A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene
Carregando...
Citações na Scopus
4
Tipo de produção
article
Data de publicação
2020
Título da Revista
ISSN da Revista
Título do Volume
Editora
AMER INST MATHEMATICAL SCIENCES-AIMS
Autores
Citação
MATHEMATICAL BIOSCIENCES AND ENGINEERING, v.17, n.5, p.5477-5503, 2020
Resumo
This manuscript presents a comparison of noise properties exhibited by two stochastic binary models for: (i) a self-repressing gene; (ii) a repressed or activated externally regulating one. The stochastic models describe the dynamics of probability distributions governing two random variables, namely, protein numbers and the gene state as ON or OFF. In a previous work, we quantify noise in protein numbers by means of its Fano factor and write this quantity as a function of the covariance between the two random variables. Then we show that distributions governing the number of gene products can be super-Fano, Fano or sub-Fano if the covariance is, respectively, positive, null or negative. The latter condition is exclusive for the self-repressing gene and our analysis shows the conditions for which the Fano factor is a sufficient classifier of fluctuations in gene expression. In this work, we present the conditions for which the noise on the number of gene products generated from a self-repressing gene or an externally regulating one are quantitatively similar. That is important for inference of gene regulation from noise in gene expression quantitative data. Our results contribute to a classification of noise function in biological systems by theoretically demonstrating the mechanisms underpinning the higher precision in expression of a self-repressing gene in comparison with an externally regulated one.
Palavras-chave
noise in gene regulatory models, stochastic gene regulation, binary gene model, fluctuations on gene expression
Referências
- Abramowitz M., 1972, HDB MATH FUNCTIONS F, V55
- Anastas JN, 2013, NAT REV CANCER, V13, P11, DOI 10.1038/nrc3419
- Andersson R, 2020, NAT REV GENET, V21, P71, DOI 10.1038/s41576-019-0173-8
- Ansel J, 2008, PLOS GENET, V4, DOI 10.1371/journal.pgen.1000049
- Arias AM, 2006, NAT REV GENET, V7, P34, DOI 10.1038/nrg1750
- Bakk A, 2004, BIOPHYS J, V86, P58, DOI 10.1016/S0006-3495(04)74083-7
- Balazsi G, 2011, CELL, V144, P910, DOI 10.1016/j.cell.2011.01.030
- Becskei A, 2000, NATURE, V405, P590, DOI 10.1038/35014651
- Blake WJ, 2003, NATURE, V422, P633, DOI 10.1038/nature01546
- Brock A, 2015, NAT REV CANCER, V15, P499, DOI 10.1038/nrc3959
- Cao ZX, 2020, P NATL ACAD SCI USA, V117, P4682, DOI 10.1073/pnas.1910888117
- Cao ZX, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-05822-0
- Chalancon G, 2012, TRENDS GENET, V28, P221, DOI 10.1016/j.tig.2012.01.006
- Cheb-Terrab E. S., PREPRINT
- Chetverina D, 2017, BIOESSAYS, V39, DOI 10.1002/bies.201600233
- Choubey S, 2015, PLOS COMPUT BIOL, V11, DOI 10.1371/journal.pcbi.1004345
- Cooper G. M., 2000, CELL MOL APPROACH
- Crudu A, 2009, BMC SYST BIOL, V3, DOI 10.1186/1752-0509-3-89
- DELBRUCK M, 1940, J CHEM PHYS, V8, P120, DOI 10.1063/1.1750549
- Elowitz MB, 2002, SCIENCE, V297, P1183, DOI 10.1126/science.1070919
- Fabian MR, 2010, ANNU REV BIOCHEM, V79, P351, DOI 10.1146/annurev-biochem-060308-103103
- Farquhar KS, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-10330-w
- Fiziev P. P., PREPRINT
- Fournier T, 2007, BIOINFORMATICS, V23, P3185, DOI 10.1093/bioinformatics/btm490
- Fulco CP, 2019, NAT GENET, V51, P1664, DOI 10.1038/s41588-019-0538-0
