Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.
Gennady Gorin, John J Vastola, Meichen Fang, Lior Pachter
Author Information
Gennady Gorin: Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA. ORCID
John J Vastola: Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA. ORCID
Meichen Fang: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA. ORCID
Lior Pachter: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA. lpachter@caltech.edu. ORCID
The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.
References
J R Soc Interface. 2021 May;18(178):20210274
[PMID: 34034535]
