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Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

Plants respond to environmental change by triggering biochemical and developmental networks across multiple scales. Multiscale models that link genetic input to the whole-plant scale and beyond might therefore improve biological understanding and yield prediction. We report a modular approach to build such models, validated by a framework model of Arabidopsis thaliana comprising four existing mathematical models.

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, and Andrew J. Millar

 

Significance

 

Plants respond to environmental change by triggering biochemical and developmental networks across multiple scales. Multiscale models that link genetic input to the whole-plant scale and beyond might therefore improve biological understanding and yield prediction. We report a modular approach to build such models, validated by a framework model of Arabidopsis thaliana comprising four existing mathematical models. Our model brings together gene dynamics, carbon partitioning, organ growth, shoot architecture, and development in response to environmental signals. It predicted the biomass of each leaf in independent data, demonstrated flexible control of photosynthesis across photoperiods, and predicted the pleiotropic phenotype of a developmentally misregulated transgenic line. Systems biology, crop science, and ecology might thus be linked productively in a community-based approach to modeling.

 

Abstract

 

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.

 

See: http://www.pnas.org/content/111/39/E4127.abstract.html?etoc

PNAS, September 30 2014, Vol.111, No.39: E4127-E4136  

 

Fig. 2. The FM’s workflow predicts whole-plant and individual organ growth data. Input data required are hourly light intensity (A), CO2 level (B), and temperature (C), illustrated for simulated three 12-h light (open):12-h dark (shaded area) cycles. Carbon supply (D) is used as sugar (dashed line) or stored as starch (solid line). Carbon is allocated at each hourly time step according to the organ demand (E and F). The simulated pattern of demand from individual leaves (F, thin blue lines, left axis) is used to calculate the sum of demand (dots) from leaves (thick blue line, right axis) and roots (brown line, left axis). The root-to-shoot allocation ratio (E), calculated dynamically from the FSPM (red line), is similar to the piecewise-linear function prescribed in the CDM (31) (gray dashed line), which it replaces. Times of dawn and dusk (dots in A) affect the level of flowering gene FT mRNA (G) simulated by the PPM, which in turn controls the accumulation of modified photothermal units (MPTU; H). Once the accumulated photothermal units reach the threshold for flowering (dashed lines), rosette growth is terminated in the FSPM (red arrow). Model outputs include biomass of the shoot (I) and individual leaves (J). Simulations for the Col WT (green lines) closely match experimental data for (I) total shoot biomass, (J) leaf biomass, and (K) leaf area at 18 (○), 25 (●), 27 (□), and 38 (■) d after sowing. Leaves are ranked according to the order of appearance. The integrated model uses simulated sizes of individual leaves (K) to calculate the projected rosette area for photosynthesis (red arrow), considering the spiral leaf arrangement (phyllotaxy) and upward (zenithal) angle. Experimental conditions: ∼21.3 °C; 12:12-h light/dark cycle; light intensity, 110 μmol⋅m−2⋅s−1; mean daytime CO2 level, 375 ppm. The error bars show the SEs of five plants.

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