Skip to main content

Simulating Genotype-Phenotype Interaction Using Extended Functional-Structural Plant Models: Approaches, Applications and Potential Pitfalls

  • Chapter

Abstract

Modelling approaches have increasingly been used as a supplementary tool in understanding the build-up and diversity of crop phenotypes, and their relations with morphogenesis. Among these approaches, Functional-Structural Plant Models (FSPMs) have been developed to simulate complex interactions between plant architecture and physiological processes. In this chapter, we introduce an FSPM of rice that simulates growth and morphology of individual rice plants and of small stands from germination to seed maturity. This model covers selected ecophysiological processes including photosynthesis and sink functions based on a common assimilate pool. We furthermore introduce here for the first time an extension of the rice FSPM with a module for genetics, which constitutes a genotype-phenotype model coupling quantitative genetic information of the phenotypic trait plant height with the morphogenetic rules leading to this composite trait. Lastly, a virtual breeding model is presented: this extended model enables the virtual reproduction of quantitative genetic information and the generation of a new simulated mapping population, in both its phenotypic and genotypic form. Finally, the current pitfalls and problems, and the potential uses of the virtual breeding model are discussed.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Borjigidai A, Hikosaka K, Hirose T et al. (2006) Seasonal changes in temperature dependence of photosynthetic rate in rice under a free-air CO2 enrichment. Ann Bot 97:549–557

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Buck-Sorlin GH (2013) Functional-structural plant modelling. In: Dubitzky W, Wolkenhauer O, Cho K, Yokota H (eds) Encyclopedia of systems biology. Springer, New York, pp 778–781

    Chapter  Google Scholar 

  • Buck-Sorlin GH, Kniemeyer O, Kurth W (2005) Barley morphology, genetics and hormonal regulation of internode elongation modelled by a relational growth grammar. New Phytol 166:859–867

    Article  PubMed  Google Scholar 

  • Buck-Sorlin GH, Kniemeyer O, Kurth W (2007) A grammar-based model of barley including virtual breeding, genetic control and a hormonal metabolic network. In: Vos J, Marcelis LFM, de Visser PHB, Struik PC, Evers JB (eds) Functional-structural plant modelling in crop production. Springer, The Netherlands, pp 243–252

    Chapter  Google Scholar 

  • Buck-Sorlin GH, Hemmerling R, Kniemeyer O et al. (2008) A rule-based model of barley morphogenesis, with special respect to shading and gibberellic acid signal transduction. Ann Bot 101:1109–1123

    Article  PubMed  PubMed Central  Google Scholar 

  • Buck-Sorlin GH, de Visser PHB, Henke M et al. (2011) Towards a functional-structural plant model of cut-rose – simulation of light environment, light absorption, photosynthesis and interferences with the plant structure. Ann Bot 108:1121–1134

    Article  PubMed  PubMed Central  Google Scholar 

  • Chenu K, Franck N, Lecoeur J (2007) Simulations of virtual plants reveal a role for SERRATE in the response of leaf development to light in Arabidopsis thaliana. New Phytol 175:472–481

    Article  PubMed  CAS  Google Scholar 

  • Clark B, Bullock S (2007) Shedding light on plant competition: modelling the influence of plant morphology on light capture (and vice versa). J Theor Biol 244:208–217

    Article  PubMed  Google Scholar 

  • Cournède PH, Chen Y, Wu QL et al. (2013) Development and evaluation of plant growth models: methodology and implementation in the Pygmalion platform. Math Model Nat Phenom 8(4):112–130

    Article  Google Scholar 

  • Drouet JL, Pagès L (2007) GRAAL-CN: a model of growth, architecture and allocation for carbon and nitrogen dynamics within whole plants formalised at the organ level. Ecol Model 206:231–249

    Article  CAS  Google Scholar 

  • Fournier C, Andrieu B (1999) ADEL-maize: an L-system based model for the integration of growth processes from the organ to the canopy. Application to regulation of morphogenesis by light availability. Agronomie 19:313–327

    Article  Google Scholar 

  • Fournier C, Andrieu B, Ljutovac S et al. (2003) Adel-wheat: a 3D architectural model of wheat development. In: Hu B, Jaeger M (eds) Plant growth modeling and applications. Tsinghua University Press/Springer, Beijing, pp 54–66

    Google Scholar 

  • Fournier C, Andrieu B, Buck-Sorlin GH et al. (2007) Functional-structural modelling of gramineae. In: Vos J, Marcelis LFM, de Visser PHB, Struik PC, Evers JB (eds) Functional-structural plant modelling in crop production. Springer, Dordrecht, pp 175–186

    Chapter  Google Scholar 

  • Fukuoka S, Nonoue Y, Yano M (2010) Germplasm enhancement by developing advanced plant materials from diverse rice accessions. Breed Sci 60:509–517

    Article  Google Scholar 

  • Goudriaan J, van Laar HH (1994) Modelling potential crop growth processes. Kluwer, Dordrecht

    Book  Google Scholar 

  • Haldane JBS, Waddington CH (1931) Inbreeding and linkage. Genetics 16:357–374

    PubMed  CAS  PubMed Central  Google Scholar 

  • Hammer GL, Cooper M, Tardieu F et al. (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11:587–593

