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 mech...
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Cold Sprimg Harbor Laboratory (CSH)
2024
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| Acceso en línea: | https://www.biorxiv.org/content/10.1101/2022.06.09.494908v1 http://hdl.handle.net/20.500.12324/39919 https://doi.org/10.1101/2022.06.09.494908 |
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RepoAGROSAVIA39919 |
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dspace |
| institution |
Corporación Colombiana de Investigación Agropecuaria |
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Repositorio AGROSAVIA |
| language |
Inglés |
| topic |
Investigación agropecuaria - A50 Salvelino Estadìsticas Variación genética Célula Transversal http://aims.fao.org/aos/agrovoc/c_1504 http://aims.fao.org/aos/agrovoc/c_49978 http://aims.fao.org/aos/agrovoc/c_15975 http://aims.fao.org/aos/agrovoc/c_1418 |
| spellingShingle |
Investigación agropecuaria - A50 Salvelino Estadìsticas Variación genética Célula Transversal http://aims.fao.org/aos/agrovoc/c_1504 http://aims.fao.org/aos/agrovoc/c_49978 http://aims.fao.org/aos/agrovoc/c_15975 http://aims.fao.org/aos/agrovoc/c_1418 Nieto, César Ghusinga, Khem Raj 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 Ghusinga, Khem Raj Vargas García, César Singh, Abhyudai |
| author_facet |
Nieto, César Ghusinga, Khem Raj 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 http://hdl.handle.net/20.500.12324/39919 https://doi.org/10.1101/2022.06.09.494908 |
| work_keys_str_mv |
AT nietocesar thresholdcrossingtimestatisticsforgeneexpressioningrowingcells AT ghusingakhemraj thresholdcrossingtimestatisticsforgeneexpressioningrowingcells AT vargasgarciacesar thresholdcrossingtimestatisticsforgeneexpressioningrowingcells AT singhabhyudai thresholdcrossingtimestatisticsforgeneexpressioningrowingcells |
| _version_ |
1842256239456681984 |
| spelling |
RepoAGROSAVIA399192024-08-24T03:02:28Z Threshold-crossing time statistics for gene expression in growing cells Threshold-crossing time statistics for gene expression in growing cells Nieto, César Ghusinga, Khem Raj Vargas García, César Singh, Abhyudai Investigación agropecuaria - A50 Salvelino Estadìsticas Variación genética Célula Transversal http://aims.fao.org/aos/agrovoc/c_1504 http://aims.fao.org/aos/agrovoc/c_49978 http://aims.fao.org/aos/agrovoc/c_15975 http://aims.fao.org/aos/agrovoc/c_1418 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. 2024-08-23T16:10:26Z 2024-08-23T16:10:26Z 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 http://hdl.handle.net/20.500.12324/39919 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 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. 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 C.I Tibaitatá Colombia Cold Sprimg Harbor Laboratory (CSH) BioRxiv; (2022): BioRxiv (June);p. 1-7. |