Research article Special Issues

Analytic delay distributions for a family of gene transcription models

  • Received: 19 November 2023 Revised: 01 March 2024 Accepted: 24 April 2024 Published: 13 June 2024
  • Models intended to describe the time evolution of a gene network must somehow include transcription, the DNA-templated synthesis of RNA, and translation, the RNA-templated synthesis of proteins. In eukaryotes, the DNA template for transcription can be very long, often consisting of tens of thousands of nucleotides, and lengthy pauses may punctuate this process. Accordingly, transcription can last for many minutes, in some cases hours. There is a long history of introducing delays in gene expression models to take the transcription and translation times into account. Here we study a family of detailed transcription models that includes initiation, elongation, and termination reactions. We establish a framework for computing the distribution of transcription times, and work out these distributions for some typical cases. For elongation, a fixed delay is a good model provided elongation is fast compared to initiation and termination, and there are no sites where long pauses occur. The initiation and termination phases of the model then generate a nontrivial delay distribution, and elongation shifts this distribution by an amount corresponding to the elongation delay. When initiation and termination are relatively fast, the distribution of elongation times can be approximated by a Gaussian. A convolution of this Gaussian with the initiation and termination time distributions gives another analytic approximation to the transcription time distribution. If there are long pauses during elongation, because of the modularity of the family of models considered, the elongation phase can be partitioned into reactions generating a simple delay (elongation through regions where there are no long pauses), and reactions whose distribution of waiting times must be considered explicitly (initiation, termination, and motion through regions where long pauses are likely). In these cases, the distribution of transcription times again involves a nontrivial part and a shift due to fast elongation processes.

    Citation: S. Hossein Hosseini, Marc R. Roussel. Analytic delay distributions for a family of gene transcription models[J]. Mathematical Biosciences and Engineering, 2024, 21(6): 6225-6262. doi: 10.3934/mbe.2024273

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  • Models intended to describe the time evolution of a gene network must somehow include transcription, the DNA-templated synthesis of RNA, and translation, the RNA-templated synthesis of proteins. In eukaryotes, the DNA template for transcription can be very long, often consisting of tens of thousands of nucleotides, and lengthy pauses may punctuate this process. Accordingly, transcription can last for many minutes, in some cases hours. There is a long history of introducing delays in gene expression models to take the transcription and translation times into account. Here we study a family of detailed transcription models that includes initiation, elongation, and termination reactions. We establish a framework for computing the distribution of transcription times, and work out these distributions for some typical cases. For elongation, a fixed delay is a good model provided elongation is fast compared to initiation and termination, and there are no sites where long pauses occur. The initiation and termination phases of the model then generate a nontrivial delay distribution, and elongation shifts this distribution by an amount corresponding to the elongation delay. When initiation and termination are relatively fast, the distribution of elongation times can be approximated by a Gaussian. A convolution of this Gaussian with the initiation and termination time distributions gives another analytic approximation to the transcription time distribution. If there are long pauses during elongation, because of the modularity of the family of models considered, the elongation phase can be partitioned into reactions generating a simple delay (elongation through regions where there are no long pauses), and reactions whose distribution of waiting times must be considered explicitly (initiation, termination, and motion through regions where long pauses are likely). In these cases, the distribution of transcription times again involves a nontrivial part and a shift due to fast elongation processes.


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    [1] D. R. Larson, D. Zenklusen, B. Wu, J. A. Chao, R. H. Singer, Real-time observation of transcription initiation and elongation on an endogeneous yeast gene, Science, 332 (2011), 475–478. https://doi.org/10.1126/science.1202142 doi: 10.1126/science.1202142
    [2] S. Buratowski, S. Hahn, L. Guarente, P. A. Sharp, Five intermediate complexes in transcription initiation by RNA polymerase Ⅱ, Cell, 56 (1989), 549–561. https://doi.org/10.1016/0092-8674(89)90578-3 doi: 10.1016/0092-8674(89)90578-3
    [3] A. Dvir, J. W. Conaway, R. C. Conaway, Mechanism of transcription initiation and promoter escape by RNA polymerase Ⅱ, Curr. Opin. Genet. Dev., 11 (2001), 209–214. https://doi.org/10.1016/S0959-437X(00)00181-7 doi: 10.1016/S0959-437X(00)00181-7
    [4] X. Darzacq, Y. Shav-Tal, V. de Turris, Y. Brody, S. M. Shenoy, R. D. Phair, et al., In vivo dynamics of RNA polymerase Ⅱ transcription, Nat. Struct. Mol. Biol., 14 (2007), 796–806. https://doi.org/10.1038/nsmb1280 doi: 10.1038/nsmb1280
    [5] J. C. Venter, M. D. Adams, E. W. Myers, P. W. Li, R. J. Mural, G. G. Sutton, et al., The sequence of the human genome, Science, 291 (2001), 1304–1351.
