Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

Chew Yin Hoon, Wenden Bénédicte, Flis Anna, Mengin Virginie, Taylor Jasper, Davey Christopher L., Tindal Christopher, Thomas Howard, Ougham Helen J., De Reffye Philippe, Stitt Mark, Williams Mathew, Muetzelfeldt Robert, Halliday Karen J., Millar Andrew J.. 2014. Multiscale digital Arabidopsis predicts individual organ and whole-organism growth. Proceedings of the National Academy of Sciences of the United States of America, 111 (39) : pp. 4127-4136.

Journal article ; Article de revue à facteur d'impact
Published version - Anglais
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Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Economie-gestion; Psychologie-éthologie-ergonomie

Abstract : Understanding how dynamic molecular networks affect wholeorganism physiology, analogous to mapping genotype to phenotype, remains a key challenge in biology. Quantitative models that represent processes at multiple scales and link understanding from several research domains can help to tackle this problem. Such integrated models are more common in crop science and ecophysiology than in the research communities that elucidate molecular networks. Several laboratories have modeled particular aspects of growth in Arabidopsis thaliana, but it was unclear whether these existing models could productively be combined. We test this approach by constructing a multiscale model of Arabidopsis rosette growth. Four existing models were integrated with minimal parameter modification (leaf water content and one flowering parameter used measured data). The resulting framework model links genetic regulation and biochemical dynamics to events at the organ and whole-plant levels, helping to understand the combined effects of endogenous and environmental regulators on Arabidopsis growth. The framework model was validated and tested with metabolic, physiological, and biomass data from two laboratories, for five photoperiods, three accessions, and a transgenic line, highlighting the plasticity of plant growth strategies. The model was extended to include stochastic development. Model simulations gave insight into the developmental control of leaf production and provided a quantitative explanation for the pleiotropic developmental phenotype caused by overexpression of miR156, which was an open question. Modular, multiscale models, assembling knowledge from systems biology to ecophysiology, will help to understand and to engineer plant behavior from the genome to the field. (Résumé d'auteur)

Mots-clés Agrovoc : Arabidopsis thaliana, Modèle de simulation, Modélisation des cultures, Croissance, Physiologie végétale, Génétique, Modèle végétal

Classification Agris : U10 - Computer science, mathematics and statistics
F62 - Plant physiology - Growth and development
F30 - Plant genetics and breeding

Champ stratégique Cirad : Hors axes (2014-2018)

Auteurs et affiliations

  • Chew Yin Hoon, University of Edinburgh (GBR)
  • Wenden Bénédicte, INRA (FRA)
  • Flis Anna, Max Planck Institute of Molecular Plant Physiology (DEU)
  • Mengin Virginie, Max Planck Institute of Molecular Plant Physiology (DEU)
  • Taylor Jasper, Simulistics Ltd (GBR)
  • Davey Christopher L., Aberystwyth University (GBR)
  • Tindal Christopher, University of Edinburgh (GBR)
  • Thomas Howard, Aberystwyth University (GBR)
  • Ougham Helen J., Aberystwyth University (GBR)
  • De Reffye Philippe, CIRAD-BIOS-UMR AMAP (FRA)
  • Stitt Mark, Max Planck Institute of Molecular Plant Physiology (DEU)
  • Williams Mathew, University of Edinburgh (GBR)
  • Muetzelfeldt Robert, Simulistics Ltd (GBR)
  • Halliday Karen J., University of Edinburgh (GBR)
  • Millar Andrew J., University of Edinburgh (GBR)

Source : Cirad - Agritrop (

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