Predicting the pleiotropic effects of circadian timing, from clock gene expression to biomass.

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• Professor Andrew Millar, University of Edinburgh
• Thursday 19 January 2017, 14:00-15:00
• Part II Room, Department of Genetics.

If you have a question about this talk, please contact Caroline Newnham.

Host: Alfonso Martinez-Arias

The 24-hour circadian clock controls biological processes from the sleep-wake cycle to the cell cycle (Millar, 2016). We seek to understand how the dynamics of this gene regulatory network (Flis et al. 2015) control growth, biomass and life history in the whole plant. The clock is a case study to link mechanistic understanding at the molecular level to organismal and field performance. Breeders have selected clock-associated gene variants in crop species (barley, wheat, tomato), for reasons that are not always clear. Our ultimate aim is to understand both this artificial selection and the natural selection of the clock gene circuit in the model plant species, Arabidopsis thaliana. Plant growth in the daily light:dark cycle depends upon molecular, biochemical and physiological responses to light, and on the 24-hour rhythms of the circadian clock. Predictable, seasonal changes in day length demand further adjustment to the plant’s daily programme and control flowering time. Using the rich data of the Arabidopsis community, we and our partners in the BBSRC RO BuST and EU FP7 TiMet projects have built mathematical models of these processes between germination and flowering, including the crucial, nightly utilisation of starch carbon stores (e.g. Seaton et al. Mol Syst Biol 2015). We recently combined models from three further biological research areas into the Arabidopsis Framework Model (FM), which predicts biomass quantitatively in reference conditions (Chew et al. PNAS 2014 ). We have recently used the model, together with metabolic, molecular and whole-plant data, to understand quantitatively the pleiotropic phenotypes of a ‘slow’ clock mutant. I will discuss the challenges for experiments and modelling, and a community approach to broaden the quantitative links between genotype and phenotype.

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