Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions

Miles, Christopher E.; McKinley, Scott A.; Ding, Fangyuan; Lehoucq, Richard B.

doi:10.1007/s11538-024-01301-4

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions

Journal Article · Mon May 13 00:00:00 EDT 2024 · Bulletin of Mathematical Biology

DOI:https://doi.org/10.1007/s11538-024-01301-4· OSTI ID:2394705

^[1]; McKinley, Scott A. ^[2]; Ding, Fangyuan ^[1]; Lehoucq, Richard B. ^[3]

Univ. of California, Irvine, CA (United States)
Tulane Univ., New Orleans, LA (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Many imaging techniques for biological systems—like fixation of cells coupled with fluorescence microscopy—provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. In this study, these snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; National Institutes of Health (NIH)

Grant/Contract Number:: NA0003525

OSTI ID:: 2394705

Report Number(s):: SAND--2024-08367J

Journal Information:: Bulletin of Mathematical Biology, Journal Name: Bulletin of Mathematical Biology Journal Issue: 6 Vol. 86; ISSN 0092-8240

Publisher:: Society for Mathematical Biology - SpringerCopyright Statement

Country of Publication:: United States

Language:: English

References (94)

Stochastic Resonance in Biology How Noise Can Enhance Detection of Weak Signals and Help Improve Biological Information Processing Hänggi, Peter ChemPhysChem, Vol. 3, Issue 3 https://doi.org/10.1002/1439-7641(20020315)3:3<285::AID-CPHC285>3.0.CO;2-A	journal	March 2002
Temporal and spatial regulation of mRNA export: Single particle RNA‐imaging provides new tools and insights Heinrich, Stephanie; Derrer, Carina Patrizia; Lari, Azra BioEssays, Vol. 39, Issue 2 https://doi.org/10.1002/bies.201600124	journal	January 2017
The poisson representation. I. A new technique for chemical master equations Gardiner, C. W.; Chaturvedi, S. Journal of Statistical Physics, Vol. 17, Issue 6 https://doi.org/10.1007/BF01014349	journal	December 1977
The poisson representation. II Two-time correlation functions Chaturvedi, S.; Gardiner, C. W. Journal of Statistical Physics, Vol. 18, Issue 5 https://doi.org/10.1007/BF01014520	journal	May 1978
Stochastic analysis of a chemical reaction with spatial and temporal structures Chaturvedi, S.; Gardiner, C. W.; Matheson, I. S. Journal of Statistical Physics, Vol. 17, Issue 6 https://doi.org/10.1007/bf01014350	journal	December 1977
Parameter estimation for a point-source diffusion-decay morphogen model Flegg, Mark B.; Muñoz, Mario A.; Smith-Miles, Kate Journal of Mathematical Biology, Vol. 80, Issue 7 https://doi.org/10.1007/s00285-020-01494-x	journal	April 2020
Spatial Point Processes and Moment Dynamics in the Life Sciences: A Parsimonious Derivation and Some Extensions Plank, Michael J.; Law, Richard Bulletin of Mathematical Biology, Vol. 77, Issue 4 https://doi.org/10.1007/s11538-014-0018-8	journal	September 2014
Spatial Stochastic Intracellular Kinetics: A Review of Modelling Approaches Smith, Stephen; Grima, Ramon Bulletin of Mathematical Biology, Vol. 81, Issue 8 https://doi.org/10.1007/s11538-018-0443-1	journal	May 2018
A general mathematical framework for the analysis of spatiotemporal point processes Ovaskainen, Otso; Finkelshtein, Dmitri; Kutoviy, Oleksandr Theoretical Ecology, Vol. 7, Issue 1 https://doi.org/10.1007/s12080-013-0202-8	journal	October 2013
Bayesian statistical learning for big data biology Yau, Christopher; Campbell, Kieran Biophysical Reviews, Vol. 11, Issue 1 https://doi.org/10.1007/s12551-019-00499-1	journal	February 2019