    [6] C. N. Tennyson, H. J. Klamut, R. G. Worton, The human dystrophin gene requires 16 hours to be transcribed and is cotranscriptionally spliced, Nat. Genet., 9 (1995), 184–190. https://doi.org/10.1038/ng0295-184 doi: 10.1038/ng0295-184
    [7] J.-F. Lemay, F. Bachand, Fail-safe transcription termination: Because one is never enough, RNA Biol., 12 (2015), 927–932. https://doi.org/10.1080/15476286.2015.1073433 doi: 10.1080/15476286.2015.1073433
    [8] R. Ben-Yishay, Y. Shav-Tal, The dynamic lifecycle of mRNA in the nucleus, Curr. Opin. Cell Biol., 58 (2019), 69–75. https://doi.org/10.1016/j.ceb.2019.02.007 doi: 10.1016/j.ceb.2019.02.007
    [9] B. Daneholt, Assembly and transport of a premessenger RNP particle, Proc. Natl. Acad. Sci. U.S.A., 98 (2001), 7012–7017. https://doi.org/10.1073/pnas.111145498 doi: 10.1073/pnas.111145498
    [10] J. Sheinberger, Y. Shav-Tal, The dynamic pathway of nuclear RNA in eukaryotes, Nucleus, 4 (2013), 195–205. https://doi.org/10.4161/nucl.24434 doi: 10.4161/nucl.24434
    [11] A. Chaudhuri, S. Das, B. Das, Localization elements and zip codes in the intracellular transport and localization of messenger RNAs in Saccharomyces cerevisiae, WIREs RNA, 11 (2020), e1591.
    [12] U. Schmidt, E. Basyuk, M.-C. Robert, M. Yoshida, J.-P. Villemin, D. Auboeuf, et al., Real-time imaging of cotranscriptional splicing reveals a kinetic model that reduces noise: Implications for alternative splicing regulation, J. Cell Biol., 193 (2011), 819–829. https://doi.org/10.1083/jcb.201009012 doi: 10.1083/jcb.201009012
    [13] P. Cramer, A. Srebrow, S. Kadener, S. Werbajh, M. de la Mata, G. Melen, et al., Coordination between transcription and pre-mRNA processing, FEBS Lett., 498 (2001), 179–182. https://doi.org/10.1016/S0014-5793(01)02485-1 doi: 10.1016/S0014-5793(01)02485-1
    [14] A. Babour, C. Dargemont, F. Stutz, Ubiquitin and assembly of export competent mRNP, Biochim. Biophys. Acta, 1819 (2012), 521–530. https://doi.org/10.1016/j.bbagrm.2011.12.006 doi: 10.1016/j.bbagrm.2011.12.006
    [15] R. A. Coleman, B. F. Pugh, Slow dimer dissociation of the TATA binding protein dictates the kinetics of DNA binding, Proc. Natl. Acad. Sci. U.S.A., 94 (1997), 7221–7226. https://doi.org/10.1073/pnas.94.14.7221 doi: 10.1073/pnas.94.14.7221
    [16] J. F. Kugel, J. A. Goodrich, A kinetic model for the early steps of RNA synthesis by human RNA polymerase Ⅱ, J. Biol. Chem., 275 (2000), 40483–40491. https://doi.org/10.1074/jbc.M006401200 doi: 10.1074/jbc.M006401200
    [17] A. Kalo, I. Kanter, A. Shraga, J. Sheinberger, H. Tzemach, N. Kinor, et al., Cellular levels of signaling factors are sensed by $\beta$-actin alleles to modulate transcriptional pulse intensity, Cell Rep., 11 (2015), 419–432. https://doi.org/10.1016/j.celrep.2015.03.039 doi: 10.1016/j.celrep.2015.03.039
    [18] R. D. Bliss, P. R. Painter, A. G. Marr, Role of feedback inhibition in stabilizing the classical operon, J. Theor. Biol., 97 (1982), 177–193. https://doi.org/10.1016/0022-5193(82)90098-4 doi: 10.1016/0022-5193(82)90098-4
    [19] F. Buchholtz, F. W. Schneider, Computer simulation of T3/T7 phage infection using lag times, Biophys. Chem., 26 (1987), 171–179. https://doi.org/10.1016/0301-4622(87)80020-0 doi: 10.1016/0301-4622(87)80020-0
    [20] S. N. Busenberg, J. M. Mahaffy, The effects of dimension and size for a compartmental model of repression, SIAM J. Appl. Math., 48 (1988), 882–903. https://doi.org/10.1137/0148049 doi: 10.1137/0148049
    [21] J. Lewis, Autoinhibition with transcriptional delay: A simple mechanism for the zebrafish somitogenesis oscillator, Curr. Biol., 13 (2003), 1398–1408. https://doi.org/10.1016/S0960-9822(03)00534-7 doi: 10.1016/S0960-9822(03)00534-7
    [22] N. A. M. Monk, Oscillatory expression of Hes1, p53, and NF-$\kappa$B driven by transcriptional time delays, Curr. Biol., 13 (2003), 1409–1413. https://doi.org/10.1016/S0960-9822(03)00494-9 doi: 10.1016/S0960-9822(03)00494-9
    [23] L.-J. Chiu, M.-Y. Ling, E.-H. Wu, C.-X. You, S.-T. Lin, C.-C. Shu, The distributed delay rearranges the bimodal distribution at protein level, J. Taiwan Inst. Chem. Eng., 137 (2022), 104436. https://doi.org/10.1016/j.jtice.2022.104436 doi: 10.1016/j.jtice.2022.104436
    [24] M. Jansen, P. Pfaffelhuber, Stochastic gene expression with delay, J. Theor. Biol., 364 (2015), 355–363. https://doi.org/10.1016/j.jtbi.2014.09.031 doi: 10.1016/j.jtbi.2014.09.031
    [25] K. Rateitschak, O. Wolkenhauer, Intracellular delay limits cyclic changes in gene expression, Math. Biosci., 205 (2007), 163–179. https://doi.org/10.1016/j.mbs.2006.08.010 doi: 10.1016/j.mbs.2006.08.010
    [26] M. R. Roussel, On the distribution of transcription times, BIOMATH, 2 (2013), 1307247. https://doi.org/10.11145/j.biomath.2013.07.247 doi: 10.11145/j.biomath.2013.07.247
    [27] M. R. Roussel, R. Zhu, Stochastic kinetics description of a simple transcription model, Bull. Math. Biol., 68 (2006), 1681–1713. https://doi.org/10.1007/s11538-005-9048-6 doi: 10.1007/s11538-005-9048-6
    [28] S. Vashishtha, Stochastic modeling of eukaryotic transcription at the single nucleotide level, M.Sc. thesis, University of Lethbridge, 2011, URL https://www.uleth.ca/dspace/handle/10133/3190.
