Threshold-crossing time statistics for gene expression in growing cells

Many intracellular events are triggered by attaining critical concentrations of their corresponding regulatory proteins. How cells ensure precision in the timing of the protein accumulation is a fundamental problem, and contrasting predictions of different models can help us understand the mechanism...

Full description

Bibliographic Details
Main Authors:	Nieto, César, Raj Ghusinga, Khem, Vargas García, César, Singh, Abhyudai
Format:	article
Language:	Inglés
Published:	Cold Sprimg Harbor Laboratory (CSH) 2024
Subjects:	Genética y mejoramiento animal - L10 Cruzamiento Genética animal Célula Estadìsticas Ganadería y especies menores http://aims.fao.org/aos/agrovoc/c_1976 http://aims.fao.org/aos/agrovoc/c_49986 http://aims.fao.org/aos/agrovoc/c_1418 http://aims.fao.org/aos/agrovoc/c_49978
Online Access:	https://www.biorxiv.org/content/10.1101/2022.06.09.494908v1.full http://hdl.handle.net/20.500.12324/40374 https://doi.org/10.1101/2022.06.09.494908

id	RepoAGROSAVIA40374
record_format	dspace
institution	Corporación Colombiana de Investigación Agropecuaria
collection	Repositorio AGROSAVIA
language	Inglés
topic	Genética y mejoramiento animal - L10 Cruzamiento Genética animal Célula Estadìsticas Ganadería y especies menores http://aims.fao.org/aos/agrovoc/c_1976 http://aims.fao.org/aos/agrovoc/c_49986 http://aims.fao.org/aos/agrovoc/c_1418 http://aims.fao.org/aos/agrovoc/c_49978
spellingShingle	Genética y mejoramiento animal - L10 Cruzamiento Genética animal Célula Estadìsticas Ganadería y especies menores http://aims.fao.org/aos/agrovoc/c_1976 http://aims.fao.org/aos/agrovoc/c_49986 http://aims.fao.org/aos/agrovoc/c_1418 http://aims.fao.org/aos/agrovoc/c_49978 Nieto, César Raj Ghusinga, Khem Vargas García, César Singh, Abhyudai Threshold-crossing time statistics for gene expression in growing cells
description	Many intracellular events are triggered by attaining critical concentrations of their corresponding regulatory proteins. How cells ensure precision in the timing of the protein accumulation is a fundamental problem, and contrasting predictions of different models can help us understand the mechanisms involved in such processes. Here, we formulate the timing of protein threshold-crossing as a first passage time (FPT) problem focusing on how the mean FPT and its fluctuations depend on the threshold protein concentration. First, we model the protein-crossing dynamics from the perspective of three classical models of gene expression that do not explicitly accounts for cell growth but consider the dilution as equivalent to degradation: (birth-death process, discrete birth with continuous deterministic degradation, and Fokker-Planck approximation). We compare the resulting FPT statistics with a fourth model proposed by us (growing cell) that comprises size-dependent expression in an exponentially growing cell. When proteins accumulate in growing cells, their concentration reaches a steady value. We observe that if dilution by cell growth is modeled as degradation, cells can reach concentrations higher than this steady-state level at a finite time. In the growing cell model, on the other hand, the FPT moments diverge if the threshold is higher than the steady-state level. This effect can be interpreted as a transition between noisy dynamics when cells are small to an almost deterministic behavior when cells grow enough. We finally study the mean FPT that optimizes the timing precision. The growing cell model predicts a higher optimal FPT and less variability than the classical models.