Transient Antibody-Mucin Interactions Produce a Dynamic Molecular Shield against Viral Invasion Chen, Alex; McKinley, Scott A.; Wang, Simi Biophysical Journal, Vol. 106, Issue 9 https://doi.org/10.1016/j.bpj.2014.02.038	journal	May 2014
Subdiffusive Dynamics Lead to Depleted Particle Densities near Cellular Borders Holmes, William R. Biophysical Journal, Vol. 116, Issue 8 https://doi.org/10.1016/j.bpj.2019.02.021	journal	April 2019
Modeling bursty transcription and splicing with the chemical master equation Gorin, Gennady; Pachter, Lior Biophysical Journal, Vol. 121, Issue 6 https://doi.org/10.1016/j.bpj.2022.02.004	journal	March 2022
Palmitate-mediated disruption of the endoplasmic reticulum decreases intracellular vesicle motility Rayens, Nathan T.; Cook, Keisha J.; McKinley, Scott A. Biophysical Journal, Vol. 122, Issue 7 https://doi.org/10.1016/j.bpj.2023.03.001	journal	April 2023
Advances in live-cell single-particle tracking and dynamic super-resolution imaging Cognet, Laurent; Leduc, Cécile; Lounis, Brahim Current Opinion in Chemical Biology, Vol. 20 https://doi.org/10.1016/j.cbpa.2014.04.015	journal	June 2014
Linking gene regulation to mRNA production and export Rodríguez-Navarro, Susana; Hurt, Ed Current Opinion in Cell Biology, Vol. 23, Issue 3 https://doi.org/10.1016/j.ceb.2010.12.002	journal	June 2011
A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting Lammers, Nicholas C.; Kim, Yang Joon; Zhao, Jiaxi Current Opinion in Cell Biology, Vol. 67 https://doi.org/10.1016/j.ceb.2020.08.001	journal	December 2020
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences Raj, Arjun; van Oudenaarden, Alexander Cell, Vol. 135, Issue 2 https://doi.org/10.1016/j.cell.2008.09.050	journal	October 2008
Dynamics and functional roles of splicing factor autoregulation Ding, Fangyuan; Su, Christina J.; Edmonds, KeHuan Kuo Cell Reports, Vol. 39, Issue 12 https://doi.org/10.1016/j.celrep.2022.110985	journal	June 2022
On structural and practical identifiability Wieland, Franz-Georg; Hauber, Adrian L.; Rosenblatt, Marcus Current Opinion in Systems Biology, Vol. 25 https://doi.org/10.1016/j.coisb.2021.03.005	journal	March 2021
Space as the final frontier in stochastic simulations of biological systems Lemerle, Caroline; Di Ventura, Barbara; Serrano, Luis FEBS Letters, Vol. 579, Issue 8 https://doi.org/10.1016/j.febslet.2005.02.009	journal	February 2005
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
Analysis of single particle diffusion with transient binding using particle filtering Bernstein, Jason; Fricks, John Journal of Theoretical Biology, Vol. 401 https://doi.org/10.1016/j.jtbi.2016.04.013	journal	July 2016
Modelling count data with partial differential equation models in biology Simpson, Matthew J.; Murphy, Ryan J.; Maclaren, Oliver J. Journal of Theoretical Biology, Vol. 580 https://doi.org/10.1016/j.jtbi.2024.111732	journal	March 2024
Bursting onto the scene? Exploring stochastic mRNA production in bacteria Jones, Daniel; Elf, Johan Current Opinion in Microbiology, Vol. 45 https://doi.org/10.1016/j.mib.2018.04.001	journal	October 2018
RNA Velocity: Molecular Kinetics from Single-Cell RNA-Seq Svensson, Valentine; Pachter, Lior Molecular Cell, Vol. 72, Issue 1 https://doi.org/10.1016/j.molcel.2018.09.026	journal	October 2018
Models of stochastic gene expression Paulsson, Johan Physics of Life Reviews, Vol. 2, Issue 2 https://doi.org/10.1016/j.plrev.2005.03.003	journal	June 2005
Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics Munsky, Brian; Fox, Zachary; Neuert, Gregor Methods, Vol. 85 https://doi.org/10.1016/j.ymeth.2015.06.009	journal	September 2015