- Gama LR, 2020, ENTROPY-SWITZ, V22, DOI 10.3390/e22040479
- Grima R, 2012, J CHEM PHYS, V137, DOI 10.1063/1.4736721
- Gronlund A, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms2867
- Hallikas O, 2006, CELL, V124, P47, DOI 10.1016/j.cell.2005.10.042
- HAWLEY DK, 1982, J MOL BIOL, V157, P493, DOI 10.1016/0022-2836(82)90473-9
- Heemskerk I, 2019, ELIFE, V8, DOI 10.7554/eLife.40526
- Holehouse J, 2020, BIOPHYS J, V118, P1517, DOI 10.1016/j.bpj.2020.02.016
- Hooshangi S, 2006, CHAOS, V16, DOI 10.1063/1.2208927
- Hornos JEM, 2005, PHYS REV E, V72, DOI 10.1103/PhysRevE.72.051907
- Innocentini GCP, 2007, J MATH BIOL, V55, P413, DOI 10.1007/s00285-007-0090-x
- Innocentini GCP, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4905217
- Iyer-Biswas S, 2009, PHYS REV E, V79, DOI 10.1103/PhysRevE.79.031911
- Jia C, 2020, J CHEM PHYS, V152, DOI 10.1063/1.5144578
- Kim AR, 2013, PLOS GENET, V9, DOI 10.1371/journal.pgen.1003243
- Kumar N, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.268105
- Kuwahara H, 2015, INTEGR BIOL-UK, V7, P1622, DOI 10.1039/c5ib00107b
- Lepzelter D, 2010, CHEM PHYS LETT, V490, P216, DOI 10.1016/j.cplett.2010.03.029
- Marciano DC, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.258104
- Mirabelli CK, 2019, SCI SIGNAL, V12, DOI 10.1126/scisignal.aay4494
- Mozziconacci J, 2020, J MOL BIOL, V432, P712, DOI 10.1016/j.jmb.2019.10.017
- Munsky B, 2012, SCIENCE, V336, P183, DOI 10.1126/science.1216379
- Nevozhay D, 2009, P NATL ACAD SCI USA, V106, P5123, DOI 10.1073/pnas.0809901106
- Pare A, 2009, CURR BIOL, V19, P2037, DOI 10.1016/j.cub.2009.10.028
- Park K., PREPRINT
- PECCOUD J, 1995, THEOR POPUL BIOL, V48, P222, DOI 10.1006/tpbi.1995.1027
- Pedraza JM, 2005, SCIENCE, V307, P1965, DOI 10.1126/science.1109090
- Prata GN, 2016, PHYS REV E, V93, DOI 10.1103/PhysRevE.93.022403
- Raj A, 2006, PLOS BIOL, V4, P1707, DOI 10.1371/journal.pbio.0040309
- Ramos AF, 2011, PHYS REV E, V83, DOI 10.1103/PhysRevE.83.062902
- Ramos AF, 2010, IET SYST BIOL, V4, P311, DOI 10.1049/iet-syb.2010.0058
- Ramos A. F., 2020, PROGNOSTIC THERAPEUT, P257
- Ramos AF, 2019, J CHEM PHYS, V151, DOI 10.1063/1.5105361
- Ramos AF, 2015, PHYS REV E, V91, DOI 10.1103/PhysRevE.91.020701
- Ramos AF, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.108103
- Raser JM, 2005, SCIENCE, V309, P2010, DOI 10.1126/science.1105891
- Reeves GT, 2019, J BIOL ENG, V13, DOI 10.1186/s13036-019-0190-3
- Rosenfeld N, 2002, J MOL BIOL, V323, P785, DOI 10.1016/S0022-2836(02)00994-4
- Rosenfeld N, 2006, BIOPHYS J, V91, P759, DOI 10.1529/biophysj.105.073098
- Sancar A, 2010, FEBS LETT, V584, P2618, DOI 10.1016/j.febslet.2010.03.017
- Sanchez A, 2013, SCIENCE, V342, P1188, DOI 10.1126/science.1242975
- SAVAGEAU MA, 1974, NATURE, V252, P546, DOI 10.1038/252546a0
- Sepulveda LA, 2016, SCIENCE, V351, P1218, DOI 10.1126/science.aad0635
- Shahrezaei V, 2008, P NATL ACAD SCI USA, V105, P17256, DOI 10.1073/pnas.0803850105
- Shen-Orr SS, 2002, NAT GENET, V31, P64, DOI 10.1038/ng881
- Sneppen K, 2017, REP PROG PHYS, V80, DOI 10.1088/1361-6633/aa5aeb
- Subramanian A, 2005, P NATL ACAD SCI USA, V102, P15545, DOI 10.1073/pnas.0506580102
- Suter DM, 2011, SCIENCE, V332, P472, DOI 10.1126/science.1198817
- Thattai M, 2001, P NATL ACAD SCI USA, V98, P8614, DOI 10.1073/pnas.151588598
- Tripathi T, 2008, PHYS REV E, V77, DOI 10.1103/PhysRevE.77.011921
- Tsimring LS, 2014, REP PROG PHYS, V77, DOI 10.1088/0034-4885/77/2/026601
- Xu H, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.128101
- Yvinec R., PREPRINT