Mathematical approaches in studying bicoid gene

Zara Ghodsi , Hossein Hassani , Kevin McGhee

Quant. Biol. ›› 2015, Vol. 3 ›› Issue (4) : 182 -192.

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Quant. Biol. ›› 2015, Vol. 3 ›› Issue (4) : 182 -192. DOI: 10.1007/s40484-015-0058-6
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Mathematical approaches in studying bicoid gene

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Abstract

It is widely believed that in Drosophila melanogaster the pattern of Bicoid protein gradient plays a crucial role in the segmentation stage of embryo development. As a result of its fundamental role, modelling the Bicoid gradient has become increasingly popular for researchers from many different areas of study. The aim of this paper is to bring together the most prominent studies on this maternal gene and discuss how existing techniques for modelling this gradient have evolved over the years.

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bicoid / Drosophila melanogaster / model / segmentation gene

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Zara Ghodsi, Hossein Hassani, Kevin McGhee. Mathematical approaches in studying bicoid gene. Quant. Biol., 2015, 3(4): 182-192 DOI:10.1007/s40484-015-0058-6

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References

[1]

Driesch,H. (1908). The science and philosophy of the organism. Aberdeen: Aberdeen University

[2]

Driever,W. and Nusslein-Volhard, C. (1988) The bicoid protein determines position in the Drosophila embryo in a concentration-dependent manner. Cell, 54, 95104

[3]

Frohnhöfer,H. G. and Nüsslein-Volhard,C. (1986) Organization of anterior pattern in the Drosophila embryo by the maternal gene bicoid. Nature, 324, 120–125

[4]

Berleth,T., Burri, M., Thoma,G., Bopp,D., Richstein, S., Frigerio,G., Noll,M., Nüsslein-Volhard, C. (1988) The role of localization of bicoid RNA in organizing the anterior pattern of the Drosophila embryo. EMBO J., 7, 1749

[5]

Embryos receiving different doses of Bcd have differently sized anterior structures.

[6]

Grimm,O., Coppey, M. and Wieschaus,E. (2010) Modelling the Bicoid gradient. Development, 137, 2253–2264

[7]

Grimm,O. and Wieschaus, E. (2010) The Bicoid gradient is shaped independently of nuclei. Development, 137, 2857–2862

[8]

Houchmandzadeh,B., Wieschaus, E. and Leibler,S. (2002) Establishment of developmental precision and proportions in the early Drosophila embryo. Nature, 415, 798–802

[9]

Gregor,T., Bialek, W., de Ruyter van Steveninck, R. R. and Tank,D. W.,Wieschaus. E.F.(2005). Diffusion and scaling during early embryonic pattern formation. Proc. Natl. Acad. Sci. USA., 102, 18402–18407

[10]

Wartlick,O., Kicheva, A. and Gonzlez-Gaitn,M. (2009) Morphogen gradient formation. Cold Spring Harb. Perspect. Biol., 1, a001255

[11]

Liu,W. (2013). Machine learning approaches to modelling bicoid morphogen in Drosophila melanogaster. Ph. D. Thesis, University of Southampton.

[12]

Morgan,T. H. (1901). Regeneration. New York: The Macmillan Company

[13]

Spemann,H. and Mangold, H. (1924) Introduction of embryonic primordia by implantation of organizers from a different species. Rouxs Arch. Entw. Mech, 100, 599638

[14]

Child,C. M. (1941). Patterns and Problems of Development. Chicago: The university of chicago press

[15]

Turing,A. M. (1952) The chemical basis of morphogenesis. Philos. Trans. R. Soc. Lond. B Biol. Sci., 237, 37–72

[16]

Wolpert,L. (1969) Positional information and the spatial pattern of cellular differentiation. J. Theor. Biol., 25, 1–47

[17]

Crick,F. (1970) Diffusion in embryogenesis. Nature, 225, 420–422

[18]

Bergmann,S., Sandler, O., Sberro,H., Shnider,S., Schejter, E., Shilo,B. Z. and Barkai,N. (2007) Pre-steady-state decoding of the Bicoid morphogen gradient. PLoS Biol., 5, e46

[19]

Gregor,T., Tank, D. W., Wieschaus,E. F. and Bialek,W. (2007) Stability and nuclear dynamics of the bicoid morphogen gradient. Cell, 130, 153–164

[20]

