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Cited by 11Gene Regulatory Network Analysisstochastic dynamics and bifurcationStochastic processes and statistical mechanics

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Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives

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Zhang, Zhanhao, Zabaikina, Iryna, Nieto, Cesar, Vahdat, Zahra, Bokes, Pavol, Singh, Abhyudai·2025·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1013014PMC PMC12151482

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Abstract

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives can lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth. This protein inhibits growth at high concentrations, establishing a positive feedback loop. Using a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

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Research works printed but not linked: 125

  1. JM Pedraza, 2005, Noise propagation in gene networks, Science, 307, 1965, 10.1126/science.1109090 · DOI
  2. JM Raser, 2005, Noise in gene expression: origins, consequences, and control, Science, 309, 2010, 10.1126/science.1105891 · DOI
  3. WJ Blake, 2006, Phenotypic consequences of promoter-mediated transcriptional noise, Mol Cell, 24, 853, 10.1016/j.molcel.2006.11.003 · DOI
  4. A Raj, 2008, Nature, nurture, or chance: stochastic gene expression and its consequences, Cell, 135, 216, 10.1016/j.cell.2008.09.050 · DOI · Paper page
  5. Z Wang, 2011, Impact of gene expression noise on organismal fitness and the efficacy of natural selection, Proc Natl Acad Sci U S A, 108, 10.1073/pnas.1100059108 · DOI
  6. A Singh, 2009, Evolution of gene auto-regulation in the presence of noise, IET Syst Biol, 3, 368, 10.1049/iet-syb.2009.0002 · DOI
  7. B Lehner, 2008, Selection to minimise noise in living systems and its implications for the evolution of gene expression, Mol Syst Biol, 4, 170, 10.1038/msb.2008.11 · DOI
  8. N Puzović, 2023, Being noisy in a crowd: differential selective pressure on gene expression noise in model gene regulatory networks, PLoS Comput Biol, 19, 10.1371/journal.pcbi.1010982 · DOI
  9. L Wolf, 2015, Expression noise facilitates the evolution of gene regulation, Elife, 4, 10.7554/eLife.05856 · DOI
  10. H Einarsson, 2022, Promoter sequence and architecture determine expression variability and confer robustness to genetic variants, Elife, 11, 10.7554/eLife.80943 · DOI
  11. CKY Hong, 2024, Effect of genomic and cellular environments on gene expression noise, Genome Biol, 25, 137, 10.1186/s13059-024-03277-9 · DOI
  12. K Loell, 2022, Activation domains can decouple the mean and noise of gene expression, Cell Rep, 40, 111118, 10.1016/j.celrep.2022.111118 · DOI
  13. EM Ozbudak, 2002, Regulation of noise in the expression of a single gene, Nat Genet, 31, 69, 10.1038/ng869 · DOI
  14. J Paulsson, 2005, Models of stochastic gene expression, Phys. Life Rev, 2, 157, 10.1016/j.plrev.2005.03.003 · DOI
  15. Y Dublanche, 2006, Noise in transcription negative feedback loops: simulation and experimental analysis, Mol Syst Biol, 2, 41, 10.1038/msb4100081 · DOI
  16. A Singh, 2011, Negative feedback through mRNA provides the best control of gene-expression noise, IEEE Trans Nanobioscience, 10, 194, 10.1109/TNB.2011.2168826 · DOI
  17. M Voliotis, 2012, The magnitude and colour of noise in genetic negative feedback systems, Nucleic Acids Res
  18. U Alon, 2007, Network motifs: theory and experimental approaches, Nat Rev Genet, 8, 450, 10.1038/nrg2102 · DOI · Paper page
