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SUMMARY:Predicting the pleiotropic effects of circadian timing\, from cloc
 k gene expression to biomass. - Professor Andrew Millar\, University of Ed
 inburgh
DTSTART:20170119T140000Z
DTEND:20170119T150000Z
UID:TALK69052@talks.cam.ac.uk
CONTACT:Caroline Newnham
DESCRIPTION:The 24-hour circadian clock controls biological processes from
  the sleep-wake cycle to the cell cycle (Millar\, 2016). We seek to unders
 tand how the dynamics of this gene regulatory network (Flis et al. 2015) c
 ontrol growth\, biomass and life history in the whole plant. The clock is 
 a case study to link mechanistic understanding at the molecular level to o
 rganismal and field performance. Breeders have selected clock-associated g
 ene variants in crop species (barley\, wheat\, tomato)\, for reasons that 
 are not always clear. Our ultimate aim is to understand both this artifici
 al selection and the natural selection of the clock gene circuit in the mo
 del plant species\, Arabidopsis thaliana. \nPlant growth in the daily ligh
 t:dark cycle depends upon molecular\, biochemical and physiological respon
 ses to light\, and on the 24-hour rhythms of the circadian clock. Predicta
 ble\, seasonal changes in day length demand further adjustment to the plan
 t’s daily programme and control flowering time. Using the rich data of t
 he Arabidopsis community\, we and our partners in the BBSRC ROBuST and EU 
 FP7 TiMet projects have built mathematical models of these processes betwe
 en germination and flowering\, including the crucial\, nightly utilisation
  of starch carbon stores (e.g. Seaton et al. Mol Syst Biol 2015). We recen
 tly combined models from three further biological research areas into the 
 Arabidopsis Framework Model (FM)\, which predicts biomass quantitatively i
 n reference conditions (Chew et al. PNAS 2014). We have recently used the 
 model\, together with metabolic\, molecular and whole-plant data\, to unde
 rstand quantitatively the pleiotropic phenotypes of a ‘slow’ clock mut
 ant. I will discuss the challenges for experiments and modelling\, and a c
 ommunity approach to broaden the quantitative links between genotype and p
 henotype.\n
LOCATION:Part II Room\, Department of Genetics
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