Coppey,M., Berezhkovskii, A. M., Kim,Y., Boettiger,A.N. and Shvartsman,S. Y. (2007) Modeling the bicoid gradient: diffusion and reversible nuclear trapping of a stable protein. Dev. Biol., 312, 623– 630

[21]

St Johnston,D., Driever, W., Berleth,T., Richstein,S. and Nusslein-Volhard,C. (1989) Multiple steps in the localization of bicoid RNA to the anterior pole of the Drosophila oocyte. Development, 107, 13

[22]

Ephrussi,A. and Johnston, D. S. (2004) Seeing is believing: the bicoid morphogen gradient matures. Cell, 116, 143–152

[23]

Surdej,P. and Jacobs-Lorena, M. (1989) Developmental regulation of bicoid mRNA stability is mediated by the first 43 nucleotides of the 3 untranslated region. Mol. Cell. Biol., 18, 28922900

[24]

Dilão,R. and Muraro, D. (2010) mRNA diffusion explains protein gradients in Drosophila early development. J. Theor. Biol., 264, 847–853

[25]

Little,S. C., Tkačik, G.Kneeland,T. B.Wieschaus,E. F., and GregorT. (2011) The formation of the bicoid morphogen gradient requires protein movement from anteriorly localized mRNA. PLoS Biol., 9, e1000596

[26]

Gregor,T., Tank, D. W., Wieschaus,E. F. and Bialek,W. (2007) Probing the limits to positional information. Cell, 130, 153–164

[27]

Wang,Y., Liu, F. and Wang,W. (2012) Dynamic mechanism for the tran-scription apparatus orchestrating reliable responses to activators. Sci. Rep., 2422,

[28]

Crauk,O. and Dostatni, N. (2005) Bicoid determines sharp and precise target gene expression in the Drosophila embryo. Curr. Biol., 15, 1888–1898

[29]

Lewis,J. (2008) From signals to patterns: space, time, and mathematics in developmental biology. Science, 322, 399–403

[30]

Gibson,M. A. and Bruck,J. (2009) Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem., 104, 18761889

[31]

Andrews,S. S. and Bray,D. (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys. Biol.,1, 137–151

[32]

Hattne,J., Fange, D. and Elf,J. (2005) Stochastic reaction-diffusion simulation with MesoRD. Bioinformatics, 21, 2923–2924

[33]

Erban,R. and Chapman, S. J. (2009) Stochastic modelling of reaction diffusion processes: Algorithms for bimolecular reactions. Phys. Biol., 6, 046001

[34]

Wu,Y. F., Myasnikova, E. and Reinitz,J. (2007) Master equation simulation analysis of immune stained Bicoid morphogen gradient. BMC Syst. Biol., 1, 52

[35]

Hattne,J., Fange, D. and Elf,D. (2005) Stochastic reaction-diffusion simulation with MesoRD. Bioinformatics, 21, 2923–2924

[36]

Dewar,M. A., Kadirkamanathan, V., Opper,M. and Sanguinetti,G. (2010) Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster BMC Syst. Biol., 4, 21

[37]

Okabe-Oho,Y., Murakami, H., Oho,S. and Sasai,M. (2009) Stable, precise, and reproducible patterning of bicoid and hunchback molecules in the early Drosophila embryo. PLoS Comput. Biol., 5, e1000486

[38]

Deng,J., Wang, W., Lu,L. J. and Ma,J. (2010) A two-dimensional simulation model of the Bicoid gradient in Drosophila. PLoS One, 5, e10275

[39]

Pisarev,A., Poustelnikova, E., Samsonova,M. and Reinitz,J. (2009) FlyEx, the quantitative atlas on segmentation gene expression at cellular resolution. Nucleic Acids Res., 37, D560–D566

[40]

Myasnikova,E., Samsonova, M., Kosman,D. and R einitz,J. (2005) Removal of background signal from in situ data on the expression of segmentation genes in drosophila. Dev. Genes Evol., 215, 320–326

[41]

Hassani,H. (2007) Singular spectrum analysis: Methodology and comparison. J. Data Sci., 5, 239–257

[42]

Holloway, D. M., Harrison, L. G., Kosman,D., Vanario-Alonso,C. E. and Spirov,A. V. (2006) Analysis of pattern precision shows that Drosophila segmentation develops substantial independence from gradients of maternal gene products. Dev. Dyn., 235, 2949–2960

[43]

Alexandrov,T., Golyandina, N. and Spirov,A. (2008) Singular spectrum analysis of gene expression profiles of early Drosophila embryo: Exponential-in-distance patterns. Res. Lett. Signal Process., 2008, 825758