Single particle tracking. Analysis of diffusion and flow in two-dimensional systems Qian, H.; Sheetz, M. P.; Elson, E. L. Biophysical Journal, Vol. 60, Issue 4 https://doi.org/10.1016/s0006-3495(91)82125-7	journal	October 1991
Stochastic protein expression in individual cells at the single molecule level Cai, Long; Friedman, Nir; Xie, X. Sunney Nature, Vol. 440, Issue 7082 https://doi.org/10.1038/nature04599	journal	March 2006
SAGA interacting factors confine sub-diffusion of transcribed genes to the nuclear envelope Cabal, Ghislain G.; Genovesio, Auguste; Rodriguez-Navarro, Susana Nature, Vol. 441, Issue 7094 https://doi.org/10.1038/nature04752	journal	June 2006
Cox process representation and inference for stochastic reaction–diffusion processes Schnoerr, David; Grima, Ramon; Sanguinetti, Guido Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms11729	journal	May 2016
Exporting RNA from the nucleus to the cytoplasm Köhler, Alwin; Hurt, Ed Nature Reviews Molecular Cell Biology, Vol. 8, Issue 10 https://doi.org/10.1038/nrm2255	journal	October 2007
Neural network aided approximation and parameter inference of non-Markovian models of gene expression Jiang, Qingchao; Fu, Xiaoming; Yan, Shifu Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-22919-1	journal	May 2021
Subcellular spatial transcriptomics identifies three mechanistically different classes of localizing RNAs Cassella, Lucia; Ephrussi, Anne Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-34004-2	journal	October 2022
Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments Gorin, Gennady; Vastola, John J.; Fang, Meichen Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-34857-7	journal	December 2022
RNA velocity of single cells La Manno, Gioele; Soldatov, Ruslan; Zeisel, Amit Nature, Vol. 560, Issue 7719 https://doi.org/10.1038/s41586-018-0414-6	journal	August 2018
Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+ Eng, Chee-Huat Linus; Lawson, Michael; Zhu, Qian Nature, Vol. 568, Issue 7751 https://doi.org/10.1038/s41586-019-1049-y	journal	March 2019
Method of the Year: spatially resolved transcriptomics Marx, Vivien Nature Methods, Vol. 18, Issue 1 https://doi.org/10.1038/s41592-020-01033-y	journal	January 2021
Museum of spatial transcriptomics Moses, Lambda; Pachter, Lior Nature Methods, Vol. 19, Issue 5 https://doi.org/10.1038/s41592-022-01409-2	journal	March 2022
Constitutive splicing and economies of scale in gene expression Ding, Fangyuan; Elowitz, Michael B. Nature Structural & Molecular Biology, Vol. 26, Issue 6 https://doi.org/10.1038/s41594-019-0226-x	journal	May 2019
Gene expression model inference from snapshot RNA data using Bayesian non-parametrics Kilic, Zeliha; Schweiger, Max; Moyer, Camille Nature Computational Science, Vol. 3, Issue 2 https://doi.org/10.1038/s43588-022-00392-0	journal	January 2023
The random walker's toolbox for analyzing single-particle tracking data Rehfeldt, Florian; Weiss, Matthias Soft Matter, Vol. 19, Issue 28 https://doi.org/10.1039/D3SM00557G	journal	January 2023
Complex nearly immotile behaviour of enzymatically driven cargos Osunbayo, O.; Miles, C. E.; Doval, F. Soft Matter, Vol. 15, Issue 8 https://doi.org/10.1039/c8sm01893f	journal	January 2019
Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts Lin, Yen Ting; Buchler, Nicolas E. The Journal of Chemical Physics, Vol. 151, Issue 2 https://doi.org/10.1063/1.5110503	journal	July 2019