    [29] V. Pelechano, S. Chávez, J. E. Pérez-Ortín, A complete set of nascent transcription rates for yeast genes, PLoS One, 5 (2010), e15442. https://doi.org/10.1371/journal.pone.0015442 doi: 10.1371/journal.pone.0015442
    [30] T. Muramoto, D. Cannon, M. Gierliński, A. Corrigan, G. J. Barton, J. R. Chubb, Live imaging of nascent RNA dynamics reveals distinct types of transcriptional pulse regulation, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 7350–7355. https://doi.org/10.1073/pnas.1117603109 doi: 10.1073/pnas.1117603109
    [31] A. Raj, C. S. Peskin, D. Tranchina, D. Y. Vargas, S. Tyagi, Stochastic mRNA synthesis in mammalian cells, PLoS Biol., 4 (2006), e309. https://doi.org/10.1371/journal.pbio.0040309 doi: 10.1371/journal.pbio.0040309
    [32] D. M. Suter, N. Molina, D. Gatfield, K. Schneider, U. Schibler, F. Naef, Mammalian genes are transcribed with widely different bursting kinetics, Science, 332 (2011), 472–474. https://doi.org/10.1126/science.1198817 doi: 10.1126/science.1198817
    [33] I. Jonkers, H. Kwak, J. T. Lis, Genome-wide dynamics of Pol Ⅱ elongation and its interplay with promoter proximal pausing, chromatin, and exons, eLife, 3 (2014), e02407. https://doi.org/10.7554/eLife.02407 doi: 10.7554/eLife.02407
    [34] P. K. Parua, G. T. Booth, M. Sansó, B. Benjamin, J. C. Tanny, J. T. Lis, et al., A Cdk9-PP1 switch regulates the elongation-termination transition of RNA polymerase Ⅱ, Nature, 558 (2018), 460–464. https://doi.org/10.1038/s41586-018-0214-z doi: 10.1038/s41586-018-0214-z
    [35] L. Bai, R. M. Fulbright, M. D. Wang, Mechanochemical kinetics of transcription elongation, Phys. Rev. Lett., 98 (2007), 068103. https://doi.org/10.1103/PhysRevLett.98.068103 doi: 10.1103/PhysRevLett.98.068103
    [36] L. Bai, A. Shundrovsky, M. D. Wang, Sequence-dependent kinetic model for transcription elongation by RNA polymerase, J. Mol. Biol., 344 (2004), 335–349. https://doi.org/10.1016/j.jmb.2004.08.107 doi: 10.1016/j.jmb.2004.08.107
    [37] F. Jülicher, R. Bruinsma, Motion of RNA polymerase along DNA: a stochastic model, Biophys. J., 74 (1998), 1169–1185. https://doi.org/10.1016/S0006-3495(98)77833-6 doi: 10.1016/S0006-3495(98)77833-6
    [38] H.-Y. Wang, T. Elston, A. Mogilner, G. Oster, Force generation in RNA polymerase, Biophys. J., 74 (1998), 1186–1202. https://doi.org/10.1016/S0006-3495(98)77834-8 doi: 10.1016/S0006-3495(98)77834-8
    [39] T. D. Yager, P. H. Von Hippel, A thermodynamic analysis of RNA transcript elongation and termination in Escherichia coli, Biochemistry, 30 (1991), 1097–1118. https://doi.org/10.1021/bi00218a032 doi: 10.1021/bi00218a032
    [40] S. J. Greive, J. P. Goodarzi, S. E. Weitzel, P. H. von Hippel, Development of a "modular" scheme to describe the kinetics of transcript elongation by RNA polymerase, Biophys. J., 101 (2011), 1155–1165. https://doi.org/10.1016/j.bpj.2011.07.042 doi: 10.1016/j.bpj.2011.07.042
    [41] T. Filatova, N. Popovic, R. Grima, Statistics of nascent and mature rna fluctuations in a stochastic model of transcriptional initiation, elongation, pausing, and termination, Bull. Math. Biol., 83 (2021), 3. https://doi.org/10.1007/s11538-020-00827-7 doi: 10.1007/s11538-020-00827-7
    [42] A. N. Boettiger, P. L. Ralph, S. N. Evans, Transcriptional regulation: Effects of promoter proximal pausing on speed, synchrony and reliability, PLoS Comput. Biol., 7 (2011), e1001136. https://doi.org/10.1371/journal.pcbi.1001136 doi: 10.1371/journal.pcbi.1001136
    [43] X. Xu, N. Kumar, A. Krishnan, R. V. Kulkarni, Stochastic modeling of dwell-time distributions during transcriptional pausing and initiation, in 52nd IEEE Conference on Decision and Control, 2013, 4068–4073.