  19. A Singh, 2009, Optimal feedback strength for noise suppression in autoregulatory gene networks, Biophys J, 96, 4013, 10.1016/j.bpj.2009.02.064 · DOI
  20. SK Aoki, 2019, A universal biomolecular integral feedback controller for robust perfect adaptation, Nature, 570, 533, 10.1038/s41586-019-1321-1 · DOI
  21. P Bokes, 2025, Optimisation of gene expression noise for cellular persistence against lethal events, J Theor Biol, 598, 111996, 10.1016/j.jtbi.2024.111996 · DOI
  22. A Burger, 2012, Influence of decoys on the noise and dynamics of gene expression, Phys Rev E Stat Nonlin Soft Matter Phys, 86, 041920, 10.1103/PhysRevE.86.041920 · DOI
  23. S Dey, 2020, Enhancement of gene expression noise from transcription factor binding to genomic decoy sites, Scientific Reports, 10, 9126, 10.1038/s41598-020-65750-2 · DOI
  24. T-H Lee, 2012, A regulatory role for repeated decoy transcription factor binding sites in target gene expression, Mol Syst Biol, 8, 576, 10.1038/msb.2012.7 · DOI
  25. A Guillemin, 2021, Noise and the molecular processes underlying cell fate decision-making, Phys Biol, 18, 011002, 10.1088/1478-3975/abc9d1 · DOI
  26. A Damour, 2023, Transcriptional stochasticity as a key aspect of HIV-1 latency, Viruses, 15, 1969, 10.3390/v15091969 · DOI
  27. G Balázsi, 2011, Cellular decision making and biological noise: from microbes to mammals, Cell, 144, 910, 10.1016/j.cell.2011.01.030 · DOI
  28. A Singh, 2009, Stochastic gene expression as a molecular switch for viral latency, Curr Opin Microbiol, 12, 460, 10.1016/j.mib.2009.06.016 · DOI
  29. H Maamar, 2007, Noise in gene expression determines cell fate in Bacillus subtilis, Science, 317, 526, 10.1126/science.1140818 · DOI · Paper page
  30. P Piho, 2024, Feedback between stochastic gene networks and population dynamics enables cellular decision-making, Sci Adv, 10, 10.1126/sciadv.adl4895 · DOI
  31. EA Urban, 2023, Activating and repressing gene expression between chromosomes during stochastic fate specification, Cell Rep, 42, 111910, 10.1016/j.celrep.2022.111910 · DOI
  32. HR Clark, 2021, Epigenetically regulated digital signaling defines epithelial innate immunity at the tissue level, Nat Commun, 12, 1836, 10.1038/s41467-021-22070-x · DOI
  33. S Tang, 2024, Bet hedging in a unicellular microalga, Nat Commun, 15, 2063, 10.1038/s41467-024-46297-6 · DOI
  34. JR Bernhardt, 2020, Life in fluctuating environments, Philos Trans R Soc Lond B Biol Sci, 375, 20190454, 10.1098/rstb.2019.0454 · DOI
  35. A Eldar, 2010, Functional roles for noise in genetic circuits, Nature, 467, 167, 10.1038/nature09326 · DOI
  36. KS Farquhar, 2019, Role of network-mediated stochasticity in mammalian drug resistance, Nat Commun, 10, 2766, 10.1038/s41467-019-10330-w · DOI
  37. DA Charlebois, 2011, Gene expression noise facilitates adaptation and drug resistance independently of mutation, Phys Rev Lett, 107, 218101, 10.1103/PhysRevLett.107.218101 · DOI
  38. NA Rossi, 2019, Forecasting cell fate during antibiotic exposure using stochastic gene expression, Commun Biol, 2, 259, 10.1038/s42003-019-0509-0 · DOI
  39. T Hossain, 2023, Escherichia coli cells are primed for survival before lethal antibiotic stress, Microbiol Spectr, 11, 10.1128/spectrum.01219-23 · DOI
  40. E Rotem, 2010, Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence, Proc Natl Acad Sci
  41. I El Meouche, 2016, Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells, Sci Rep, 6, 19538, 10.1038/srep19538 · DOI · Paper page