New directions in single-molecule imaging and analysis Moerner, W. E. Proceedings of the National Academy of Sciences, Vol. 104, Issue 31 https://doi.org/10.1073/pnas.0610081104	journal	July 2007
Stochastic branching-diffusion models for gene expression Cottrell, David; Swain, Peter S.; Tupper, Paul F. Proceedings of the National Academy of Sciences, Vol. 109, Issue 25 https://doi.org/10.1073/pnas.1201103109	journal	June 2012
Fundamental limits on dynamic inference from single-cell snapshots Weinreb, Caleb; Wolock, Samuel; Tusi, Betsabeh K. Proceedings of the National Academy of Sciences, Vol. 115, Issue 10 https://doi.org/10.1073/pnas.1714723115	journal	February 2018
Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells Cao, Zhixing; Grima, Ramon Proceedings of the National Academy of Sciences, Vol. 117, Issue 9 https://doi.org/10.1073/pnas.1910888117	journal	February 2020
Inferring gene regulation from stochastic transcriptional variation across single cells at steady state Gupta, Anika; Martin-Rufino, Jorge D.; Jones, Thouis R. Proceedings of the National Academy of Sciences, Vol. 119, Issue 34 https://doi.org/10.1073/pnas.2207392119	journal	August 2022
Variational Inference: A Review for Statisticians Blei, David M.; Kucukelbir, Alp; McAuliffe, Jon D. Journal of the American Statistical Association, Vol. 112, Issue 518 https://doi.org/10.1080/01621459.2017.1285773	journal	July 2016
A review of progress in single particle tracking: from methods to biophysical insights Manzo, Carlo; Garcia-Parajo, Maria F. Reports on Progress in Physics, Vol. 78, Issue 12 https://doi.org/10.1088/0034-4885/78/12/124601	journal	October 2015
Diffusion analysis of single particle trajectories in a Bayesian nonparametrics framework Falcao, Rebeca Cardim; Coombs, Daniel Physical Biology, Vol. 17, Issue 2 https://doi.org/10.1088/1478-3975/ab64b3	journal	February 2020
Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking McLaughlin, Grace A.; Langdon, Erin M.; Crutchley, John M. Molecular Biology of the Cell, Vol. 31, Issue 14 https://doi.org/10.1091/mbc.E20-03-0210	journal	July 2020
An agent-based model for mRNA export through the nuclear pore complex Azimi, Mohammad; Bulat, Evgeny; Weis, Karsten Molecular Biology of the Cell, Vol. 25, Issue 22 https://doi.org/10.1091/mbc.e14-06-1065	journal	November 2014
BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data Luo, Xizhi; Qin, Fei; Xiao, Feifei Briefings in Bioinformatics, Vol. 23, Issue 6 https://doi.org/10.1093/bib/bbac464	journal	November 2022
Scalable inference of transcriptional kinetic parameters from MS2 time series data Bowles, Jonathan R.; Hoppe, Caroline; Ashe, Hilary L. Bioinformatics, Vol. 38, Issue 4 https://doi.org/10.1093/bioinformatics/btab765	journal	November 2021
FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution Walter, Florin C.; Stegle, Oliver; Velten, Britta Bioinformatics, Vol. 39, Issue 5 https://doi.org/10.1093/bioinformatics/btad183	journal	April 2023
dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues Maynard, Kristen R.; Tippani, Madhavi; Takahashi, Yoichiro Nucleic Acids Research, Vol. 48, Issue 11 https://doi.org/10.1093/nar/gkaa312	journal	May 2020
Identifiability analysis for stochastic differential equation models in systems biology Browning, Alexander P.; Warne, David J.; Burrage, Kevin Journal of The Royal Society Interface, Vol. 17, Issue 173 https://doi.org/10.1098/rsif.2020.0652	journal	December 2020
Learning differential equation models from stochastic agent-based model simulations Nardini, John T.; Baker, Ruth E.; Simpson, Matthew J. Journal of The Royal Society Interface, Vol. 18, Issue 176 https://doi.org/10.1098/rsif.2020.0987	journal	March 2021
