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Abstract MotivationAdvances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks. ResultsWe propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth–death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. Availability and implementationAccompanying code in R is available at https://github.com/cbskust/DDE_BD. Supplementary informationSupplementary data are available at Bioinformatics online.
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This content will become publicly available on November 1, 2026
Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data
Abstract MotivationStochastic gene expression and cell-to-cell heterogeneity have attracted increased interest in recent years, enabled by advances in single-cell measurement technologies. These studies are also increasingly complemented by quantitative biophysical modeling, often using the framework of stochastic biochemical kinetic models. However, inferring parameters for such models (i.e., the kinetic rates of biochemical reactions) remains a technical and computational challenge, particularly doing so in a manner that can leverage high-throughput single-cell sequencing data. ResultsIn this work, we develop a chemical master equation model reference library-based computational pipeline to infer kinetic parameters describing noisy mRNA distributions from single-cell RNA sequencing data, using the commonly applied stochastic telegraph model. The approach fits kinetic parameters via steady-state distributions, as measured across a population of cells in snapshot data. Our pipeline also serves as a tool for comprehensive analysis of parameter identifiability, in both a priori (studying model properties in the absence of data) and a posteriori (in the context of a particular dataset) use-cases. The pipeline can perform both of these tasks, i.e. inference and identifiability analysis, in an efficient and scalable manner, and also serves to disentangle contributions to uncertainty in inferred parameters from experimental noise versus structural properties of the model. We found that for the telegraph model, the majority of the parameter space is not practically identifiable from single-cell RNA sequencing data, and low experimental capture rates worsen the identifiability. Our methodological framework could be extended to other data types in the fitting of small biochemical network models. Availability and implementationAll code relevant to this work is available at https://github.com/Read-Lab-UCI/TelegraphLikelihoodInfer, archival DOI: https://doi.org/10.5281/zenodo.16915450.
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- Award ID(s):
- 2022182
- PAR ID:
- 10651799
- Editor(s):
- Mathelier, Anthony
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 41
- Issue:
- 11
- ISSN:
- 1367-4811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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