    [44] M. Hamano, Stochastic transcription elongation via rule based modelling, Electron. Notes Theor. Comput. Sci., 326 (2016), 73–88. https://doi.org/10.1016/j.entcs.2016.09.019 doi: 10.1016/j.entcs.2016.09.019
    [45] S. Klumpp, T. Hwa, Stochasticity and traffic jams in the transcription of ribosomal RNA: Intriguing role of termination and antitermination, Proceedings of the National Academy of Sciences.
    [46] A. S. Ribeiro, O.-P. Smolander, T. Rajala, A. Häkkinen, O. Yli-Harja, Delayed stochastic model of transcription at the single nucleotide level, J. Computat. Biol., 16 (2009), 539–553. https://doi.org/10.1089/cmb.2008.0153 doi: 10.1089/cmb.2008.0153
    [47] M. J. Schilstra, C. L. Nehaniv, Stochastic model of template-directed elongation processes in biology, BioSystems, 102 (2010), 55–60. https://doi.org/10.1016/j.biosystems.2010.07.006 doi: 10.1016/j.biosystems.2010.07.006
    [48] A. Garai, D. Chowdhury, D. Chowdhury, T. V. Ramakrishnan, Stochastic kinetics of ribosomes: single motor properties and collective behavior, Phys. Rev. E, 80 (2009), 011908. https://doi.org/10.1103/PhysRevE.79.011916 doi: 10.1103/PhysRevE.79.011916
    [49] A. Garai, D. Chowdhury, T. V. Ramakrishnan, Fluctuations in protein synthesis from a single RNA template: Stochastic kinetics of ribosomes, Phys. Rev. E, 79 (2009), 011916. https://doi.org/10.1103/PhysRevE.79.011916 doi: 10.1103/PhysRevE.79.011916
    [50] L. Mier-y-Terán-Romero, M. Silber, V. Hatzimanikatis, The origins of time-delay in template biopolymerization processes, PLoS Comput. Biol., 6 (2010), e1000726. https://doi.org/10.1371/journal.pcbi.1000726 doi: 10.1371/journal.pcbi.1000726
    [51] L. S. Churchman, J. S. Weissman, Nascent transcript sequencing visualizes transcription at nucleotide resolution, Nature, 469 (2011), 368–373.
    [52] K. C. Neuman, E. A. Abbondanzieri, R. Landick, J. Gelles, S. M. Block, Ubiquitous transcriptional pausing is independent of RNA polymerase backtracking, Cell, 115 (2003), 437 – 447. https://doi.org/10.1016/S0092-8674(03)00845-6 doi: 10.1016/S0092-8674(03)00845-6
    [53] R. Landick, The regulatory roles and mechanism of transcriptional pausing, Biochem. Soc. Trans., 34 (2006), 1062–1066. https://doi.org/10.1042/BST0341062 doi: 10.1042/BST0341062
    [54] V. Epshtein, F. Toulmé, A. R. Rahmouni, S. Borukhov, E. Nudler, Transcription through the roadblocks: the role of RNA polymerase cooperation, EMBO J., 22 (2003), 4719–4727. https://doi.org/10.1093/emboj/cdg452 doi: 10.1093/emboj/cdg452
    [55] S. Klumpp, Pausing and backtracking in transcription under dense traffic conditions, J. Stat. Phys., 142 (2011), 1252–1267. https://doi.org/10.1007/s10955-011-0120-3 doi: 10.1007/s10955-011-0120-3
    [56] M. Voliotis, N. Cohen, C. Molina-París, T. B. Liverpool, Fluctuations, pauses, and backtracking in DNA transcription, Biophys. J., 94 (2008), 334–348.
    [57] J. Li, D. S. Gilmour, Promoter proximal pausing and the control of gene expression, Curr. Opin. Genet. Dev., 21 (2011), 231–235.
    [58] S. Nechaev, K. Adelman, Pol Ⅱ waiting in the starting gates: Regulating the transition from transcription initiation into productive elongation, Biochim. Biophys. Acta, 1809 (2011), 34 – 45.