Parameter estimation and uncertainty quantification using information geometry Sharp, Jesse A.; Browning, Alexander P.; Burrage, Kevin Journal of The Royal Society Interface, Vol. 19, Issue 189 https://doi.org/10.1098/rsif.2021.0940	journal	April 2022
Stochastic switching of delayed feedback suppresses oscillations in genetic regulatory systems Karamched, Bhargav R.; Miles, Christopher E. Journal of The Royal Society Interface, Vol. 20, Issue 203 https://doi.org/10.1098/rsif.2023.0059	journal	June 2023
Imaging Organization of RNA Processing within the Nucleus Biswas, Jeetayu; Li, Weihan; Singer, Robert H. Cold Spring Harbor Perspectives in Biology, Vol. 13, Issue 12 https://doi.org/10.1101/cshperspect.a039453	journal	June 2021
Special function methods for bursty models of transcription Gorin, Gennady; Pachter, Lior Physical Review E, Vol. 102, Issue 2 https://doi.org/10.1103/PhysRevE.102.022409	journal	August 2020
A Cramér Rao Bound for Point Processes Clark, Daniel E. IEEE Transactions on Information Theory, Vol. 68, Issue 4 https://doi.org/10.1109/TIT.2022.3140374	journal	April 2022
A Review of Modern Computational Algorithms for Bayesian Optimal Design: A Review of Modern Algorithms for Bayesian Design Ryan, Elizabeth G.; Drovandi, Christopher C.; McGree, James M. International Statistical Review, Vol. 84, Issue 1 https://doi.org/10.1111/insr.12107	journal	June 2015
Maximum likelihood estimation for single particle, passive microrheology data with drift Mellnik, John W. R.; Lysy, Martin; Vasquez, Paula A. Journal of Rheology, Vol. 60, Issue 3 https://doi.org/10.1122/1.4943988	journal	May 2016
A New Derivation of Robin Boundary Conditions through Homogenization of a Stochastically Switching Boundary Lawley, Sean D.; Keener, James P. SIAM Journal on Applied Dynamical Systems, Vol. 14, Issue 4 https://doi.org/10.1137/15M1015182	journal	January 2015
Fisher Information Matrix for Single Molecules with Stochastic Trajectories Vahid, Milad R.; Hanzon, Bernard; Ober, Raimund J. SIAM Journal on Imaging Sciences, Vol. 13, Issue 1 https://doi.org/10.1137/19M1242562	journal	January 2020
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks Chen, Xiaoli; Yang, Liu; Duan, Jinqiao SIAM Journal on Scientific Computing, Vol. 43, Issue 3 https://doi.org/10.1137/20M1360153	journal	January 2021
Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster Dewar, Michael A.; Kadirkamanathan, Visakan; Opper, Manfred BMC Systems Biology, Vol. 4, Issue 1 https://doi.org/10.1186/1752-0509-4-21	journal	March 2010
Robust and efficient parameter estimation in dynamic models of biological systems Gábor, Attila; Banga, Julio R. BMC Systems Biology, Vol. 9, Issue 1 https://doi.org/10.1186/s12918-015-0219-2	journal	October 2015
Inferring gene regulatory networks from single-cell data: a mechanistic approach Herbach, Ulysse; Bonnaffoux, Arnaud; Espinasse, Thibault BMC Systems Biology, Vol. 11, Issue 1 https://doi.org/10.1186/s12918-017-0487-0	journal	November 2017
BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells Gómez-Schiavon, Mariana; Chen, Liang-Fu; West, Anne E. Genome Biology, Vol. 18, Issue 1 https://doi.org/10.1186/s13059-017-1297-9	journal	September 2017
An introduction to spatial transcriptomics for biomedical research Williams, Cameron G.; Lee, Hyun Jae; Asatsuma, Takahiro Genome Medicine, Vol. 14, Issue 1 https://doi.org/10.1186/s13073-022-01075-1	journal	June 2022
