Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T09:20:58.533Z Has data issue: false hasContentIssue false

Postponing production exponentially enhances the molecular memory of a stochastic switch

Published online by Cambridge University Press:  12 January 2021

PAVOL BOKES*
Affiliation:
Comenius University, Bratislava, Slovakia email: pavol.bokes@fmph.uniba.sk

Abstract

Delayed production can substantially alter the qualitative behaviour of feedback systems. Motivated by stochastic mechanisms in gene expression, we consider a protein molecule which is produced in randomly timed bursts, requires an exponentially distributed time to activate and then partakes in positive regulation of its burst frequency. Asymptotically analysing the underlying master equation in the large-delay regime, we provide tractable approximations to time-dependent probability distributions of molecular copy numbers. Importantly, the presented analysis demonstrates that positive feedback systems with large production delays can constitute a stable toggle switch even if they operate with low copy numbers of active molecules.

Type
Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Radhawi, M. A., Del Vecchio, D. & Sontag, E. D. (2019) Multi-modality in gene regulatory networks with slow promoter kinetics. Plos Comput. Biol. 15(2), e1006784.Google Scholar
Andreychenko, A., Bortolussi, L., Grima, R., Thomas, P. & Wolf, V. (2017) Distribution approximations for the chemical master equation: comparison of the method of moments and the system size expansion. In: Modeling Cellular Systems, Springer, pp. 3966.Google Scholar
Assaf, M. & Meerson, B. (2017) WKB theory of large deviations in stochastic populations. J. Phys. A: Math. Theor. 50(26), 263001.Google Scholar
Assaf, M., Roberts, E. & Luthey-Schulten, Z. (2011) Determining the stability of genetic switches: explicitly accounting for mRNA noise. Phys. Rev. Lett. 106(24), 248102.Google ScholarPubMed
Baker, R. E. & Röst, G. (2020) Global dynamics of a novel delayed logistic equation arising from cell biology. J. Nonlinear Sci. 30(1), 397418.CrossRefGoogle Scholar
Barrio, M., Burrage, K., Leier, A. & Tian, T. (2006) Oscillatory regulation of hes1: discrete stochastic delay modelling and simulation. PLoS Comput. Biol. 2, e117.CrossRefGoogle ScholarPubMed
Battich, N., Stoeger, T. & Pelkmans, L. (2015) Control of transcript variability in single mammalian cells. Cell 163(7), 15961610.Google ScholarPubMed
Becskei, A., Séraphin, B. & Serrano, L. (2001) Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 20, 25282535.Google ScholarPubMed
Belitsky, V. & Schütz, G. (2019) Rna polymerase interactions and elongation rate. J. Theor. Biol. 462, 370380.Google ScholarPubMed
Bokes, P., Borri, A., Palumbo, P. & Singh, A. (2020) Mixture distributions in a stochastic gene expression model with delayed feedback: a WKB approximation approach. J. Math. Biol. 81(1), 343367.Google Scholar
Borri, A., Palumbo, P. & Singh, A. (2019) Time delays in a genetic positive-feedback circuit. IEEE Control Syst. Lett. 4(1), 163168.CrossRefGoogle Scholar
Bressloff, P. C. (2014) Stochastic Processes in Cell Biology. Springer, New York.Google Scholar
Bressloff, P. C. (2017) Stochastic switching in biology: from genotype to phenotype. J. Phys. A: Math. Theor. 50(13), 133001.Google Scholar
Cai, L., Friedman, N. & Xie, X. (2006) Stochastic protein expression in individual cells at the single molecule level. Nature 440, 358362, 2006.CrossRefGoogle Scholar
Çelik, C., Bokes, P. & Singh, A. (2020) Stationary distributions and metastable behaviour for self-regulating proteins with general lifetime distributions. In: International Conference on Computational Methods in Systems Biology, Springer, pp. 2743.Google Scholar
Chong, K. H., Samarasinghe, S., Kulasiri, D. & Zheng, J. (2019) Mathematical modelling of core regulatory mechanism in p53 protein that activates apoptotic switch. J. Theor. Biol. 462, 134147.Google ScholarPubMed
Escudero, C. & Kamenev, A. (2009) Switching rates of multistep reactions. Phys. Rev. E 79(4), 041149.Google ScholarPubMed
Feng, J., Sevier, S. A., Huang, B., Jia, D. & Levine, H. (2016) Modeling delayed processes in biological systems. Phys. Rev. E 94(3), 032408.CrossRefGoogle ScholarPubMed