    [59] P. B. Rahl, C. Y. Lin, A. C. Seila, R. A. Flynn, S. McCuine, C. B. Burge, et al., c-Myc regulates transcriptional pause release, Cell, 141 (2010), 432 – 445. https://doi.org/10.1016/j.cell.2010.03.030 doi: 10.1016/j.cell.2010.03.030
    [60] P. Feng, A. Xiao, M. Fang, F. Wan, S. Li, P. Lang, et al., A machine learning-based framework for modeling transcription elongation, Proc. Natl. Acad. Sci. U.S.A., 118 (2021), e2007450118. https://doi.org/10.1073/pnas.2007450118 doi: 10.1073/pnas.2007450118
    [61] B. Zamft, L. Bintu, T. Ishibashi, C. Bustamante, Nascent RNA structure modulates the transcriptional dynamics of RNA polymerases, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 8948–8953. https://doi.org/10.1073/pnas.1205063109 doi: 10.1073/pnas.1205063109
    [62] R. D. Alexander, S. A. Innocente, J. D. Barrass, J. D. Beggs, Splicing-dependent RNA polymerase pausing in yeast, Mol. Cell, 40 (2010), 582–593. https://doi.org/10.1016/j.molcel.2010.11.005 doi: 10.1016/j.molcel.2010.11.005
    [63] N. Gromak, S. West, N. J. Proudfoot, Pause sites promote transcriptional termination of mammalian RNA polymerase Ⅱ, Mol. Cell. Biol., 26 (2006), 3986–3996. https://doi.org/10.1128/MCB.26.10.3986-3996.2006 doi: 10.1128/MCB.26.10.3986-3996.2006
    [64] N. MacDonald, Time delay in prey-predator models, Math. Biosci., 28 (1976), 321–330. https://doi.org/10.1016/0025-5564(76)90130-9 doi: 10.1016/0025-5564(76)90130-9
    [65] N. MacDonald, Time lag in a model of a biochemical reaction sequence with end product inhibition, J. Theor. Biol., 67 (1977), 549–556. https://doi.org/10.1016/0022-5193(77)90056-X doi: 10.1016/0022-5193(77)90056-X
    [66] N. MacDonald, Biological Delay Systems: Linear Stability Theory, Cambridge, Cambridge, 1989.
    [67] M. Barrio, A. Leier, T. T. Marquez-Lago, Reduction of chemical reaction networks through delay distributions, J. Chem. Phys., 138 (2013), 104114. https://doi.org/10.1063/1.4793982 doi: 10.1063/1.4793982
    [68] A. Leier, M. Barrio, T. T. Marquez-Lago, Exact model reduction with delays: Closed-form distributions and extensions to fully bi-directional monomolecular reactions, J. R. Soc. Interface, 11 (2014), 20140108. https://doi.org/10.1098/rsif.2014.0108 doi: 10.1098/rsif.2014.0108
    [69] I. R. Epstein, Differential delay equations in chemical kinetics: Some simple linear model systems, J. Chem. Phys., 92 (1990), 1702–1712. https://doi.org/10.1063/1.458052 doi: 10.1063/1.458052
    [70] D. Bratsun, D. Volfson, L. S. Tsimring, J. Hasty, Delay-induced stochastic oscillations in gene regulation, Proc. Natl. Acad. Sci. U.S.A., 102 (2005), 14593–14598. https://doi.org/10.1073/pnas.0503858102 doi: 10.1073/pnas.0503858102
    [71] M. R. Roussel, R. Zhu, Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression, Phys. Biol., 3 (2006), 274. https://doi.org/10.1088/1478-3975/3/4/005 doi: 10.1088/1478-3975/3/4/005
    [72] B. H. Jennings, Pausing for thought: Disrupting the early transcription elongation checkpoint leads to developmental defects and tumourigenesis, BioEssays, 35 (2013), 553–560. https://doi.org/10.1002/bies.201200179 doi: 10.1002/bies.201200179
    [73] H. Kwak, N. J. Fuda, L. J. Core, J. T. Lis, Precise maps of RNA polymerase reveal how promoters direct initiation and pausing, Science, 339 (2013), 950–953. https://doi.org/10.1126/science.1229386 doi: 10.1126/science.1229386
    [74] A. R. Hieb, S. Baran, J. A. Goodrich, J. F. Kugel, An 8nt RNA triggers a rate-limiting shift of RNA polymerase Ⅱ complexes into elongation, EMBO J., 25 (2006), 3100–3109. https://doi.org/10.1038/sj.emboj.7601197 doi: 10.1038/sj.emboj.7601197
    [75] T. J. Stasevich, Y. Hayashi-Takanaka, Y. Sato, K. Maehara, Y. Ohkawa, K. Sakata-Sogawa, et al., Regulation of RNA polymerase Ⅱ activation by histone acetylation in single living cells, Nature, 516 (2014), 272–275. https://doi.org/10.1038/nature13714 doi: 10.1038/nature13714
    [76] B. Steurer, R. C. Janssens, B. Geverts, M. E. Geijer, F. Wienholz, A. F. Theil, et al., Live-cell analysis of endogeneous GFP-RPB1 uncovers rapid turnover of initiating and promoter-paused RNA polymerase Ⅱ, Proc. Natl. Acad. Sci. U.S.A., 115 (2018), E4368–E4376. https://doi.org/10.1073/pnas.1717920115 doi: 10.1073/pnas.1717920115
    [77] J. Liu, D. Hansen, E. Eck, Y. J. Kim, M. Turner, S. Alamos, et al., Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage, PLoS Comput. Biol., 17 (2021), e1008999. https://doi.org/10.1371/journal.pcbi.1008999 doi: 10.1371/journal.pcbi.1008999
    [78] A. Kremling, Comment on mathematical models which describe transcription and calculate the relationship between mrna and protein expression ratio, Biotech. Bioeng., 96 (2007), 815–819. https://doi.org/10.1002/bit.21065 doi: 10.1002/bit.21065