Bayesian Data Analysis Gelman, Andrew; Carlin, John B.; Stern, Hal S. Chapman and Hall/CRC https://doi.org/10.1201/9780429258480	book	July 2003
Pseudolikelihood for Exponential Family Models of Spatial Point Processes Jensen, Jens Ledet; Moller, Jesper The Annals of Applied Probability, Vol. 1, Issue 3 https://doi.org/10.1214/aoap/1177005877	journal	August 1991
Three-dimensional analysis of single molecule FISH in human colon organoids Omerzu, Manja; Fenderico, Nicola; de Barbanson, Buys Biology Open https://doi.org/10.1242/bio.042812	journal	January 2019
FISH-quant v2: a scalable and modular tool for smFISH image analysis Imbert, Arthur; Ouyang, Wei; Safieddine, Adham RNA, Vol. 28, Issue 6 https://doi.org/10.1261/rna.079073.121	journal	March 2022
Stochastic mRNA Synthesis in Mammalian Cells Raj, Arjun; Peskin, Charles S.; Tranchina, Daniel PLoS Biology, Vol. 4, Issue 10 https://doi.org/10.1371/journal.pbio.0040309	journal	September 2006
The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments Fox, Zachary R.; Munsky, Brian PLOS Computational Biology, Vol. 15, Issue 1 https://doi.org/10.1371/journal.pcbi.1006365	journal	January 2019
RNA velocity unraveled Gorin, Gennady; Fang, Meichen; Chari, Tara PLOS Computational Biology, Vol. 18, Issue 9 https://doi.org/10.1371/journal.pcbi.1010492	journal	September 2022
Monte Carlo samplers for efficient network inference Kilic, Zeliha; Schweiger, Max; Moyer, Camille PLOS Computational Biology, Vol. 19, Issue 7 https://doi.org/10.1371/journal.pcbi.1011256	journal	July 2023
The specious art of single-cell genomics Chari, Tara; Pachter, Lior PLOS Computational Biology, Vol. 19, Issue 8 https://doi.org/10.1371/journal.pcbi.1011288	journal	August 2023
Turbo FISH: A Method for Rapid Single Molecule RNA FISH Shaffer, Sydney M.; Wu, Min-Tzu; Levesque, Marshall J. PLoS ONE, Vol. 8, Issue 9 https://doi.org/10.1371/journal.pone.0075120	journal	September 2013
Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models Singh, Abhyudai; Soltani, Mohammad PLoS ONE, Vol. 8, Issue 12 https://doi.org/10.1371/journal.pone.0084301	journal	December 2013
Live‐cell imaging reveals the interplay between transcription factors, nucleosomes, and bursting Donovan, Benjamin T.; Huynh, Anh; Ball, David A. The EMBO Journal, Vol. 38, Issue 12 https://doi.org/10.15252/embj.2018100809	journal	May 2019
Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise Vo, Huy D.; Forero-Quintero, Linda S.; Aguilera, Luis U. Frontiers in Cell and Developmental Biology, Vol. 11 https://doi.org/10.3389/fcell.2023.1133994	journal	May 2023
Post-transcriptional splicing can occur in a slow-moving zone around the gene Coté, Allison; O'Farrell, Aoife; Dardani, Ian Dryad https://doi.org/10.5061/dryad.m0cfxppb7	dataset	January 2024
BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells Gรณmez-Schiavon, Mariana; Chen, Liang-Fu; West, Anne E. Figshare https://doi.org/10.6084/m9.figshare.c.3870658	collection	January 2017
Inferring gene regulatory networks from single-cell data: a mechanistic approach Herbach, Ulysse; Bonnaffoux, Arnaud; Espinasse, Thibault figshare https://doi.org/10.6084/m9.figshare.c.3935374.v1	collection	January 2017
Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions Fu, Xiaoming; Patel, Heta P.; Coppola, Stefano eLife, Vol. 11 https://doi.org/10.7554/eLife.82493	journal	October 2022
Post-transcriptional splicing can occur in a slow-moving zone around the gene Coté, Allison; O'Farrell, Aoife; Dardani, Ian eLife, Vol. 12 https://doi.org/10.7554/eLife.91357	journal	April 2024

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