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Reaction Network Models as a Tool to Study Gene Regulation and Cell Signaling in Development and Diseases

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Networks in Systems Biology

Part of the book series: Computational Biology ((COBO,volume 32))

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Abstract

Advances in molecular biology and experimental techniques have produced a large amount of data with unprecedented accuracy. In addition to computational methods for handling these massive amounts of data, the development of theoretical models aimed at solving specific questions is essential. Many of these questions reside in the description of gene regulation and cell signaling networks which are key aspects during development and diseases. To deal with this problem, we present in this chapter a set of strategies to describe the dynamic behavior of reaction network models. We present some results selected from broad mathematical results, focused on what is closely related to gene regulation and cell signaling. We give special attention to the so-called Chemical Reaction Network Theory (CRNT) that has been developed since the first half of the past century. Some of the most important and practical results of this theory are the analysis of the conditions for a given network to exhibit emergent proprieties like bistability. The description of this behavior has been recurrent in recent studies of developmental biology or cellular signaling networks [1,2,3,4,5]. However, while referring to the same phenomena, the connection between bistability or oscillations in CRNT and biology is not obvious and has been a challenge, despite some well-succeeded examples. Contribution to bringing these two areas of knowledge together is the primary purpose of this chapter.

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References

  1. Rulands S, Klünder B, Frey E (2013) Stability of localized wave fronts in bistable systems. Phys Rev Lett 110:038102

    Article  Google Scholar 

  2. Lopes FJP, Vieira FMC, Holloway DM, Bisch PM, Spirov AV (2008) Spatial bistability generates hunchback expression sharpness in the Drosophila embryo. PLoS Comput Biol 4

    Google Scholar 

  3. Graham TGW, Tabei SMA, Dinner AR, Rebay I (2010) Modeling bistable cell-fate choices in the Drosophila eye: Qualitative and quantitative perspectives. Development 137:2265–2278

    Article  Google Scholar 

  4. Taniguchi K, Karin M (2018) NF-B, inflammation, immunity and cancer: coming of age. Nat Rev Immunol 18:309–324

    Article  Google Scholar 

  5. Hoffmann A, Levchenko A, Scott ML, Baltimore D (2002) The IκB-NF-κB signaling module: temporal control and selective gene activation. Science 298:1241–1245

    Google Scholar 

  6. Araujo H, Fontenele MR, da Fonseca RN (2011) Position matters: variability in the spatial pattern of BMP modulators generates functional diversity. Genesis 49:698–718

    Article  Google Scholar 

  7. Hong JW, Hendrix DA, Papatsenko D, Levine MS (2008) How the Dorsal gradient works: insights from postgenome technologies. Proc Natl Acad Sci USA 105:20072–20076

    Article  Google Scholar 

  8. Brumby AM, Richardson HE (2005) Using Drosophila melanogaster to map human cancer pathways. Nat Rev Cancer 5:626–639

    Article  Google Scholar 

  9. Rudrapatna VA, Cagan RL, Das TK (2012) Drosophila cancer models. Dev Dyn 241:107–118

    Article  Google Scholar 

  10. Pires BRB, Mencalha AL, Ferreira GM, De Souza WF, Morgado-Díaz JA, Maia AM et al (2017) NF-kappaB is involved in the regulation of EMT genes in breast cancer cells. PLoS One 12

    Google Scholar 

  11. Horn F (1973) On a connexion between stability and graphs in chemical kinetics II. Stability and the complex graph. Proc R Soc London. A Math Phys Sci 334:313–30

    Google Scholar 

  12. Horn F, Jackson R (1972) General mass action kinetics. Arch Ration Mech Anal 47:81–116

    Article  MathSciNet  Google Scholar 

  13. Clarke BL (2007) Stability of complex reaction networks. In: Advances in chemical physics. Wiley, pp 1–215

    Google Scholar 

  14. Willamowski KD, Rossler OE (1978) Contributions to the theory of mass action kinetics I. Enumeration of second order mass action kinetics. Zeitschrift fur Naturforsch. Sect A J Phys Sci 33:827–33

    Google Scholar 

  15. Feinberg M (1987) Chemical reaction network structure and the stability of complex isothermal reactors-I. The deficiency zero and deficiency one theorems. Chem Eng Sci 42:2229–68

    Google Scholar 

  16. Ashyraliyev M, Fomekong-Nanfack Y, Kaandorp JA, Blom JG (2009) Systems biology: parameter estimation for biochemical models. FEBS J 276:886–902

    Article  Google Scholar 

  17. Baker SM, Poskar CH, Schreiber F, Junker BH (2015) A unified framework for estimating parameters of kinetic biological models. BMC Bioinf 16

