Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

Yin Hoon Chew, Bénédicte Wenden, Anna Flis, Virginie Mengin, Jasper Taylor, Christopher L. Davey, Christopher Tindal, Howard Thomas, Helen J. Ougham, Philippe De Reffye, Mark Stitt, Mathew Williams, Robert Muetzelfeldt, Karen J. Halliday, Andrew J. Millar

Research output: Contribution to journalArticlepeer-review

87 Scopus citations


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.

Original languageEnglish
Pages (from-to)E4127-E4136
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number39
StatePublished - 30 Sep 2014
Externally publishedYes


  • Crop modeling
  • Digital organism
  • Ecology
  • Plant growth model


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