Gallego, M. & Virshup, D. M. (2007) Post-translational modifications regulate the ticking of the circadian clock. Nat. Rev. Mol. Cell Bio. 8(2), 139148.Google ScholarPubMed
Giaretta, A., Toffolo, G. M. & Elston, T. C. (2020) Stochastic modeling of human papillomavirusearly promoter gene regulation. J. Theor. Biol. 486, 110057.CrossRefGoogle ScholarPubMed
Gupta, C., López, J. M., Ott, W., Josić, K. & Bennett, M. R. (2013) Transcriptional delay stabilizes bistable gene networks. Phys. Rev. Lett. 111(5), 058104.CrossRefGoogle ScholarPubMed
Gupta, A., Mikelson, J. & Khammash, M. (2017) A finite state projection algorithm for the stationary solution of the chemical master equation. J. Chem. Phys. 147(15), 154101.CrossRefGoogle ScholarPubMed
Hart, A. G. & Tweedie, R. L. (2012) Convergence of invariant measures of truncation approximations to markov processes. Appl. Math. 3, 26113 (2012).Google Scholar
Haken, H. (1987) Synergetics. In: Self-Organizing Systems, Springer, pp. 417434.Google Scholar
Hansen, J. & Benenson, Y. (2016) Synthetic biology of cell signaling. Nat. Comput. 15(1), 513.Google Scholar
Heinrich, R., Neel, B. G. & Rapoport, T. A. (2002) Mathematical models of protein kinase signal transduction. Mol. Cell 9(5), 957970.Google ScholarPubMed
Herath, N. & Del Vecchio, D. (2018) Reduced linear noise approximation for biochemical reaction networks with time-scale separation: the stochastic tqssa+. J. Chem. Phys. 148(9), 094108.Google Scholar
Hinch, R. & Chapman, S. J. (2005) Exponentially slow transitions on a Markov chain: the frequency of calcium sparks. Eur. J. Appl. Math. 16(04), 427446.Google Scholar
Hufton, P. G., Lin, Y. T. & Galla, T. (2019) Classical stochastic systems with fast-switching environments: reduced master equations, their interpretation, and limits of validity. Phys. Rev. E 99(3), 032121.CrossRefGoogle ScholarPubMed
Hufton, P. G., Lin, Y. T. & Galla, T. (2019) Model reduction methods for population dynamics with fast-switching environments: reduced master equations, stochastic differential equations, and applications. Phys. Rev. E 99(3), 032122.CrossRefGoogle Scholar
Hufton, P. G., Lin, Y. T., Galla, T. & McKane, A. J. (2016) Intrinsic noise in systems with switching environments. Phys. Rev. E 93, 052119.Google ScholarPubMed
Jia, C. & Grima, R. (2020) Dynamical phase diagram of an auto-regulating gene in fast switching conditions. J. Chem. Phys. 152(17), 174110.CrossRefGoogle ScholarPubMed
Kepler, T. & Elston, T. (2001) Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. Biophys. J. 81, 31163136.CrossRefGoogle ScholarPubMed
Kerr, R., Jabbari, S. & Johnston, I. G. (2019) Intracellular energy variability modulates cellular decision-making capacity. Sci. Rep. 9(1), 112.CrossRefGoogle ScholarPubMed
Kevorkian, J. & Cole, J. (1981) Perturbation Methods in Applied Mathematics. Springer.CrossRefGoogle Scholar
Kumar, N. & Kulkarni, R. V. (2019) Constraining the complexity of promoter dynamics using fluctuations in gene expression. Phys. Biol. 17(1), 015001.CrossRefGoogle ScholarPubMed
Kurtz, T. G. (1972) The relationship between stochastic and deterministic models for chemical reactions. J. Chem. Phys. 57(7), 29762978.CrossRefGoogle Scholar
Kurasov, P., Lück, A., Mugnolo, D. & Wolf, V. (2018) Stochastic hybrid models of gene regulatory networks – a PDE approach. Math. Biosci. 305, 170177.CrossRefGoogle ScholarPubMed
Kyrychko, Y. & Schwartz, I. (2018) Enhancing noise-induced switching times in systems with distributed delays. Chaos 28(6), 063106.CrossRefGoogle ScholarPubMed
Lafuerza, L. & Toral, R. (2011) Role of delay in the stochastic creation process. Phys. Rev. E 84, 021128.CrossRefGoogle ScholarPubMed
Lestas, I., Paulsson, J., Ross, N. & Vinnicombe, G. (2008) Noise in gene regulatory networks. IEEE T. Circuits-I 53(1), 189200.Google Scholar
Li, G.-W. & Xie, X. S. (2011) Central dogma at the single-molecule level in living cells. Nature 475(7356), 308315.CrossRefGoogle Scholar
Lipniacki, T., Paszek, P., Marciniak-Czochra, A., Brasier, A. & Kimmel, M. (2006) Transcriptional stochasticity in gene expression. J. Theor. Biol. 238, 348367.CrossRefGoogle ScholarPubMed
Martinez-Corral, R., Raimundez, E., Lin, Y., Elowitz, M. B. & Garcia-Ojalvo, J. (2018) Self-amplifying pulsatile protein dynamics without positive feedback. Cell Syst. 7(4), 453462.CrossRefGoogle ScholarPubMed
