ArticleJournal of the Royal Society, Interface2019
Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- What can we learn when fitting a simple telegraph model to a complex gene expression model?PLoS computational biology · 2024Article
- Stochastic modeling of the mRNA life process: A generalized master equation.Biophysical journal · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Inference and uncertainty quantification of stochastic gene expression via synthetic models.Journal of the Royal Society, Interface · 2022Article
- Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes.ACS omega · 2022Article
- Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns.Journal of the Royal Society, Interface · 2022Article
- A minimal model for gene expression dynamics of bacterial type II toxin-antitoxin systems.Scientific reports · 2021Article
- Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments.Patterns (New York, N.Y.) · 2021Article
- Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data.Journal of the Royal Society, Interface · 2021Article
- Neural network aided approximation and parameter inference of non-Markovian models of gene expression.Nature communications · 2021Article
- Incorporating age and delay into models for biophysical systems.Physical biology · 2021Article
- A Stochastic Model of Gene Expression with Polymerase Recruitment and Pause Release.Biophysical journal · 2020Article
- Stochastic Modeling of Autoregulatory Genetic Feedback Loops: A Review and Comparative Study.Biophysical journal · 2020Review
- Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO.International journal for uncertainty quantification · 2020Article
- Revisiting the Reduction of Stochastic Models of Genetic Feedback Loops with Fast Promoter Switching.Biophysical journal · 2019Article
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2 authors.
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Abstract
Bayesian and non-Bayesian moment-based inference methods are commonly used to estimate the parameters defining stochastic models of gene regulatory networks from noisy single cell or population snapshot data. However, a systematic investigation of the accuracy of the predictions of these methods remains missing. Here, we present the results of such a study using synthetic noisy data of a negative auto-regulatory transcriptional feedback loop, one of the most common building blocks of complex gene regulatory networks. We study the error in parameter estimation as a function of (i) number of cells in each sample; (ii) the number of time points; (iii) the highest-order moment of protein fluctuations used for inference; (iv) the moment-closure method used for likelihood approximation. We find that for sample sizes typical of flow cytometry experiments, parameter estimation by maximizing the likelihood is as accurate as using Bayesian methods but with a much reduced computational time. We also show that the choice of moment-closure method is the crucial factor determining the maximum achievable accuracy of moment-based inference methods. Common likelihood approximation methods based on the linear noise approximation or the zero cumulants closure perform poorly for feedback loops with large protein-DNA binding rates or large protein bursts; this is exacerbated for highly heterogeneous cell populations. By contrast, approximating the likelihood using the linear-mapping approximation or conditional derivative matching leads to highly accurate parameter estimates for a wide range of conditions.
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