    [79] N. Mitarai, S. Pedersen, Control of ribosome traffic by position-dependent choice of synonymous codons, Phys. Biol., 10 (2013), 056011. https://doi.org/10.1088/1478-3975/10/5/056011 doi: 10.1088/1478-3975/10/5/056011
    [80] M. R. Roussel, The use of delay differential equations in chemical kinetics, J. Phys. Chem., 100 (1996), 8323–8330. https://doi.org/10.1021/jp9600672 doi: 10.1021/jp9600672
    [81] R. A. Coleman, B. F. Pugh, Evidence for functional binding and stable sliding of the TATA binding protein on nonspecific DNA, J. Biol. Chem., 270 (1995), 13850–13859. https://doi.org/10.1074/jbc.270.23.13850 doi: 10.1074/jbc.270.23.13850
    [82] A. Dasgupta, S. A. Juedes, R. O. Sprouse, D. T. Auble, Mot1-mediated control of transcription complex assembly and activity, EMBO J., 24 (2005), 1717–1729. https://doi.org/10.1038/sj.emboj.7600646 doi: 10.1038/sj.emboj.7600646
    [83] R. O. Sprouse, T. S. Karpova, F. Mueller, A. Dasgupta, J. G. McNally, D. T. Auble, Regulation of TATA-binding protein dynamics in living yeast cells, Proc. Natl. Acad. Sci. U.S.A., 105 (2008), 13304–13308. https://doi.org/10.1073/pnas.0801901105 doi: 10.1073/pnas.0801901105
    [84] S. H. Hosseini, Analytic Solutions for Stochastic Models of Transcription, Master's thesis, University of Lethbridge, 2016, URL https://www.uleth.ca/dspace/handle/10133/4791.
    [85] H.-J. Woo, Analytical theory of the nonequilibrium spatial distribution of RNA polymerase translocations, Phys. Rev. E, 74 (2006), 011907. https://doi.org/10.1103/PhysRevE.74.011907 doi: 10.1103/PhysRevE.74.011907
    [86] M. H. Larson, J. Zhou, C. D. Kaplan, M. Palangat, R. D. Kornberg, R. Landick, et al., Trigger loop dynamics mediate the balance between the transcriptional fidelity and speed of RNA polymerase Ⅱ, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 6555–6560. https://doi.org/10.1073/pnas.1200939109 doi: 10.1073/pnas.1200939109
    [87] A. C. M. Cheung, P. Cramer, Structural basis of RNA polymerase Ⅱ backtracking, arrest and reactivation, Nature, 471 (2011), 249–253. https://doi.org/10.1038/nature09785 doi: 10.1038/nature09785
    [88] G. Brzyżek, S. Świeżewski, Mutual interdependence of splicing and transcription elongation, Transcription, 6 (2015), 37–39. https://doi.org/10.1080/21541264.2015.1040146 doi: 10.1080/21541264.2015.1040146
    [89] M. Imashimizu, M. L. Kireeva, L. Lubkowska, D. Gotte, A. R. Parks, J. N. Strathem, et al., Intrinsic translocation barrier as an initial step in pausing by RNA polymerase Ⅱ, J. Mol. Biol., 425 (2013), 697–712. https://doi.org/10.1016/j.jmb.2012.12.002 doi: 10.1016/j.jmb.2012.12.002
    [90] J. W. Roberts, Molecular basis of transcriptional pausing, Science, 344 (2014), 1226–1227. https://doi.org/10.1126/science.1255712 doi: 10.1126/science.1255712
    [91] J. Singh, R. A. Padgett, Rates of in situ transcription and splicing in large human genes, Nat. Struct. Mol. Biol., 16 (2009), 1128–1133. https://doi.org/10.1038/nsmb.1666 doi: 10.1038/nsmb.1666
    [92] E. Rosonina, S. Kaneko, J. L. Manley, Terminating the transcript: breaking up is hard to do, Genes Dev., 20 (2006), 1050–1056. https://doi.org/10.1101/gad.1431606 doi: 10.1101/gad.1431606
    [93] E. A. Abbondanzieri, W. J. Greenleaf, J. W. Shaevitz, R. Landick, S. M. Block, Direct observation of base-pair stepping by RNA polymerase, Nature, 438 (2005), 460–465. https://doi.org/10.1038/nature04268 doi: 10.1038/nature04268
    [94] L. M. Hsu, Promoter clearance and escape in prokaryotes, Biochim. Biophys. Acta, 1577 (2002), 191–207. https://doi.org/10.1016/S0167-4781(02)00452-9 doi: 10.1016/S0167-4781(02)00452-9
    [95] H. Kimura, K. Sugaya, P. R. Cook, The transcription cycle of RNA polymerase Ⅱ in living cells, J. Cell Biol., 159 (2002), 777–782. https://doi.org/10.1083/jcb.200206019 doi: 10.1083/jcb.200206019
    [96] H. A. Ferguson, J. F. Kugel, J. A. Goodrich, Kinetic and mechanistic analysis of the RNA polymerase Ⅱ transcription reaction at the human interleukin-2 promoter, J. Mol. Biol., 314 (2001), 993–1006. https://doi.org/10.1006/jmbi.2000.5215 doi: 10.1006/jmbi.2000.5215
    [97] D. A. Jackson, F. J. Iborra, E. M. M. Manders, P. R. Cook, Numbers and organization of RNA polymerases, nascent transcripts, and transcription units in HeLa nuclei, Mol. Biol. Cell, 9 (1998), 1523–1536. https://doi.org/10.1091/mbc.9.6.1523 doi: 10.1091/mbc.9.6.1523
    [98] P. J. Hurtado, A. S. Kirosingh, Generalizations of the 'linear chain trick': Incorporating more flexible dwell time distributions into mean field ODE models, J. Math. Biol., 79 (2019), 1831–1883. https://doi.org/10.1007/s00285-019-01412-w doi: 10.1007/s00285-019-01412-w
    [99] H. Golstein, Classical Mechanics, chapter 12, Addison-Wesley, Reading, Massachusetts, 1980.
