Abstract
Understanding how genotypes map unto phenotypes implies an integrative understanding of the processes regulating cell differentiation and morphogenesis, which comprise development. Such a task requires the use of theoretical and computational approaches to integrate and follow the concerted action of multiple genetic and nongenetic components that hold highly nonlinear interactions. Gene regulatory network (GRN) models have been proposed to approach such task. GRN models have become very useful to understand how such types of interactions restrict the multi-gene expression patterns that characterize different cell-fates. More recently, such temporal single-cell models have been extended to recover the temporal and spatial components of morphogenesis. Since the complete genomic GRN is still unknown and intractable for any organism, and some clear developmental modules have been identified, we focus here on the analysis of well-curated and experimentally grounded small GRN modules. One of the first experimentally grounded GRN that was proposed and validated corresponds to the regulatory module involved in floral organ determination. In this chapter we use this GRN as an example of the methodologies involved in: (1) formalizing and integrating molecular genetic data into the logical functions (Boolean functions) that rule gene interactions and dynamics in a Boolean GRN; (2) the algorithms and computational approaches used to recover the steady-states that correspond to each cell type, as well as the set of initial GRN configurations that lead to each one of such states (i.e., basins of attraction); (3) the approaches used to validate a GRN model using wild type and mutant or overexpression data, or to test the robustness of the GRN being proposed; (4) some of the methods that have been used to incorporate random fluctuations in the GRN Boolean functions and enable stochastic GRN models to address the temporal sequence with which gene configurations and cell fates are attained; (5) the methodologies used to approximate discrete Boolean GRN to continuous systems and their use in further dynamic analyses. The methodologies explained for the GRN of floral organ determination developed here in detail can be applied to any other functional developmental module.
Eugenio Azpeitia, José Davila-Velderrain, and Carlos Villarreal contributed equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Villarreal C, Padilla-Longoria P, Alvarez-Buylla ER (2012) General theory of genotype to phenotype mapping: derivation of epigenetic landscapes from N-node complex gene regulatory networks. Phys Rev Lett 109(118102):1–5
Alvarez-Buylla ER, Balleza E, Benítez M, Espinosa-Soto C, Padilla-Longoria P (2008) Gene regulatory network models: a dynamic and integrative approach to development. SEB Exp Biol Ser 61:113–139
Alvarez-Buylla ER, Azpeitia E, Barrio R, Benítez M, Padilla-Longoria P (2010) From ABC genes to regulatory networks, epigenetic landscapes and flower morphogenesis: making biological sense of theoretical approaches. Semin Cell Dev Biol 21(1):108–117
Alvarez-Buylla ER, Chaos A, Aldana M, Benítez M, Cortes-Poza Y, Espinosa-Soto C, Hartasánchez DA, Lotto RB, Malkin D, Escalera Santos GJ, Padilla-Longoria P (2008) Floral morphogenesis: stochastic explorations of a gene network epigenetic landscape. PLoS One 3(11):e3626
Mendoza L, Alvarez-Buylla ER (1998) Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis. J Theor Biol 193(2):307–319
Albert R, Othmer HG (2003) The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J Theor Biol 223(1):1–18
Espinosa-Soto C, Padilla-Longoria P, Alvarez-Buylla ER (2004) A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles. Plant Cell 16:2923–2939
Azpeitia E, Benítez M, Vega I, Villarreal C, Alvarez-Buylla ER (2010) Single-cell and coupled GRN models of cell patterning in the Arabidopsis thaliana root stem cell niche. BMC Syst Biol 4:134
Albert I, Thakar J, Li S, Zhang R, Albert R (2008) Boolean network simulations for life scientists. Source Code Biol Med 3:16
Albert R, Wang RS (2009) Discrete dynamic modeling of cellular signaling networks. Methods Enzymol 467:281–306
Assmann SM, Albert R (2009) Discrete dynamic modeling with asynchronous update, or how to model complex systems in the absence of quantitative information. Methods Mol Biol 553:207–225
Alvarez-Buylla ER, Benítez M, Corvera-Poiré A, Chaos CA, de Folter S, Gamboa de Buen A, Garay-Arroyo A, García-Ponce B, Jaimes- MF, Pérez-Ruiz RV, Piñeyro-Nelson A, Sánchez-Corrales YE (2010) Flower development. Arabidopsis Book 8:e0127
Pelaz S, Tapia-López R, Alvarez-Buylla ER, Yanofsky MF (2001) Conversion of leaves into petals in Arabidopsis. Curr Biol 11(3):182–184
Barrio RÁ, Hernández-Machado A, Varea C, Romero-Arias JR, Alvarez-Buylla E (2010) Flower development as an interplay between dynamical physical fields and genetic networks. PLoS One 5(10):e13523
Kauffman S (1969) Homeostasis and differentiation in random genetic control networks. Nature 224:177–178
Mendoza L, Thieffry D, Alvarez-Buylla ER (1999) Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis. Bioinformatics 15(7–8):593–606
Chaos Á, Aldana M, Espinosa-Soto C et al (2006) From genes to flower patterns and evolution: dynamic models of gene regulatory networks. J Plant Growth Regul 25(4):278–289
Sanchez-Corrales YE, Alvarez-Buylla ER, Mendoza L (2010) The Arabidopsis thaliana flower organ specification gene regulatory network determines a robust differentiation process. J Theor Biol 264:971–983