format	article
author	Nieto, César Raj Ghusinga, Khem Vargas García, César Singh, Abhyudai
author_facet	Nieto, César Raj Ghusinga, Khem Vargas García, César Singh, Abhyudai
author_sort	Nieto, César
title	Threshold-crossing time statistics for gene expression in growing cells
title_short	Threshold-crossing time statistics for gene expression in growing cells
title_full	Threshold-crossing time statistics for gene expression in growing cells
title_fullStr	Threshold-crossing time statistics for gene expression in growing cells
title_full_unstemmed	Threshold-crossing time statistics for gene expression in growing cells
title_sort	threshold-crossing time statistics for gene expression in growing cells
publisher	Cold Sprimg Harbor Laboratory (CSH)
publishDate	2024
url	https://www.biorxiv.org/content/10.1101/2022.06.09.494908v1.full http://hdl.handle.net/20.500.12324/40374 https://doi.org/10.1101/2022.06.09.494908
work_keys_str_mv	AT nietocesar thresholdcrossingtimestatisticsforgeneexpressioningrowingcells AT rajghusingakhem thresholdcrossingtimestatisticsforgeneexpressioningrowingcells AT vargasgarciacesar thresholdcrossingtimestatisticsforgeneexpressioningrowingcells AT singhabhyudai thresholdcrossingtimestatisticsforgeneexpressioningrowingcells
_version_	1842255895074963456
spelling	RepoAGROSAVIA403742024-11-06T03:01:13Z Threshold-crossing time statistics for gene expression in growing cells Threshold-crossing time statistics for gene expression in growing cells Nieto, César Raj Ghusinga, Khem Vargas García, César Singh, Abhyudai Genética y mejoramiento animal - L10 Cruzamiento Genética animal Célula Estadìsticas Ganadería y especies menores http://aims.fao.org/aos/agrovoc/c_1976 http://aims.fao.org/aos/agrovoc/c_49986 http://aims.fao.org/aos/agrovoc/c_1418 http://aims.fao.org/aos/agrovoc/c_49978 Many intracellular events are triggered by attaining critical concentrations of their corresponding regulatory proteins. How cells ensure precision in the timing of the protein accumulation is a fundamental problem, and contrasting predictions of different models can help us understand the mechanisms involved in such processes. Here, we formulate the timing of protein threshold-crossing as a first passage time (FPT) problem focusing on how the mean FPT and its fluctuations depend on the threshold protein concentration. First, we model the protein-crossing dynamics from the perspective of three classical models of gene expression that do not explicitly accounts for cell growth but consider the dilution as equivalent to degradation: (birth-death process, discrete birth with continuous deterministic degradation, and Fokker-Planck approximation). We compare the resulting FPT statistics with a fourth model proposed by us (growing cell) that comprises size-dependent expression in an exponentially growing cell. When proteins accumulate in growing cells, their concentration reaches a steady value. We observe that if dilution by cell growth is modeled as degradation, cells can reach concentrations higher than this steady-state level at a finite time. In the growing cell model, on the other hand, the FPT moments diverge if the threshold is higher than the steady-state level. This effect can be interpreted as a transition between noisy dynamics when cells are small to an almost deterministic behavior when cells grow enough. We finally study the mean FPT that optimizes the timing precision. The growing cell model predicts a higher optimal FPT and less variability than the classical models. 