    [100] H. G. Othmer, A continuum model for coupled cells, J. Math. Biol., 17 (1983), 351–369. https://doi.org/10.1007/BF00276521 doi: 10.1007/BF00276521
    [101] C. J. Roussel, M. R. Roussel, Reaction-diffusion models of development with state-dependent chemical diffusion coefficients, Prog. Biophys. Mol. Biol., 86 (2004), 113–160. https://doi.org/10.1016/j.pbiomolbio.2004.03.001 doi: 10.1016/j.pbiomolbio.2004.03.001
    [102] D. Sulsky, R. R. Vance, W. I. Newman, Time delays in age-structured populations, J. Theor. Biol., 141 (1989), 403–422. https://doi.org/10.1016/S0022-5193(89)80122-5 doi: 10.1016/S0022-5193(89)80122-5
    [103] G. Bel, B. Munsky, I. Nemenman, The simplicity of completion time distributions for common complex biochemical processes, Phys. Biol., 7 (2010), 016003. https://doi.org/10.1088/1478-3975/7/1/016003 doi: 10.1088/1478-3975/7/1/016003
    [104] P. Billingsley, Probability and Measure, Wiley, New York, 1995.
    [105] G. Bar-Nahum, V. Epshtein, A. E. Ruckenstein, R. Rafikov, A. Mustaev, E. Nudler, A ratchet mechanism of transcription elongation and its control, Cell, 120 (2005), 183–193. https://doi.org/10.1016/j.cell.2004.11.045 doi: 10.1016/j.cell.2004.11.045
    [106] J. W. Shaevitz, E. A. Abbondanzieri, R. Landick, S. M. Block, Backtracking by single RNA polymerase molecules observed at near-base-pair resolution, Nature, 426 (2003), 684–687. https://doi.org/10.1038/nature02191 doi: 10.1038/nature02191
    [107] M. A. Gibson, J. Bruck, Efficient exact stochastic simulation of chemical systems with many species and many channels, J. Phys. Chem. A, 104 (2000), 1876–1889. https://doi.org/10.1021/jp993732q doi: 10.1021/jp993732q
    [108] H. T. Banks, J. Catenacci, S. Hu, A comparison of stochastic systems with different types of delays, Stoch. Anal. Appl., 31 (2013), 913–955. https://doi.org/10.1080/07362994.2013.806217 doi: 10.1080/07362994.2013.806217
    [109] Y.-L. Feng, J.-M. Dong, X.-L. Tang, Non-Markovian effect on gene transcriptional systems, Chin. Phys. Lett., 33. https://doi.org/10.1088/0256-307X/33/10/108701
    [110] J. Lloyd-Price, A. Gupta, A. S. Ribeiro, Sgns2: A compartmental stochastic chemical kinetics simulator for dynamic cell populations, Bioinformatics, 28 (2012), 3004–3005. https://doi.org/10.1093/bioinformatics/bts556 doi: 10.1093/bioinformatics/bts556
    [111] T. Maarleveld, StochPy User Guide, Release 2.3.0, 2015, URL https://sourceforge.net/projects/stochpy/files/stochpy_userguide_2.3.pdf/download.