    Article  PubMed  CAS  Google Scholar 

  • Hemmerling R, Kniemeyer O, Lanwert D et al. (2008) The rule-based language XL and the modelling environment GroIMP illustrated with simulated tree competition. Funct Plant Biol 35:739–750

    Article  Google Scholar 

  • Kitchen JL, Allaby RG (2013) Systems modeling at multiple levels of regulation: linking systems and genetic networks to spatially explicit plant populations. Plant 2:16–49

    Article  Google Scholar 

  • Kniemeyer O (2008) Design and implementation of a graph grammar based language for functional-structural plant modelling. Doctoral dissertation, University of Technology at Cottbus

    Google Scholar 

  • Kniemeyer O, Buck-Sorlin GH, Kurth W (2003) Representation of genotype and phenotype in a coherent framework based on extended L-systems. Lect Notes Artif Intel 2801:625–634

    Google Scholar 

  • Lecoeur J, Roire-Lassus R, Christophe A et al. (2011) Quantifying physiological determinants of genetic variation for yield potential in sunflower. SUNFLO: a model-based analysis. Funct Plant Biol 38:246–259

    Article  Google Scholar 

  • Luquet D, Dingkuhn M, Kim H et al. (2006) EcoMeristem, a model of morphogenesis and competition among sinks in rice. 1. Concept, validation and sensitivity analysis. Funct Plant Biol 33:309–323

    Article  Google Scholar 

  • Luquet D, Song YH, Elbelt S et al. (2007) Model-assisted physiological analysis of phyllo, a rice architectural mutant. Funct Plant Biol 34:11–23

    Article  Google Scholar 

  • Luquet D, Soulié JC, Rebolledo MC (2012) Developmental dynamics and early growth vigour in rice. 2. Modelling genetic diversity using ecomeristem. J Agron Crop Sci 198:385–398

    Article  Google Scholar 

  • Nikolov NT, Massman WJ, Schoettle AW (1995) Coupling biochemical and biophysical processes at the leaf level: an equilibrium photosynthesis model for leaves of C3 plants. Ecol Model 80:205–235

    Article  CAS  Google Scholar 

  • Qu H, Wang Y, Cai L (2012) Orange tree simulation under heterogeneous environment using agent-based model ORASIM. Simul Model Pract Theor 23:19–35

    Article  Google Scholar 

  • Reymond M, Muller B, Tardieu F (2004) Dealing with the genotype × environment interaction via a modelling approach: a comparison of QTLs of maize leaf length or width with QTLs of model parameters. J Exp Bot 55:2461–2472

    Article  PubMed  CAS  Google Scholar 

  • Uptmoor R, Schrag T, Stuetzel H et al. (2008) Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in Brassica oleracea. Mol Breed 21:205–216

    Article  Google Scholar 

  • Xu L, Henke M, Zhu J et al. (2010) A rule-based functional-structural model of rice considering source and sink functions. In: Li B, Jaeger M, Guo Y (eds) Plant growth modelling, simulation, visualisation and applications. Proceedings–PMA09. IEEE Computer Society, Los Alamitos, pp 245–252

    Google Scholar 

  • Xu L, Henke M, Zhu J et al. (2011) A functional–structural model of rice linking quantitative genetic information with morphological development and physiological processes. Ann Bot 107:817–828

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Xu L, Ding W, Zhu J et al. (2012) Simulating superior genotypes for plant height based on QTLs: towards virtual breeding of rice. In: Kang M, Dumont Y, Guo Y (eds) 2012 IEEE 4th international symposium on plant growth modeling, simulation, visualization and applications (PMA12), Shanghai, pp 447–454

    Google Scholar 

  • Yamamoto T, Nagasaki H, Yonemaru J et al. (2010) Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms. BMC Genomics 11:267. doi:10.1186/1471-2164-11-267

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang J, Hu C, Hu H et al. (2008) QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24:721–723. doi:10.1093/bioinformatics/btm494

    Article  PubMed  Google Scholar 

  • Yin X, van Laar HH (2005) Crop systems dynamics: an ecophysiological simulation model for genotype-by-environment interactions. Wageningen Academic, Wageningen

    Book  Google Scholar 

  • Yin X, Kropff MJ, Stam P (1999) The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley. Heredity 82:415–421

    Article  PubMed  Google Scholar 

  • Yin X, Goudriaan J, Lantinga EA et al. (2003) A flexible sigmoid function of determinate growth. Ann Bot 91:361–371 (with erratum in Ann Bot 91:753, 2003)

    Article  PubMed  PubMed Central  Google Scholar 

  • Yin X, Van Oijen M, Schapendonk AHCM (2004) Extension of a biochemical model for the generalized stoichiometry of electron transport limited C3 photosynthesis. Plant Cell Environ 27(10):1211–1222

    Article  CAS  Google Scholar 

  • Zeng Z (2000) Statistical methods for mapping quantitative trait loci, program in statistical genetics. North Carolina State University, North Carolina

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gerhard Buck-Sorlin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Xu, L., Buck-Sorlin, G. (2016). Simulating Genotype-Phenotype Interaction Using Extended Functional-Structural Plant Models: Approaches, Applications and Potential Pitfalls. In: Yin, X., Struik, P. (eds) Crop Systems Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-20562-5_2

Download citation

Publish with us

Policies and ethics