La Rota C, Chopard J, Das P, Paindavoine S, Rozier F, Farcot E, Godin C, Traas J, Monéger F (2011) A data-driven integrative model of sepal primordium polarity in Arabidopsis. Plant Cell 23(12):4318–4333
Garg A, Mohanram K, De Micheli G, Xenarios I (2012) Implicit methods for qualitative modeling of gene regulatory networks. Methods Mol Biol 786:397–443
Alvarez J, Guli CL, Yu XH, Smyth DR (1992) terminal flower: a gene affecting inflorescence development in Arabidopsis thaliana. Plant J 2(1):103–116
Shannon S, Meeks-Wagner DR (1991) A mutation in the Arabidopsis TFL1 gene affects inflorescence meristem development. Plant Cell 3(9):877–892
Parcy F, Bomblies K, Weigel D (2002) Interaction of LEAFY, AGAMOUS and TERMINAL FLOWER1 in maintaining floral meristem identity in Arabidopsis. Development 129(10):2519–2527
Conti L, Bradley D (2007) TERMINAL FLOWER1 is a mobile signal controlling Arabidopsis architecture. Plant Cell 19(3):767–778
Chen L, Cheng JC, Castle L, Sung ZR (1997) EMF genes regulate Arabidopsis inflorescence development. Plant Cell 9(11):2011–2024
Liljegren SJ, Gustafson-Brown C, Pinyopich A (1999) Interactions among APETALA1, LEAFY, and TERMINAL FLOWER1 specify meristem fate. Plant Cell 11(6):1007–1018
Ratcliffe OJ, Bradley DJ, Coen ES (1999) Separation of shoot and floral identity in Arabidopsis. Development 126(6):1109–1120
Gustafson-Brown C, Savidge B, Yanofsky MF (1994) Regulation of the Arabidopsis floral homeotic gene APETALA1. Cell 76(1):131–143
Gómez-Mena C, de Folter S, Costa MMR, Angenent GC, Sablowski R (2005) Transcriptional program controlled by the floral homeotic gene agamous during early organogenesis. Development 132(3):429–438
Kitano H (2007) Towards a theory of biological robustness. Mol Syst Biol 3:137
Whitacre JM (2012) Biological robustness: paradigms, mechanisms, and systems principles. Front Genet 3:67
Garg A, Mohanram K, Di Cara A, De Micheli G, Xenarios I (2009) Modeling stochasticity and robustness in gene regulatory networks. Bioinformatics 25:i101–i109
Samoilov MS, Price G, Arkin AP (2006) From fluctuations to phenotypes: the physiology of noise. Sci STKE 2006:re17
Hoffmann M, Chang HH, Huang S, Ingber DE, Loeffler M, Galle J (2008) Noise-driven stem cell and progenitor population dynamics. PLoS One 3(8):e2922
Eldar A, Elowitz MB (2010) Functional roles for noise in genetic circuits. Nature 467(7312):167–173
Balázsi G, van Oudenaarden A, Collins JJ (2011) Cellular decision making and biological noise: from microbes to mammals. Cell 144(6):910–925
Horsthemke W, Lefever R (1984) Noise-induced transitions: theory and applications in physics, chemistry, and biology. Springer, Berlin
Chalancon G, Ravarani CNJ, Balaji S, Martinez-Arias A, Aravind L, Jothi R, Babu MM (2012) Interplay between gene expression noise and regulatory network architecture. Trends Genet 28(5):221–232
Glass L (1975) Classification of biological networks by their qualitative dynamics. J Theor Biol 54:85–107
Mendoza L, Xenarios I (2006) A method for the generation of standardized qualitative dynamical systems of regulatory networks. Theor Biol Med Model 3:13
Ferrell JE Jr (2012) Bistability, bifurcations, and Waddington’s epigenetic landscape. Curr Biol 22:R458–R466
Zhou JX, Brusch L, Huang S (2011) Predicting pancreas cell fate decisions and reprogramming with a hierarchical multi-attractor model. PLoS One 6(3):e14752
Wang J, Zhang K, Xua L, Wang E (2011) Quantifying the Waddington landscape and biological paths for development and differentiation. Proc Natl Acad Sci 108:8257–8262
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Arellano G, Argil J, Azpeitia E, Benítez M, Carrillo M, Góngora P, Rosenblueth DA, Alvarez-Buylla ER (2011) “Antelope”: a hybrid-logic model checker for branching-time Boolean GRN analysis. BMC Bioinformatics 12:490
Müssel C, Hopfensitz M, Kestler HA (2010) BoolNet—an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics 26(10):1378–1380
von Dassow G, Meir E, Munro EM, Odell GM (2000) The segment polarity network is a robust developmental module. Nature 406(6792):188–192. doi:10.1038/35018085
Naldi A, Berenguier D, Fauré A, Lopez F, Chaouiya C (2009) Logical modelling of regulatory networks with GINsim 2.3. Biosystems 97(2):134–139
Corblin F, Fanchon E, Trilling L (2010) Applications of a formal approach to decipher discrete genetic networks. BMC Bioinformatics 11(1):385
de Jong H, Geiselmann J, Hernandez C, Page M (2003) Genetic network analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 19(3):336–344
Calzone L, Fages F, Soliman S (2006) Biocham: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22(14):1805–1807
Azpeitia E, Benítez M, Padilla-Longoria P, Espinosa-Soto C, Alvarez-Buylla ER (2011) Dynamic network-based epistasis analysis: boolean examples. Front Plant Sci 2:92
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media, New York
About this protocol
Cite this protocol
Azpeitia, E., Davila-Velderrain, J., Villarreal, C., Alvarez-Buylla, E.R. (2014). Gene Regulatory Network Models for Floral Organ Determination. In: Riechmann, J., Wellmer, F. (eds) Flower Development. Methods in Molecular Biology, vol 1110. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4614-9408-9_26
Download citation
DOI: https://doi.org/10.1007/978-1-4614-9408-9_26
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4614-9407-2
Online ISBN: 978-1-4614-9408-9
eBook Packages: Springer Protocols