2024-11-05T16:19:17Z 2024-11-05T16:19:17Z 2022-06 2022 article Artículo científico http://purl.org/coar/resource_type/c_2df8fbb1 info:eu-repo/semantics/article https://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 https://www.biorxiv.org/content/10.1101/2022.06.09.494908v1.full http://hdl.handle.net/20.500.12324/40374 https://doi.org/10.1101/2022.06.09.494908 reponame:Biblioteca Digital Agropecuaria de Colombia instname:Corporación colombiana de investigación agropecuaria AGROSAVIA eng BioRxiv 1 1 1 7 M. B. Elowitz, A. J. Levine, E. D. Siggia, and P. S. Swain, “Stochastic gene expression in a single cell,” Science, vol. 297, no. 5584, pp. 11831186, 2002. W. J. Blake, M. Kærn, C. R. Cantor, and J. J. Collins, “Noise in eukaryotic gene expression,” Nature, vol. 422, no. 6932, pp. 633–637, 2003. B. Schwanh¨ausser, D. Busse, N. Li, G. Dittmar, J. Schuchhardt, J. Wolf, W. Chen, and M. Selbach, “Global quantification of mammalian gene expression control,” Nature, vol. 473, no. 7347, pp. 337342, 2011. J. Paulsson, “Models of stochastic gene expression,” Physics of life reviews, vol. 2, no. 2, pp. 157–175, 2005. R. Dessalles, V. Fromion, and P. Robert, “Models of protein production along the cell cycle: An investigation of possible sources of noise,” Plos one, vol. 15, no. 1, p. e0226016, 2020. M. P. Swaffer, D. Chandler-Brown, M. Langhinrichs, G. Marinov, W. Greenleaf, A. Kundaje, K. M. Schmoller, and J. M. Skotheim, “Size-independent mrna synthesis and chromatin-based partitioning mechanisms generate and maintain constant amounts of protein per cell,” bioRxiv, 2020. ] C. Jia, A. Singh, and R. Grima, “Concentration fluctuations due to sizedependent gene expression and cell-size control mechanisms,” bioRxiv, 2021. M. Soltani, C. A. Vargas-Garcia, D. Antunes, and A. Singh, “Intercellular variability in protein levels from stochastic expression and noisy cell cycle processes,” PLoS computational biology, vol. 12, no. 8, p. e1004972, 2016. P. Thomas, G. Terradot, V. Danos, and A. Y. Weiße, “Sources, propagation and consequences of stochasticity in cellular growth,” Nature communications, vol. 9, no. 1, pp. 1–11, 2018. S. Klumpp, Z. Zhang, and T. Hwa, “Growth rate-dependent global effects on gene expression in bacteria,” Cell, vol. 139, no. 7, pp. 13661375, 2009. J. Lin and A. Amir, “Disentangling intrinsic and extrinsic gene expression noise in growing cells,” Physical Review Letters, vol. 126, no. 7, p. 078101, 2021. P. Thomas and V. Shahrezaei, “Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations,” Journal of the Royal Society Interface, vol. 18, no. 178, p. 20210274, 2021. C. Jia and R. Grima, “Frequency domain analysis of fluctuations of mrna and protein copy numbers within a cell lineage: theory and experimental validation,” Physical Review X, vol. 11, no. 2, p. 021032, 2021. C. H. Beentjes, R. Perez-Carrasco, and R. Grima, “Exact solution of stochastic gene expression models with bursting, cell cycle and replication dynamics,” Physical Review E, vol. 101, no. 3, p. 032403, 2020. D. Gomez, R. Marathe, V. Bierbaum, and S. Klumpp, “Modeling stochastic gene expression in growing cells,” Journal of theoretical biology, vol. 348, pp. 1–11, 2014. K. R. Ghusinga, J. J. Dennehy, and A. Singh, “First-passage time approach to controlling noise in the timing of intracellular events,” Proceedings of the National Academy of Sciences, vol. 114, no. 4, pp. 693–698, 2017. P. J. Piggot and D. W. Hilbert, “Sporulation of bacillus subtilis,” Current opinion in microbiology, vol. 7, no. 6, pp. 579–586, 2004. K. Sekar, R. Rusconi, J. T. Sauls, T. Fuhrer, E. Noor, J. Nguyen, V. I. Fernandez, M. F. Buffing, M. Berney, S. Jun, et al., “Synthesis and degradation of ftsz quantitatively predict the first cell division in starved bacteria,” Molecular systems biology, vol. 14, no. 11, p. e8623, 2018. F. Si, G. Le Treut, J. T. Sauls, S. Vadia, P. A. Levin, and S. Jun, “Mechanistic origin of cell-size control and homeostasis in bacteria,” Current Biology, vol. 29, no. 11, pp. 1760–1770, 2019. K. R. Ghusinga, C. A. Vargas-Garcia, and A. Singh, “A mechanistic stochastic framework for regulating bacterial cell division,” Scientific reports, vol. 6, no. 1, pp. 1–9, 2016. K. Carniol, P. Eichenberger, and R. Losick, “A threshold mechanism governing activation of the developmental regulatory protein σf in bacillus subtilis,” Journal of Biological Chemistry, vol. 279, no. 15, pp. 