Biomass of Fei-0 Arabidopsis rosette and leaves
Millar group

Data for Figs. 3D, 3E in Chew et al. PNAS 2014
Fresh biomass, dry biomass (i.e. after baking out all water),
SLA - specific leaf area (area per g)
Also FMv1 model simulation results.

The same data are available on the BioDare resource, with additional experimental meta data on growth conditions.
BioDare ID 13790837647786, title "Physiology experiment using Fei", the direct link is:
https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13790837647786

Creator: Yin Hoon Chew

Contributor: Andrew Millar

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Biomass of Ler Arabidopsis rosette and individual leaves
Millar group

Data for Figs. 3A, 3B in Chew et al. PNAS 2014
Fresh biomass, dry biomass (i.e. after baking out all water),
SLA - specific leaf area (area per g)

Also contains model simulation data from the FMv1
The same data are available, with additional experimental meta data on growth conditions, from the BioDare resource:
BioDare experiment 13790834110003; title "Physiology experiment using Ler", the direct link is:
https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13790834110003

Creator: Yin Hoon Chew

Contributor: Andrew Millar

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Biomass and area of Col-0 and 35S:miR156 Arabidopsis rosette and leaves
Millar group

Data for Figs. 5D, 5E and Supp 7C in Chew et al. PNAS 2014
FW - fresh weight
DW - dry weight (i.e. after baking out all water)
SLA - specific leaf area (area per g mass), indicates leaf thickness
Indiv, data for individual leaves rather than rosette

Creator: Yin Hoon Chew

Contributor: Andrew Millar

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Data images of Arabidopsis Col-0 and 35S:miR156 plants, as .JPG files in .ZIP archive
Millar group

Experiment conducted in early April 2014
Intact rosette is pictured, with plant number and genotype in handwritten labels, and ruler for scale.
Then dissected leaves are organised in sequence of age,
if necessary with small cuts to let them lie flat.

Areas are then measured in image processing.

Creator: Yin Hoon Chew

Contributor: Andrew Millar

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Multiscale digital Arabidopsis predicts individual organ and whole-organism growth
Millar group

Abstract (Expand)

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.

Authors: Y. H. Chew, B. Wenden, A. Flis, V. Mengin, J. Taylor, C. L. Davey, C. Tindal, H. Thomas, H. J. Ougham, P. de Reffye, M. Stitt, M. Williams, R. Muetzelfeldt, K. J. Halliday, A. J. Millar

Date Published: 10th Sep 2014

Publication Type: Not specified

Linking circadian time to growth rate quantitatively via carbon metabolism
Millar group

Abstract (Expand)

Predicting a multicellular organism's phenotype quantitatively from its genotype is challenging, as genetic effects must propagate up time and length scales. Circadian clocks are intracellular … regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour, from sleep/wake cycles in mammals to flowering in plants1-3. Clock genes are rarely essential but appropriate alleles can confer a competitive advantage4,5 and have been repeatedly selected during crop domestication3,6. Here we quantitatively explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for growth of Arabidopsis thaliana7-9. The model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants. Altered night-time metabolism of stored starch accounted for most but not all of the decrease in whole-plant growth rate. Altered mobilisation of a secondary store of organic acids quantitatively explained the remaining defect. Our results link genotype through specific processes to higher-level phenotypes, formalising our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits.

Authors: Yin Hoon Chew, Daniel D. Seaton, Virginie Mengin, Anna Flis, Sam T. Mugford, Alison M. Smith, Mark Stitt, Andrew J Millar

Date Published: No date defined

Publication Type: Not specified

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