Gas exchange of Fei-0 and Ler plants in 12hL:12hD

Data for Figure 3G and Supplementary Figure 4, including gas exchange measurements and photo of the experimental setup. The 'Summary' sheets in the XLSX files often include published graphs. Simulation data are included from FMv1.

These data were acquired in a separate experiment from the biomass, in March 2013. Replication of the earlier biomass study was imperfect, as some plants became a little dry when watering was controlled to reduce moss growth. Sufficient plants grew strongly to measure ...

Submitter: Andrew Millar

Assay type: Cultivation Experiment

Technology type: Cultivation experiment

Investigation: Arabidopsis Framework Model v1, predicting rose...

Study: Test of FMv1, growth study of other accessions ...

Flux Balance Analysis of multi-compartment metabolic model of growing Arabidopsis thaliana

The multi-compartmental metabolic network of Arabidopsis thaliana was reconstructed and optimized in order to explain growth stoichiometry of the plant both in light and in dark conditions. Balances and turnover of energy (ATP/ADP) and redox (NAD(P)H/NAD(P)) metabolites as well as proton in different compartments were estimated. The model showed that in light conditions, the plastid ATP balance depended on the relationship between fluxes through photorespiration and photosynthesis including both ...

Submitter: Maksim Zakhartsev

Biological problem addressed: Metabolic Network

Investigation: Metabolic analysis of effects of sucrose transl...

Study: Analysis of central carbon and energy metabolis...

Framework Model v2

Framework Model for Arabidopsis vegetative growth, version 2 (FMv2), as described in Chew et al. bioRxiv 2017 (https://doi.org/10.1101/105437; please see linked Article file).

The FMv2 model record on FAIRDOMHub has the following versions, which represent the same FMv2 model: Version 1 is an archive of the github repository of MATLAB code for the Framework Model v2, downloaded from https://github.com/danielseaton/frameworkmodel on 06/02/17. This version was not licensed for further use and was ...

Creators: Daniel Seaton, Yin Hoon Chew, Andrew Millar

Submitter: Daniel Seaton

External Link
Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

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

Abstract (Expand)

Summary paragraph 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 …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 plants 1–3 . Clock genes are rarely essential but appropriate alleles can confer a competitive advantage 4,5 and have been repeatedly selected during crop domestication 3,6 . 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 thaliana 7–9 . 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 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: 6th Feb 2017

Publication Type: Tech report

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