Mor, A., Suliman, S., Ben-Yishay, R., Yunger, S., Brody, Y. & Shav-Tal, Y. (2010) Dynamics of single mRNP nucleocytoplasmic transport and export through the nuclear pore in living cells. Nat. Cell Biol. 12(6), 543.CrossRefGoogle ScholarPubMed
Munsky, B., Neuert, G. & Van Oudenaarden, A. (2012) Using gene expression noise to understand gene regulation. Science 336, 183187.CrossRefGoogle ScholarPubMed
Newby, J. (2015) Bistable switching asymptotics for the self regulating gene. J. Phys. A-math. Gen. 48, 185001.Google Scholar
Newby, J. & Chapman, S. J. (2014) Metastable behavior in Markov processes with internal states. J. Math. Biol. 69(4), 941976.CrossRefGoogle ScholarPubMed
Parmar, K., Blyuss, K. B., Kyrychko, Y. N. & Hogan, S. J. (2015) Time-delayed models of gene regulatory networks. Comput. Math. Method. M. 2015.CrossRefGoogle Scholar
Plesa, T., Erban, R. & Othmer, H. G. (2019) Noise-induced mixing and multimodality in reaction networks. Eur. J. Appl. Math. 30(5), 887911.CrossRefGoogle Scholar
Rodriguez, J. & Larson, D. R. (2020) Transcription in living cells: molecular mechanisms of bursting. Annu. Rev. Biochem. 89.CrossRefGoogle Scholar
Roussel, M. R. & Zhu, R. (2006) Stochastic kinetics description of a simple transcription model. B. Math. Biol. 68(7), 16811713.CrossRefGoogle ScholarPubMed
Satin, Y., Zeifman, A., Korotysheva, A. & Kiseleva, K. (2017) Two-sided truncations for a class of continuous-time Markov chains. In: Dudin, A. et al. (editors) International Conference on Information Technologies and Mathematical Modelling, Springer, pp. 312323.CrossRefGoogle Scholar
Smith, M. & Singh, A. (2020) Noise suppression by stochastic delays in negatively autoregulated gene expression. In: 2020 American Control Conference (ACC), IEEE, pp. 42704275.CrossRefGoogle Scholar
Smith, S., Cianci, C. & Grima, R. (2015) Model reduction for stochastic chemical systems with abundant species. J. Chem. Phys. 143(21), 12B615_1.CrossRefGoogle Scholar
Singh, A. & Bokes, P. (2012) Consequences of mRNA transport on stochastic variability in protein levels. Biophys. J. 103, 10871096.CrossRefGoogle ScholarPubMed
Soltani, M., Vargas-Garcia, C. A., Antunes, D. & Singh, A. (2016) Intercellular variability in protein levels from stochastic expression and noisy cell cycle processes. Plos Comput. Biol. 12(8), e1004972.CrossRefGoogle ScholarPubMed
Stoeger, T., Battich, N. & Pelkmans, L. (2016) Passive noise filtering by cellular compartmentalization. Cell 164(6), 11511161.CrossRefGoogle ScholarPubMed
Sturrock, M., Li, S. & Shahrezaei, V. (2017) The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression. J. Theor. Biol. 424, 5572.CrossRefGoogle ScholarPubMed
Taniguchi, Y., Choi, P., Li, G., Chen, H., Babu, M., Hearn, J., Emili, A. & Xie, X. (2010) Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science, 329, 533538.CrossRefGoogle ScholarPubMed
Thattai, M. & van Oudenaarden, A. (2001) Intrinsic noise in gene regulatory networks. P. Natl. Acad. Sci. USA 98, 151588598.CrossRefGoogle ScholarPubMed
Thomas, P. & Grima, R. (2015) Approximate probability distributions of the master equation. Phys. Rev. E 92, 012120.CrossRefGoogle ScholarPubMed
Thomas, P., Grima, R. & Straube, A. V. (2012) Rigorous elimination of fast stochastic variables from the linear noise approximation using projection operators. Phys. Rev. E 86(4), 041110.CrossRefGoogle ScholarPubMed
van Kampen, N. (2006) Stochastic Processes in Physics and Chemistry. Elsevier, Amsterdam.Google Scholar
Wallace, E., Gillespie, D., Sanft, K. R. & Petzold, L. (2012) Linear noise approximation is valid over limited times for any chemical system that is sufficiently large. IET Syst. Biol. 6(4), 102115.CrossRefGoogle ScholarPubMed
Wang, J., Lefranc, M. & Thommen, Q. (2014) Stochastic oscillations induced by intrinsic fluctuations in a self-repressing gene. Biophys. J. 107(10), 24032416.CrossRefGoogle Scholar
Warne, D. J., Baker, R. E. & Simpson, M. J. (2019) Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art. J. Roy. Soc. Interface 16(151), 20180943.CrossRefGoogle ScholarPubMed
Xin, Y., Cummins, B. & Gedeon, T. (2020) Multistability in the epithelial-mesenchymal transition network. BMC Bioinform. 21(1), 71.CrossRefGoogle ScholarPubMed
Zavala, E. & Marquez-Lago, T. T. (2014) Delays induce novel stochastic effects in negative feedback gene circuits. Biophys. J. 106(2), 467478.CrossRefGoogle ScholarPubMed