    [112] A. S. Ribeiro, J. Lloyd-Price, SGN Sim, a stochastic genetic networks simulator, Bioinformatics, 23 (2007), 777–779. https://doi.org/10.1093/bioinformatics/btm004 doi: 10.1093/bioinformatics/btm004
    [113] D. T. Gillespie, A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J. Comput. Phys., 22 (1976), 403–434. https://doi.org/10.1016/0021-9991(76)90041-3 doi: 10.1016/0021-9991(76)90041-3
    [114] R. J. Sims Ⅲ, R. Belotserkovskaya, D. Reinberg, Elongation by RNA polymerase Ⅱ: the short and long of it, Genes Dev., 18 (2004), 2437–2468. https://doi.org/10.1101/gad.1235904 doi: 10.1101/gad.1235904
    [115] E. A. M. Trofimenkoff, M. R. Roussel, Small binding-site clearance delays are not negligible in gene expression modeling, Math. Biosci., 325 (2020), 108376. https://doi.org/10.1016/j.mbs.2020.108376 doi: 10.1016/j.mbs.2020.108376
    [116] V. Epshtein, E. Nudler, Cooperation between RNA polymerase molecules in transcription elongation, Science, 300 (2003), 801–805. https://doi.org/10.1126/science.1083219 doi: 10.1126/science.1083219
    [117] C. Jia, L. Y. Wang, G. G. Yin, M. Q. Zhang, Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback, Phys. Rev. E, 100. https://doi.org/10.1103/PhysRevE.100.052406
    [118] J. Szavits-Nossan, R. Grima, Uncovering the effect of RNA polymerase steric interactions on gene expression noise: Analytical distributions of nascent and mature RNA numbers, Phys. Rev. E, 108 (2023), 034405. https://doi.org/10.1103/PhysRevE.108.034405 doi: 10.1103/PhysRevE.108.034405
    [119] P. Bokes, J. R. King, A. T. A. Wood, M. Loose, Transcriptional bursting diversifies the behaviour of a toggle switch: Hybrid simulation of stochastic gene expression, Bull. Math. Biol., 75 (2013), 351–371. https://doi.org/10.1007/s11538-013-9811-z doi: 10.1007/s11538-013-9811-z
    [120] M. Dobrzyński, F. J. Bruggeman, Elongation dynamics shape bursty transcription and translation, Proc. Natl. Acad. Sci. U.S.A., 106 (2009), 2583–2588. https://doi.org/10.1073/pnas.0803507106 doi: 10.1073/pnas.0803507106
    [121] L. Cai, N. Friedman, X. S. Xie, Stochastic protein expression in individual cells at the single molecule level, Nature, 440 (2006), 358–362. https://doi.org/10.1038/nature04599 doi: 10.1038/nature04599
    [122] A. J. M. Larsson, P. Johnsson, M. Hagemann-Jensen, L. Hartmanis, O. R. Faridani, B. Reinius, et al., Genomic encoding of transcriptional burst kinetics, Nature, 565 (2019), 251–254. https://doi.org/10.1038/s41586-018-0836-1 doi: 10.1038/s41586-018-0836-1
    [123] Y. Wan, D. G. Anastasakis, J. Rodriguez, M. Palangat, P. Gudla, G. Zaki, et al., Dynamic imaging of nascent RNA reveals general principles of transcription dynamics and stochastic splice site selection, Cell, 184 (2021), 2878–2895. https://doi.org/10.1016/j.cell.2021.04.012 doi: 10.1016/j.cell.2021.04.012
    [124] C. Jia, Y. Li, Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms, SIAM J. Appl. Math., 83 (2023), 1572–1602. https://doi.org/10.1137/22M147219X doi: 10.1137/22M147219X
    [125] B. W. Lindgren, G. W. McElrath, D. A. Berry, Introduction to Probability and Statistics, 154–156, 4th edition, Macmillan, New York, 1978.
    [126] M. Janisch, Kolmogorov's strong law of large numbers holds for pairwise uncorrelated random variables, Theory Probab. Appl., 66 (2021), 263–275. https://doi.org/10.4213/tvp5459 doi: 10.4213/tvp5459
    [127] B. Li, J. A. Weber, Y. Chen, A. L. Greenleaf, D. S. Gilmour, Analyses of promoter-proximal pausing by RNA polymerase Ⅱ on the hsp70 heat shock gene promoter in a Drosophila nuclear extract, Mol. Cell. Biol., 16 (1996), 5433–5443. https://doi.org/10.1128/MCB.16.10.5433 doi: 10.1128/MCB.16.10.5433
    [128] R.-J. Murphy, Stochastic Modeling of the Torpedo Mechanism of Eukaryotic Transcription Termination, Master's thesis, University of Lethbridge, 2017, URL https://www.uleth.ca/dspace/handle/10133/4906.
    [129] B. Choi, Y.-Y. Cheng, S. Cinar, W. Ott, M. R. Bennett, K. Josić, et al., Bayesian inference of distributed time delay in transcriptional and translational regulation, Bioinformatics, 36 (2020), 586–593. https://doi.org/10.1093/bioinformatics/btz574 doi: 10.1093/bioinformatics/btz574
    [130] H. Hong, M. J. Cortez, Y.-Y. Cheng, H. J. Kim, B. Choi, K. Josić, et al., Inferring delays in partially observed gene regulation processes, Bioinformatics, 39 (2023), btad670. https://doi.org/10.1093/bioinformatics/btad670 doi: 10.1093/bioinformatics/btad670
    [131] D. Holcman, Z. Schuss, The narrow escape problem, SIAM Rev., 56 (2014), 213–257. https://doi.org/10.1137/120898395
    [132] M. R. Roussel, T. Tang, Simulation of mRNA diffusion in the nuclear environment, IET Syst. Biol., 6 (2012), 125–133. https://doi.org/10.1049/iet-syb.2011.0032 doi: 10.1049/iet-syb.2011.0032
    [133] S. Tang, Mathematical Modeling of Eukaryotic Gene Expression, PhD thesis, University of Lethbridge, 2010, URL https://www.uleth.ca/dspace/handle/10133/2567.
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