14860–14870, 2004. T. Koyama, M. Iwami, and S. Sakurai, “Ecdysteroid control of cell cycle and cellular commitment in insect wing imaginal discs,” Molecular and cellular endocrinology, vol. 213, no. 2, pp. 155–166, 2004. J. Roux, M. Hafner, S. Bandara, J. J. Sims, H. Hudson, D. Chai, and P. K. Sorger, “Fractional killing arises from cell-to-cell variability in overcoming a caspase activity threshold,” Molecular systems biology, vol. 11, no. 5, p. 803, 2015. Z. Vahdat, K. R. Ghusinga, and A. Singh, “Comparing feedback strategies for minimizing noise in gene expression event timing,” in 2021 29th Mediterranean Conference on Control and Automation (MED), pp. 450–455, IEEE, 2021. A. D. Co, M. C. Lagomarsino, M. Caselle, and M. Osella, “Stochastic timing in gene expression for simple regulatory strategies,” Nucleic acids research, vol. 45, no. 3, pp. 1069–1078, 2017. D. R. Rigney, “Stochastic model of constitutive protein levels in growing and dividing bacterial cells,” Journal of Theoretical Biology, vol. 76, no. 4, pp. 453–480, 1979. C. Nieto-Acu˜na, J. C. Arias-Castro, C. Vargas-Garc´ ıa, C. S´ anchez, and J. M. Pedraza, “Correlation between protein concentration and bacterial cell size can reveal mechanisms of gene expression,” Physical Biology, vol. 17, no. 4, p. 045002, 2020. X.-M. Sun, A. Bowman, M. Priestman, F. Bertaux, A. MartinezSegura, W. Tang, C. Whilding, D. Dormann, V. Shahrezaei, and S. Marguerat, “Size-dependent increase in rna polymerase ii initiation rates mediates gene expression scaling with cell size,” Current Biology, 2020. K.-L. Claude, D. Bureik, P. Adarska, A. Singh, and K. M. Schmoller, “Transcription coordinates histone amounts and genome content,” bioRxiv, 2020. Z. Vahdat, Z. Xu, and A. Singh, “Modeling protein concentrations in cycling cells using stochastic hybrid systems,” IFAC-PapersOnLine, vol. 54, no. 9, pp. 521–526, 2021. A. Singh and J. J. Dennehy, “Stochastic holin expression can account for lysis time variation in the bacteriophage λ,” Journal of The Royal Society Interface, vol. 11, no. 95, p. 20140140, 2014. K. Rijal, A. Prasad, A. Singh, and D. Das, “Exact distribution of threshold-crossing times for protein concentrations: Implication for biological timekeeping,” bioRxiv, 2021. S. Dey, S. Kannoly, P. Bokes, J. J. Dennehy, and A. Singh, “The role of incoherent feedforward circuits in regulating precision of event timing,” bioRxiv, 2020. T. Williams, “The basic birth-death model for microbial infections,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 27, no. 2, pp. 338–360, 1965. W. K. Sinclair and D. W. Ross, “Modes of growth in mammalian cells,” Biophysical journal, vol. 9, no. 8, pp. 1056–1070, 1969. S. Cooper, “Distinguishing between linear and exponential cell growth during the division cycle: single-cell studies, cell-culture studies, and the object of cell-cycle research,” Theoretical Biology and Medical Modelling, vol. 3, no. 1, pp. 1–15, 2006. N. Friedman, L. Cai, and X. S. Xie, “Linking stochastic dynamics to population distribution: an analytical framework of gene expression,” Physical review letters, vol. 97, no. 16, p. 168302, 2006. H. C. Tuckwell and F. Y. Wan, “First-passage time of markov processes to moving barriers,” Journal of applied probability, vol. 21, no. 4, pp. 695–709, 1984. R. Young, “Phage lysis: three steps, three choices, one outcome,” Journal of microbiology, vol. 52, no. 3, pp. 243–258, 2014. J. J. Dennehy and N. Wang, “Factors influencing lysis time stochasticity in bacteriophage λ,” BMC microbiology, vol. 11, no. 1, p. 174, 2011. S. Kannoly, T. Gao, S. Dey, N. Wang, A. Singh, and J. J. Dennehy, “Optimum threshold minimizes noise in timing of intracellular events,” Iscience, vol. 23, no. 6, p. 101186, 2020. Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf application/pdf Cold Sprimg Harbor Laboratory (CSH) BioRxiv; (2022): BioRxiv (Jun.);p. 1 - 7.

Threshold-crossing time statistics for gene expression in growing cells

Similar Items