need to be inferred from the data 30 . Using the Hog1 system, a recent comparison also showed that NLMEs and stochastic (CME-based) approaches perform comparably for predictions at the population level, but that NLMEs yield better cell-specific estimates 33 .…”

Section: Gts Is Competitive To Saem On a Benchmark Problemmentioning

confidence: 99%

“…To scale NLME applications for single-cell biology, we propose a scalable inference method that is robust to errors commonly encountered in live imaging-based single-cell analysis 30,33 , easy to implement, and easy to parallelize. It exploits that single-cell imaging data typically contains reliable measurements for many cells.…”

Section: Introductionmentioning

confidence: 99%

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

Dharmarajan

Kaltenbach

Rudolf

et al. 2018

Preprint

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The availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Singlecell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

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A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

et al. 2019

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Graphical AbstractHighlights d Dynamic single-cell data pose opportunities and challenges for mechanistic models d Non-linear mixed-effects models disentangle sources of cellular heterogeneity d Methods for flexible, scalable parameter inference and for sub-process identification d Several sub-processes contribute dynamically to heterogeneity in yeast endocytosis SUMMARY Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intracellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and populationaverage parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of subprocesses helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

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Validation methods for population models of gene expression dynamics

Cited by 4 publications

References 14 publications

Estimation methods for heterogeneous cell population models in systems biology

Estimation methods for heterogeneous cell population models in systems biology

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

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