Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics
- Creators
-
Gorin, Gennady
- Wang, Mengyu
- Golding, Ido
- Xu, Heng
Abstract
Recent advances in single-molecule fluorescent imaging have enabled quantitative measurements of transcription at a single gene copy, yet an accurate understanding of transcriptional kinetics is still lacking due to the difficulty of solving detailed biophysical models. Here we introduce a stochastic simulation and statistical inference platform for modeling detailed transcriptional kinetics in prokaryotic systems, which has not been solved analytically. The model includes stochastic two-state gene activation, mRNA synthesis initiation and stepwise elongation, release to the cytoplasm, and stepwise co-transcriptional degradation. Using the Gillespie algorithm, the platform simulates nascent and mature mRNA kinetics of a single gene copy and predicts fluorescent signals measurable by time-lapse single-cell mRNA imaging, for different experimental conditions. To approach the inverse problem of estimating the kinetic parameters of the model from experimental data, we develop a heuristic optimization method based on the genetic algorithm and the empirical distribution of mRNA generated by simulation. As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. The platform is available as a MATLAB software package at https://data.caltech.edu/records/1287.
Additional Information
© 2020 Gorin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: November 18, 2019; Accepted: March 6, 2020; Published: March 26, 2020. Portions of work by G.G., M.W., and I.G. were performed at Baylor College of Medicine, Houston, Texas, USA. Portions of work by G.G. were performed at Shanghai Jiao Tong University, Shanghai, China. G.G. thanks Dr. Lior Pachter (California Institute of Technology) for support and Dr. Brian Munsky (Colorado State University) for valuable advice. We gratefully acknowledge the computing resources provided by the student innovation center at Shanghai Jiao Tong University. Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0230736. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: The authors were funded by the following sources during the completion of this research: GG: NIH U19MH114830. National Institutes of Health. nih.gov. GG: Undergraduate Asian Studies Internship Award (U-ASIA) 2017. Rice University Chao Center for Asian Studies. chaocenter.rice.edu. MW, IG: R01 GM082837. National Institutes of Health. nih.gov. MW, IG: PHY 1430124. National Science Foundation. nsf.gov. HX: 2018YFC0310803. National Key R&D Program of China. http://most.gov.cn/ HX: 11774225. National Natural Science Foundation of China. nsfc.gov.cn. HX: Thousand Talents Plan of China, Program for Young Professionals. 1000plan.org.cn. HX: 18ZR1419800. National Science Foundation of Shanghai. stcsm.sh.gov.cn. HX: 1013907. Burroughs Wellcome Fund Career Award at the Scientific Interface. bwfund.org. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. Author Contributions Conceptualization: Gennady Gorin, Mengyu Wang, Ido Golding, Heng Xu. Formal analysis: Gennady Gorin, Ido Golding, Heng Xu. Funding acquisition: Gennady Gorin, Mengyu Wang, Ido Golding, Heng Xu. Investigation: Gennady Gorin, Ido Golding, Heng Xu. Methodology: Gennady Gorin, Ido Golding, Heng Xu. Project administration: Ido Golding, Heng Xu. Resources: Heng Xu. Software: Gennady Gorin, Mengyu Wang. Supervision: Ido Golding, Heng Xu. Validation: Gennady Gorin, Ido Golding, Heng Xu. Visualization: Gennady Gorin, Ido Golding, Heng Xu. Writing – original draft: Gennady Gorin, Ido Golding, Heng Xu. Writing – review & editing: Gennady Gorin, Mengyu Wang, Ido Golding, Heng Xu.Attached Files
Published - journal.pone.0230736.pdf
Submitted - 825869v1.full.pdf
Supplemental Material - journal.pone.0230736.s001.docx
Supplemental Material - journal.pone.0230736.s002.mp4
Supplemental Material - journal.pone.0230736.s003.mp4
Supplemental Material - journal.pone.0230736.s004.mp4
Supplemental Material - journal.pone.0230736.s005.mp4
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Additional details
- Alternative title
- Stochastic simulation platform for visualization and estimation of transcriptional kinetics
- PMCID
- PMC7098607
- Eprint ID
- 102140
- Resolver ID
- CaltechAUTHORS:20200327-084342317
- U19MH114830
- NIH
- Undergraduate Asian Studies Internship Award
- Rice University
- R01 GM082837
- NIH
- PHY-1430124
- NSF
- 2018YFC0310803
- National Key Research and Development Program of China
- 11774225
- National Natural Science Foundation of China
- 18ZR1419800
- Thousand Talents Plan of China
- 1013907
- National Science Foundation of Shanghai
- Burroughs Wellcome Fund
- Created
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2020-03-27Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field