Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

Authors Organisations
  • Yin Hoon Chew(Author)
    University of Edinburgh
  • Benedicte Wenden(Author)
    National Institute of Agricultural Research
  • Anna Flis(Author)
    Max Planck Institute of Molecular Plant Physiology
  • Virginie Mengin(Author)
    Max Planck Institute of Molecular Plant Physiology
  • Jasper Taylor(Author)
    Simulistics Ltd
  • Chris Davey(Author)
  • Christopher Tindal(Author)
    University of Edinburgh
  • Howard Thomas(Author)
  • Helen Ougham(Author)
  • Philippe De Reffye(Author)
    Cirad-Amis, Unité Mixte de Recherche, botAnique et bioinforMatique de l'Architecture des Plantes
  • Mark Stitt(Author)
    Max Planck Institute of Molecular Plant Physiology
  • Mathew Williams(Author)
    University of Edinburgh
  • Robert Muetzelfeldt(Author)
    Simulistics Ltd
  • Karen J. Halliday(Author)
    University of Edinburgh
  • Andrew J. Millar(Author)
    University of Edinburgh
Type Article
Original languageEnglish
Pages (from-to)E4127-E4136
Number of pages10
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number39
Early online date02 Sept 2014
Publication statusPublished - 30 Sept 2014
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Understanding how dynamic molecular networks affect whole-organism 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.


  • plant growth model, digital organism, crop modelling, ecology