  42. M Sanchez-Romero, 2014, Contribution of phenotypic heterogeneity to adaptive antibiotic resistance, Proc Natl Acad Sci, 111, 355, 10.1073/pnas.1316084111 · DOI
  43. AJ Lopatkin, 2019, Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate, Nat Microbiol, 4, 2109, 10.1038/s41564-019-0536-0 · DOI
  44. C Chang, 2021, Ontogeny and vulnerabilities of drug-tolerant persisters in HER2 breast cancer, Cancer Discov
  45. GV Echeverria, 2019, Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state, Sci Transl Med, 11, 10.1126/scitranslmed.aav0936 · DOI
  46. EB Gunnarsson, 2020, Understanding the role of phenotypic switching in cancer drug resistance, J Theor Biol, 490, 110162, 10.1016/j.jtbi.2020.110162 · DOI
  47. JF Hastings, 2023, Memory of stochastic single-cell apoptotic signaling promotes chemoresistance in neuroblastoma, Sci Adv, 9, 10.1126/sciadv.abp8314 · DOI
  48. L Schuh, 2020, Gene networks with transcriptional bursting recapitulate rare transient coordinated high expression states in cancer, Cell Syst, 10
  49. A Singh, 2023, Probing transient memory of cellular states using single-cell lineages, Front Microbiol, 13, 1050516, 10.3389/fmicb.2022.1050516 · DOI
  50. P Thomas, 2019, Intrinsic and extrinsic noise of gene expression in lineage trees, Sci Rep, 9, 474, 10.1038/s41598-018-35927-x · DOI
  51. P-Y Ho, 2018, Modeling cell size regulation: from single-cell-level statistics to molecular mechanisms and population-level effects, Annu Rev Biophys, 47, 251, 10.1146/annurev-biophys-070317-032955 · DOI
  52. N Totis, 2021, A population-based approach to study the effects of growth and division rates on the dynamics of cell size statistics, IEEE Control Syst Lett, 5, 725, 10.1109/LCSYS.2020.3005069 · DOI
  53. JR Melendez-Alvarez, 2022, Emergence of qualitative states in synthetic circuits driven by ultrasensitive growth feedback, PLoS Comput Biol, 18, 10.1371/journal.pcbi.1010518 · DOI
  54. C Jia, 2017, Stochastic fluctuations can reveal the feedback signs of gene regulatory networks at the single-molecule level, Sci Rep, 7, 16037, 10.1038/s41598-017-15464-9 · DOI
  55. O Patange, 2018, Escherichia coli can survive stress by noisy growth modulation, Nat Commun, 9, 5333, 10.1038/s41467-018-07702-z · DOI
  56. C Tan, 2009, Emergent bistability by a growth-modulating positive feedback circuit, Nat Chem Biol, 5, 842, 10.1038/nchembio.218 · DOI
  57. Rezaee S, Nieto C, Singh A. Controlling biomolecular timekeeping via regulated gene product degradation. In: 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE. 2024. p. 7772–7. · DOI
  58. D Molenaar, 2009, Shifts in growth strategies reflect tradeoffs in cellular economics, Mol Syst Biol, 5, 323, 10.1038/msb.2009.82 · DOI · Paper page
  59. DH Keating, 1995, The unmodified (apo) form of Escherichia coli acyl carrier protein is a potent inhibitor of cell growth, J Biol Chem, 270, 22229, 10.1074/jbc.270.38.22229 · DOI
  60. Zabaikina I, Zhang Z, Nieto C, Bokes P, Singh A. Amplification of noisy gene expression by protein burden: an analytical approach. In: 2023 American Control Conference. IEEE. 2023. 2861–6. · DOI
  61. G Harmange, 2023, Disrupting cellular memory to overcome drug resistance, Nat Commun, 14, 7130, 10.1038/s41467-023-41811-8 · DOI
  62. KMT Rahman, 2025, Rethinking dormancy: antibiotic persisters are metabolically active, non-growing cells, Int J Antimicrob Agents, 65, 107386, 10.1016/j.ijantimicag.2024.107386 · DOI
  63. J Feng, 2014, Growth feedback as a basis for persister bistability, Proc Natl Acad Sci U S A, 111, 544, 10.1073/pnas.1320396110 · DOI
