Composition of Framework Model FMv1.5
Millar group, Regulation of flowering time in natural conditions, PlaSMo model repository

Model files for FMv1.5, based on FMv1 of Chew et al. PNAS 2014, as extended by Hannah Kinmonth-Schultz and Daniel Seaton, in Matlab.
**Files used for published simulations will be attached here upon final publication, possibly with a later version that increases commenting. Our apologies for any delay.**

Contributor: Andrew Millar

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

Chew_et_al_2014_Framework_Model version 1, Matlab and Simile
Millar group

This record includes Matlab and Simile format versions of the Arabidopsis Framework Model version 1, FMv1 (Chew et al, PNAS 2014; http://www.pnas.org/content/early/2014/08/27/1410238111), copied from the PlaSMo resource (www.plasmo.ed.ac.uk), PLM_ID=76. The model description is in the Supplementary Materials of the publication, which should be uploaded somewhere here also but I don't see how to do it.

The FMv1 links the following sub-models:
1. Arabidopsis leaf carbohydrate model (Rasse and
...

Creators: Andrew Millar, Yin Hoon Chew

Contributor: Andrew Millar

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Mechanistic model of temperature influence on flowering through whole-plant accumulation of FT
Regulation of flowering time in natural conditions

Abstract

BioRxiv preprint:

Authors: Hannah A Kinmonth-Schultz, Melissa J MacEwen, Daniel D Seaton, Andrew J Millar, Takato Imaizumi, Soo-Hyung Kim

Date Published: No date defined

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

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

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