Analysis of labeling kinetics, pool sizes, and concentration gradients of metabolites reveals the operation of multiple decarboxylation pathways and rapid movement of carbon between the Calvin–Benson cycle and the CO2-concentrating shuttles in maize.

Photoperiod duration can be predicted from previous days, but irradiance fluctuates in an unpredictable manner. To investigate how allocation to starch responds to changes in these two environmental variables, Arabidopsis Col-0 was grown in a 6 h and a 12 h photoperiod at three different irradiances. The absolute rate of starch accumulation increased when photoperiod duration was shortened and when irradiance was increased. The proportion of photosynthate allocated to starch increased strongly when photoperiod duration was decreased but only slightly when irradiance was decreased. There was a small increase in the daytime level of sucrose and twofold increases in glucose, fructose and glucose 6-phosphate at a given irradiance in short photoperiods compared to long photoperiods. The rate of starch accumulation correlated strongly with sucrose and glucose levels in the light, irrespective of whether these sugars were responding to a change in photoperiod or irradiance. Whole plant carbon budget modelling revealed a selective restriction of growth in the light period in short photoperiods. It is proposed that photoperiod sensing, possibly related to the duration of the night, restricts growth in the light period in short photoperiods, increasing allocation to starch and providing more carbon reserves to support metabolism and growth in the long night.

The carbon and nitrogen metabolism of Arabidopsis plants grown in sunlight differs from plants grown with artificial light, even when the spectral quality and sinusoidal profile of sunlight are approximated experimentally.

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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.