    Google Scholar 

  18. González J, Vujačić I, Wit E (2013) Inferring latent gene regulatory network kinetics. Stat Appl Genet Mol Biol 12:109–127

    Article  MathSciNet  Google Scholar 

  19. Hass H, Loos C, Raimúndez-Álvarez E, Timmer J, Hasenauer J, Kreutz C (2019) Benchmark problems for dynamic modeling of intracellular processes. Bioinformatics 35:3073–3082

    Article  Google Scholar 

  20. Lillacci G, Khammash M (2010) Parameter estimation and model selection in computational biology. PLoS Comput Biol 6:e1000696

    Article  MathSciNet  MATH  Google Scholar 

  21. Rodriguez-Fernandez M, Rehberg M, Kremling A, Banga JR (2013) Simultaneous model discrimination and parameter estimation in dynamic models of cellular systems. BMC Syst Biol 7

    Google Scholar 

  22. Sun J, Garibaldi JM, Hodgman C (2012) Parameter estimation using metaheuristics in systems biology: a comprehensive review. IEEE/ACM Trans Comput Biol Bioinf 9:185–202

    Article  Google Scholar 

  23. Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW (2013) Parameter uncertainty in biochemical models described by ordinary differential equations. Math Biosci 246:305–314

    Article  MathSciNet  MATH  Google Scholar 

  24. Aster RC, Borchers B, Thurber CH (2013) Parameter estimation and inverse problems. Elsevier Inc

    Google Scholar 

  25. Dreossi T, Dang T (2014) Parameter synthesis for polynomial biological models. In: HSCC 2014—proceedings of the 17th international conference on hybrid systems: computation and control (Part of CPS week). Association for computing machinery, page 233–42

    Google Scholar 

  26. Michalewicz Z, Dasgupta D, Le Riche RG, Schoenauer M (1996) Evolutionary algorithms for constrained engineering problems. Comput Ind Eng 30:851–870

    Article  Google Scholar 

  27. Reali F, Priami C, Marchetti L (2017) Optimization algorithms for computational systems biology. Front Appl Math Stat 3

    Google Scholar 

  28. Van Laarhoven PJM, Aarts EHL (1987) Simulated annealing. In: Simulated annealing: theory and applications. Springer Netherlands, Dordrecht, p 7–15

    Google Scholar 

  29. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4:65–85

    Article  Google Scholar 

  30. Onwubolu GC, Davendra D (eds) (2009) Differential evolution: a handbook for global permutation-based combinatorial optimization. Springer, Berlin, Heidelberg

    Google Scholar 

  31. Abdullah A, Deris S, Anwar S, Arjunan SNVV (2013) An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters. PLoS ONE 8:e56310

    Article  Google Scholar 

  32. Abdullah A, Deris S, Mohamad MS, Anwar S (2013) An improved swarm optimization for parameter estimation and biological model selection. PLoS ONE 8:e61258

    Article  Google Scholar 

  33. Stražar M, Mraz M, Zimic N, Moškon M (2014) An adaptive genetic algorithm for parameter estimation of biological oscillator models to achieve target quantitative system response. Nat Comput 13:119–127

    Article  MathSciNet  Google Scholar 

  34. Hussain F, Langmead CJ, Mi Q, Dutta-Moscato J, Vodovotz Y, Jha SK (2015) Automated parameter estimation for biological models using Bayesian statistical model checking. BMC Bioinf 16:S8

    Article  Google Scholar 

  35. Mansouri MM, Nounou HN, Nounou MN, Datta AA (2014) Modeling of nonlinear biological phenomena modeled by S-systems. Math Biosci 249:75–91

    Article  MathSciNet  MATH  Google Scholar 

  36. Rodriguez-Fernandez M, Banga JR, Doyle FJ (2012) Novel global sensitivity analysis methodology accounting for the crucial role of the distribution of input parameters: application to systems biology models. Int J Robust Nonlinear Control 22:1082–1102

    Article  MathSciNet  MATH  Google Scholar 

  37. Rodriguez-Fernandez M, Egea JA, Banga JR (2006) Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. BMC Bioinf 7:483

    Article  Google Scholar 

  38. Sequential monte carlo methods in practice. Springer, New York; 2001

    Google Scholar 

  39. Banga JR (2008) Optimization in computational systems biology. BMC Syst Biol 2:47

    Article  Google Scholar 

  40. Boyd S, Vandenberghe L. (2004) Convex Optimization. Cambridge University Press

    Google Scholar 

  41. Padhye N, Mittal P, Deb K (2015) Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization. Comput Optim Appl 62:851–890