  64. D Huh, 2011, Random partitioning of molecules at cell division, Proc Natl Acad Sci U S A, 108, 15004, 10.1073/pnas.1013171108 · DOI
  65. P Thomas, 2017, Making sense of snapshot data: ergodic principle for clonal cell populations, J R Soc Interface, 14, 20170467, 10.1098/rsif.2017.0467 · DOI
  66. N Friedman, 2006, Linking stochastic dynamics to population distribution: an analytical framework of gene expression, Phys Rev Lett, 97, 168302, 10.1103/PhysRevLett.97.168302 · DOI · Paper page
  67. V Shahrezaei, 2008, Analytical distributions for stochastic gene expression, Proc Natl Acad Sci U S A, 105, 17256, 10.1073/pnas.0803850105 · DOI · Paper page
  68. LC Fraser, 2021, Reduction in gene expression noise by targeted increase in accessibility at gene loci. Proc Natl Acad Sci, 118
  69. A Singh, 2010, Transcriptional bursting from the HIV-1 promoter is a significant source of stochastic noise in HIV-1 gene expression, Biophys J, 98, 10.1016/j.bpj.2010.03.001 · DOI
  70. S Chong, 2014, Mechanism of transcriptional bursting in bacteria, Cell, 158, 314, 10.1016/j.cell.2014.05.038 · DOI · Paper page
  71. A Raj, 2006, Stochastic mRNA synthesis in mammalian cells, PLoS Biol, 4
  72. DM Suter, 2011, Mammalian genes are transcribed with widely different bursting kinetics, Science, 332, 472, 10.1126/science.1198817 · DOI · Paper page
  73. T Fukaya, 2016, Enhancer control of transcriptional bursting, Cell, 166, 358, 10.1016/j.cell.2016.05.025 · DOI · Paper page
  74. T Jia, 2011, Intrinsic noise in stochastic models of gene expression with molecular memory and bursting, Phys Rev Lett, 106, 058102, 10.1103/PhysRevLett.106.058102 · DOI
  75. MA Moran, 2013, Sizing up metatranscriptomics, ISME J, 7, 237, 10.1038/ismej.2012.94 · DOI
  76. O Padovan-Merhar, 2015, Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms, Mol Cell, 58, 339, 10.1016/j.molcel.2015.03.005 · DOI
  77. DE Weidemann, 2023, The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian, Sci Adv, 9, 10.1126/sciadv.adh5138 · DOI
  78. C Basier, 2023, The cell cycle and cell size influence the rates of global cellular translation and transcription in fission yeast, EMBO J, 42, 10.15252/embj.2022113333 · DOI
  79. X-M Sun, 2020, Size-dependent increase in RNA polymerase II initiation rates mediates gene expression scaling with cell size, Curr Biol, 30
  80. H Kempe, 2015, The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise, Mol Biol Cell, 26, 797, 10.1091/mbc.E14-08-1296 · DOI
  81. CA Vargas-Garcia, 2018, Cell size control and gene expression homeostasis in single-cells, Curr Opin Syst Biol, 8, 109, 10.1016/j.coisb.2018.01.002 · DOI
  82. K-L Claude, 2021, Transcription coordinates histone amounts and genome content, Nat Commun, 12, 4202, 10.1038/s41467-021-24451-8 · DOI
  83. C Jia, 2022, Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis, PLoS Comput Biol, 18
  84. S Berry, 2022, Mechanisms of cellular mRNA transcript homeostasis, Trends Cell Biol, 32, 655, 10.1016/j.tcb.2022.05.003 · DOI
  85. Bokes P, Singh A. Controlling noisy expression through auto regulation of burst frequency and protein stability. In: Hybrid Systems Biology: 6th International Workshop HSB 2019 Prague Czech Republic April 6-7 2019 Revised Selected Papers 6. Springer. 2019. p. 80–97. · DOI
  86. P Bokes, 2017, Gene expression noise is affected differentially by feedback in burst frequency and burst size, J Math Biol, 74, 1483, 10.1007/s00285-016-1059-4 · DOI
  87. HD Summers, 2015, Poisson-event-based analysis of cell proliferation, Cytometry A, 87, 385, 10.1002/cyto.a.22620 · DOI
