TY - JOUR
T1 - Multiscale digital Arabidopsis predicts individual organ and whole-organism growth
AU - Chew, Yin Hoon
AU - Wenden, Benedicte
AU - Flis, Anna
AU - Mengin, Virginie
AU - Taylor, Jasper
AU - Davey, Christopher L.
AU - Tindal, Christopher
AU - Thomas, Howard
AU - Ougham, Helen J.
AU - De Reffye, Philippe
AU - Stitt, Mark
AU - Williams, Mathew
AU - Muetzelfeldt, Robert
AU - Halliday, Karen J.
AU - Millar, Andrew J.
N1 - Y.H.C. was a recipient of the Darwin Trust PhD studentship. This study was supported by Biotechnology and Biological Sciences Research Council Awards Bioinformatics and Biological Resources Fund BB/F010605/1 (to H.J.O. and A.J.M.) and Regulation of Biological Signaling by Temperature BB/F005237/1 (to K.J.H., M.W., and A.J.M.) and European Commission FP7 collaborative project TiMet Contract 245143 (to A.J.M. and M.S.). This is the author accepted manuscript. The final version is available from National Academy of Sciences via http://dx.doi.org/10.1073/pnas.1410238111.
PY - 2014/9/30
Y1 - 2014/9/30
N2 - 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.
AB - 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.
KW - plant growth model
KW - digital organism
KW - crop modelling
KW - ecology
UR - http://hdl.handle.net/2160/30646
UR - https://www.pnas.org/content/111/39/E4127/tab-figures-data
U2 - 10.1073/pnas.1410238111
DO - 10.1073/pnas.1410238111
M3 - Article
SN - 0027-8424
VL - 111
SP - E4127-E4136
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 39
ER -