    Article  MathSciNet  MATH  Google Scholar 

  42. Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U (2009) Computational modeling of biochemical networks using COPASI. Methods Mol Biol 500:17–59

    Article  Google Scholar 

  43. De Rainville FM, Fortin FA, Gardner MA, Parizeau M, Gagné C (2012) DEAP: a python framework for evolutionary algorithms. In: GECCO’12—proceedings of the 14th international conference on genetic and evolutionary computation companion. ACM Press, New York, USA, pp 85–92

    Google Scholar 

  44. De Sousa DJ, Arruda Cardoso M, Bisch PM, Pereira Lopes FJ, Nassif Travençolo BA (2013) A segmentation method for nuclei identification from sagittal images of Drosophila melanogaster embryos

    Google Scholar 

  45. Chaves M, Sengupta A, Sontag ED (2009) Geometry and topology of parameter space: investigating measures of robustness in regulatory networks. J Math Biol 59:315–358

    Article  MathSciNet  MATH  Google Scholar 

  46. Shinar G, Feinberg M (2013) Concordant chemical reaction networks and the species-reaction graph. Math Biosci 241:1–23

    Article  MathSciNet  MATH  Google Scholar 

  47. The chemical reaction network toolbox | chemical reaction network theory [Internet]. https://crnt.osu.edu/CRNTWin. Accessed 17 Apr 2020

  48. Gilbert SF, Barresi MJF (2017) Developmental biology, 11th edn 2016. Am J Med Genet Part A 173:1430–1430

    Google Scholar 

  49. Campos-Ortega JA, Hartenstein V (1985) The embryonic development of Drosophila melanogaster. Springer, Berlin Heidelberg

    Book  Google Scholar 

  50. FlyEx, the quantitative atlas on segmentation gene expression at cellular resolution [Internet]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2686593/. Accessed 16 Apr 2020

  51. Gregor T, Tank D, Wieschaus E, Cell WB (2007) Undefined. Probing the limits to positional information. Elsevier

    Google Scholar 

  52. Abu-Arish A, Porcher A, Czerwonka A, Dostatni N, Fradint C (2010) High mobility of bicoid captured by fluorescence correlation spectroscopy: implication for the rapid establishment of its gradient. Biophys J 99:L33–L35

    Article  Google Scholar 

  53. Wolpert L (1989) Positional information revisited. Development 3–12

    Google Scholar 

  54. Margolis JS, Borowsky ML, Steingrímsson E, Shim CW, Lengyel JA, Posakony JW (1995) Posterior stripe expression of hunchback is driven from two promoters by a common enhancer element. Undefined

    Google Scholar 

  55. Schröder C, Tautz D, Seifert E, Jäckle H (1988) Differential regulation of the two transcripts from the Drosophila gap segmentation gene hunchback. EMBO J 7:2881–2887

    Article  Google Scholar 

  56. Holloway DM, Lopes FJP, da Fontoura Costa L, Travencolo BAN, Golyandina N, Usevich K et al (2011) Gene expression noise in spatial patterning: hunchback promoter structure affects Noise amplitude and distribution in Drosophila segmentation. Plos Comput Biol 7:e1001069

    Google Scholar 

  57. Novotny T, Eiselt R, Urban J (2002) Hunchback is required for the specification of the early sublineage of neuroblast 7-3 in the Drosophila central nervous system. Development 129:1027–1036

    Google Scholar 

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

    Article  Google Scholar 

  59. Zadorin AS, Rondelez Y, Gines G, Dilhas V, Urtel G, Zambrano A et al (2017) Synthesis and materialization of a reaction-diffusion French flag pattern. Nat Chem 9:990–996

    Article  Google Scholar 

  60. Lehmann R, Nüsslein-Volhard C (1987) Hunchback, a gene required for segmentation of an anterior and posterior region of the Drosophila embryo. Dev Biol 119:402–417

    Article  Google Scholar 

  61. Papatsenko D, Levine MS (2008) Dual regulation by the Hunchback gradient in the Drosophila embryo. Proc Natl Acad Sci U.S.A 105:2901–2906

    Article  Google Scholar 

  62. Holloway DM, Spirov AV (2015) Mid-embryo patterning and precision in drosophila segmentation: Krüppel dual regulation of hunchback. PLoS One 10

    Google Scholar 

  63. Lopes FJP, Vanario-Alonso CE, Bisch PM (2005) A kinetic mechanism for Drosophila bicoid cooperative binding. J Theor Biol

    Google Scholar 

  64. Lopes FJP, Spirov AV, Bisch PM (2012) The role of bicoid cooperative binding in the patterning of sharp borders in Drosophila melanogaster. Dev Biol