  88. D Ramkrishna, 2000, Population balances: theory and applications to particulate systems in engineering
  89. J Lloyd-Price, 2014, Dynamics of small genetic circuits subject to stochastic partitioning in cell division, J Theor Biol, 356, 11, 10.1016/j.jtbi.2014.04.018 · DOI
  90. I Golding, 2005, Real-time kinetics of gene activity in individual bacteria, Cell, 123, 1025, 10.1016/j.cell.2005.09.031 · DOI · Paper page
  91. T Bergmiller, 2017, Biased partitioning of the multidrug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity, Science, 356, 311, 10.1126/science.aaf4762 · DOI
  92. D Huh, 2011, Non-genetic heterogeneity from stochastic partitioning at cell division, Nat Genet, 43, 95, 10.1038/ng.729 · DOI
  93. A Schwabe, 2014, Contributions of cell growth and biochemical reactions to nongenetic variability of cells, Biophys J, 107, 301, 10.1016/j.bpj.2014.05.004 · DOI
  94. TR Maarleveld, 2013, StochPy: a comprehensive, user-friendly tool for simulating stochastic biological processes, PLoS One, 8, 10.1371/journal.pone.0079345 · DOI
  95. P Thomas, 2021, Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations, J R Soc Interface, 18, 20210274, 10.1098/rsif.2021.0274 · DOI
  96. Z Zhang, 2023, Comparing negative feedback mechanisms in gene expression: from single cells to cell populations. In: 2023 62nd IEEE Conference on Decision and Control (CDC), 3744
  97. Nieto C, Zhang Z. Simulator for the article “Stochastic gene expression in proliferating cells: differing noise intensity in single-cell and population perspectives”. doi: 10.5281/zenodo.10884052 2024. · DOI · Paper page
  98. M Ginovart, 2002, INDISIM, an individual-based discrete simulation model to study bacterial cultures, J Theor Biol, 214, 305, 10.1006/jtbi.2001.2466 · DOI · Paper page
  99. SS Su, 1996, A nitrogen starvation-induced dormant G0 state in fission yeast: the establishment from uncommitted G1 state and its delay for return to proliferation, J Cell Sci, 109, 1347, 10.1242/jcs.109.6.1347 · DOI
  100. M Soltani, 2016, Effects of cell-cycle-dependent expression on random fluctuations in protein levels, R Soc Open Sci, 3, 160578, 10.1098/rsos.160578 · DOI · Paper page
  101. S Luo, 2023, Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics, Nucleic Acids Res, 51, 68, 10.1093/nar/gkac1204 · DOI
  102. U Alon, 2011, An introduction to systems biology: design principles of biological circuits
  103. J Lin, 2018, Homeostasis of protein and mRNA concentrations in growing cells, Nat Commun, 9, 4496, 10.1038/s41467-018-06714-z · DOI
  104. M Soltani, 2016, Intercellular variability in protein levels from stochastic expression and noisy cell cycle processes, PLoS Comput Biol, 12, 10.1371/journal.pcbi.1004972 · DOI
  105. C Jia, 2022, Coupling gene expression dynamics to cell size dynamics and cell cycle events: exact and approximate solutions of the extended telegraph model, iScience, 26, 105746, 10.1016/j.isci.2022.105746 · DOI
  106. RA Reyes Hueros, 2024, Non-genetic differences underlie variability in proliferation among esophageal epithelial clones, PLoS Comput Biol, 20, 10.1371/journal.pcbi.1012360 · DOI
  107. Nieto C, Vargas-Garcia C, Pedraza JM, Singh A. Cell size control shapes fluctuations in colony population. In: 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE. 2022. p. 3219–24. doi: 10.1109/cdc51059.2022.9992413 · DOI
  108. CA Vargas-Garcia, 2016, Conditions for cell size homeostasis: a stochastic hybrid system approach, IEEE Life Sci Lett, 2, 47, 10.1109/LLS.2016.2646383 · DOI