[44]

Alexandrov,T. (2009) A method for trend extraction using Singular Spectrum Analysis. Rev. Stat., 7, 1–22

[45]

Golyandina, N. E., Holloway, D. M., Lopesc,F. J. P., Spirov,A. V., Spirova, E. N., and Usevich, K. D. (2012). Measuring gene expression noise in early Drosophila embryos: nucleus-to-nucleus variability. Procedia. Comput. Sci.9, 373–382

[46]

Spirov,A. V., Golyandina, N. E., Holloway,D. M., Alexandrov,T., Spirova,E. N., Lope,F. (2012) Measuring gene expression noise in early Drosophila embryos: The highly dynamic Compartmentalized Micro-environment of the blastoderm is one of the main sources of noise. Evol. Comp. Mach. Learning and Data Mining in Bioinformatics, 7246, 177–188

[47]

Holloway,D. M., Lopes, F. J. P., da Fontoura Costa, L., Travençolo,B. A. N., Golyandina,N., Usevich,K. and Spirov,A. V. (2011) Gene expression noise in spatial patterning: hunchback promoter structure affects noise amplitude and distribution in Drosophila segmentation. PLoS Comput. Biol., 7, e1001069

[48]

Ghodsi,Z., Silva, E. S. and Hassani,H. (2015) Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques. Genomics Proteomics Bioinformatics, 13, 183–191

[49]

Holloway,D. M., Lopes, F. J. P., da Fontoura Costa, L., Travençolo,B. A. N., Golyandina,N., Usevich,K. and Spirov,A. V. (2011) Gene expression noise in spatial patterning: hunchback promoter structure affects noise amplitude and distribution in Drosophila segmentation. PLoS Comput. Biol., 7, e1001069

[50]

Golyandina,N. E., Holloway, D. M. and Lopes,F. J. P., Spirov,A. V., Spirova, E. N. and Usevich,K. D. (2012) Measuring gene expression noise in early Drosophila embryos: nucleus-to-nucleus variability. Procedia Comput. Sci., 9, 373–382

[51]

Hassani,H. and Ghodsi, Z. (2014) Pattern recognition of gene expression with singular spectrum analysis. Med. Sci., 2, 127–139

[52]

Tweedie S., Ashburner M.,FallsK.,LeylandP.,McQuilton P.,MarygoldS.,MillburnG.,Osumi-Sutherland D.,SchroederA.,SealR.,ZhangH., FlyBaseConsortium.(2009). FlyBase: Enhancing Drosophila gene ontology annotations. Nucleic Acids Res., 37, D555–D559

[53]

Gelbart,W., Bayraktaroglu, L., Bettencourt,B. and Campbell,K. (2003) The FlyBase database of the Drosophila genome projects and community literature. Nucleic Acids Res., 31, 172–175

[54]

Weigmann,K., Klapper, R., Strasser,T., Rickert,C., Technau, G., Jackle,H., Janning,W. and Klambt,C. (2003) FlyMove a new way to look at development of Drosophila. Trends Genet., 19, 310–311

[55]

Poustelnikova,E., Pisarev, A., Blagov,M., Samsonova,M. and Reinitz,J. (2004) A database for management of gene expression data in situ. Bioinformatics, 20, 2212–2221

[56]

BDGP Database.

[57]

The Comprehensive Drosophila Interactions Database.

[58]

FlyTF Database.

[59]

FlyMine Database.

[60]

FlyAtlas Database.

[61]

FlyCircuit Database.

[62]

[63]

Kosman,D., Reinitz, J. and Sharp,D. H. (1998). Automated assay of gene expression at cellular resolution. In Proc. 1998 P SB, 617

[64]

Janssens,H., Kosman, D., Vanario-Alonso,C. E., Jaeger,J., Samsonova, M. and Reinitz,J. (2005) A high-throughput method for quantifying gene expression data from early drosophila embryos. Dev. Genes Evol., 215, 374–381

[65]

Myasnikova,E., Samsonova, A., Kozlov,K., Samsonova,M. and Reinitz,J. (2001) Registration of the expression patterns of Drosophila segmentation genes by two independent methods. Bioinformatics, 17, 3–12

[66]

Kozlov,K., Myasnikova, E., Pisarev,A., Samsonova,M. and Reinitz,J. (2002) A method for two-dimensional registration and constrution of the two-dimensional atlas of gene expression patterns in situ. In Silio Biology, 2, 125–141

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