    Google Scholar 

  65. Lebrecht D, Foehr M, Smith E, lopes FJP, Vanario-Alonso CE, Reinitz J et al (2005) Bicoid cooperative DNA binding is critical for embryonic patterning in Drosophila. Proc Natl Acad Sci U.S.A

    Google Scholar 

  66. Burz DSD (2001) Biology SH-J of molecular, 2001 U, Hanes SD. Isolation of mutations that disrupt cooperative DNA binding by the Drosophila Bicoid protein. J Mol Biol 305:219–230

    Google Scholar 

  67. Tran H, Desponds J, Perez Romero CA, Coppey M, Fradin C, Dostatni N et al (2018) Precision in a rush: trade-offs between reproducibility and steepness of the hunchback expression pattern. PLoS Comput Biol 14

    Google Scholar 

  68. Perry MW, Bothma JP, Luu RD, Levine M (2012) Precision of hunchback expression in the Drosophila embryo. Curr Biol 22:2247–2252

    Article  Google Scholar 

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

    Article  Google Scholar 

  70. Ochoa-Espinosa A, Yucel G, Kaplan L, Pare A, Pura N, Oberstein A et al (2005) The role of binding site cluster strength in bicoid-dependent patterning in Drosophila. Proc Natl Acad Sci U.S.A. 102:4960–4965

    Article  Google Scholar 

  71. Struhl G, Struhl K, Macdonald PM (1989) The gradient morphogen bicoid is a concentration-dependent transcriptional activator. Cell 57:1259–1273

    Article  Google Scholar 

  72. Treisman J, Desplan C (1989) The products of the Drosophila gap genes hunchback and Krüppel bind to the hunchback promoters. Nature 341:335–337

    Article  Google Scholar 

  73. Nature WD (1989) Undefined. Determination of zygotic domains of gene expression in the Drosophila embryo by the affinity of binding sites for the bicoid morphogen. ci.nii.ac.jp

    Google Scholar 

  74. Ma X, Yuan D, Diepold K, Scarborough T, Ma J (1996) The Drosophila morphogenetic protein bicoid binds DNA cooperatively. Development 122:1195–1206

    Google Scholar 

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Authors and Affiliations

  1. Universidade Federal do Rio de Janeiro, Campus Duque de Caxias Professor Geraldo Cidade, Grupo de Biologia do Desenvolvimento em Sistemas Dinâmicos, Duque de Caxias, Brazil

    Francisco José Pereira Lopes & Josué Xavier de Carvalho

  2. Laboratório Nacional de Computação Científica, Data Extreme Lab (DEXL), Petropolis, Brazil

    Claudio Daniel Tenório de Barros

  3. Divisão de Físico-Química, Instituto de Química, Universidade de Brasília, Brasília, Brazil

    Fernando de Magalhães Coutinho Vieira

  4. Universidade Federal do Rio de Janeiro Campus Duque de Caxias Grupo de Biologia do Desenvolvimento em Sistemas Dinâmicos Programa de Pós-Graduação em Nanobiosistemas Instituto Federal do Rio de Janeiro Campus, Rio de Janeiro, Brazil

    Cristiano N. Costa

Authors
  1. Francisco José Pereira Lopes
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  2. Claudio Daniel Tenório de Barros
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  3. Josué Xavier de Carvalho
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  4. Fernando de Magalhães Coutinho Vieira
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  5. Cristiano N. Costa
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Correspondence to Francisco José Pereira Lopes .

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Editors and Affiliations

  1. Scientific Computing Program (PROCC), Oswaldo Cruz Foundation, Rio de Janeiro, Brazil

    Fabricio Alves Barbosa da Silva

  2. CDTS, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil

    Nicolas Carels

  3. Department of Computational Modeling, National Laboratory of Scientific Computing, Petrópolis, Rio de Janeiro, Brazil

    Marcelo Trindade dos Santos

  4. Graduate Program in Nanobiosystems, Federal University of Rio de Janeiro, Duque de Caxias, Rio de Janeiro, Brazil

    Francisco José Pereira Lopes

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Lopes, F.J.P., de Barros, C.D.T., de Carvalho, J.X., de Magalhães Coutinho Vieira, F., Costa, C.N. (2020). Reaction Network Models as a Tool to Study Gene Regulation and Cell Signaling in Development and Diseases. In: da Silva, F.A.B., Carels, N., Trindade dos Santos, M., Lopes, F.J.P. (eds) Networks in Systems Biology. Computational Biology, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-51862-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-51862-2_7

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