  109. C Nieto, 2024, A moments-based analytical approach for cell size homeostasis, IEEE Control Syst Lett
  110. M Osella, 2014, Concerted control of Escherichia coli cell division, Proc Natl Acad Sci U S A, 111, 3431, 10.1073/pnas.1313715111 · DOI
  111. S Taheri-Araghi, 2015, Cell-size control and homeostasis in bacteria, Curr Biol, 25, 385, 10.1016/j.cub.2014.12.009 · DOI
  112. I Soifer, 2016, Single-cell analysis of growth in budding yeast and bacteria reveals a common size regulation strategy, Curr Biol, 26, 356, 10.1016/j.cub.2015.11.067 · DOI
  113. Y Tanouchi, 2015, A noisy linear map underlies oscillations in cell size and gene expression in bacteria, Nature, 523, 357, 10.1038/nature14562 · DOI
  114. M Campos, 2014, A constant size extension drives bacterial cell size homeostasis, Cell, 159, 1433, 10.1016/j.cell.2014.11.022 · DOI
  115. C Nieto, 2024, Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder, NPJ Syst Biol Appl, 10, 61, 10.1038/s41540-024-00383-z · DOI
  116. S Mukherji, 2014, Mechanisms of organelle biogenesis govern stochastic fluctuations in organelle abundance, Elife, 3, 10.7554/eLife.02678 · DOI
  117. IG Johnston, 2012, Mitochondrial variability as a source of extrinsic cellular noise, PLoS Comput Biol, 8, 10.1371/journal.pcbi.1002416 · DOI
  118. C Nieto-Acuña, 2020, Correlation between protein concentration and bacterial cell size can reveal mechanisms of gene expression, Phys Biol, 17, 045002, 10.1088/1478-3975/ab891c · DOI
  119. V Shahrezaei, 2008, Colored extrinsic fluctuations and stochastic gene expression, Mol Syst Biol, 4, 196, 10.1038/msb.2008.31 · DOI
  120. A Singh, 2013, Quantifying intrinsic and extrinsic variability in stochastic gene expression models, PLoS One, 8, 10.1371/journal.pone.0084301 · DOI
  121. MB Elowitz, 2002, Stochastic gene expression in a single cell, Science, 297, 1183, 10.1126/science.1070919 · DOI · Paper page
  122. M Biondo, 2023, Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise, Phys Biol, 20, 10.1088/1478-3975/acea4e · DOI
  123. C Basier, 2024, TOR regulates variability of protein synthesis rates, EMBO J, 43, 1618, 10.1038/s44318-024-00075-8 · DOI
  124. A Moraes, 2014, Hybrid Chernoff Tau-Leap, Multiscale Model Simul, 12, 581, 10.1137/130925657 · DOI
  125. N Masuda, 2018, A Gillespie algorithm for non-Markovian stochastic processes, SIAM Rev, 60, 95, 10.1137/16M1055876 · DOI

Metadata

Authors
Zhang, Z., Zabaikina, I., Nieto, C., Vahdat, Z., Bokes, P., & Singh, A.
Affiliations
  • University of Delaware (US)
  • Comenius University Bratislava (SK)
  • Baylor College of Medicine (US)
Corresponding author
Singh, Abhyudai
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2025-06-10
Volume
21
Issue
6
Pages
e1013014
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1013014
PMC
PMC12151482
OpenAlex
W4411171556
Cited by
11
References
7
Citations by year
2026: 5 · 2025: 4
FWCI
2.67
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Agentúra na Podporu Výskumu a Vývoja (APVV-23-0039)
  • Vedecká Grantová Agentúra MŠVVaŠ SR a SAV (1/0755/22)
  • National Institute of General Medical Sciences (R35GM148351)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
  • Physical Sciences › Mathematics › Mathematical Physics › Stochastic processes and statistical mechanics
Keywords
stochastic gene expression, single-cell gene expression, positive feedback, protein dilution, cell cycle, cell proliferation

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