Table EV1 - Quantitative proteomics dataset

Version 1

SynthSys is the University of Edinburgh's research organisation in interdisciplinary, Synthetic and Systems Biology, founded in 2012 as the successor to the Centre for Systems Biology at Edinburgh (CSBE).

Andrew Millar's research group, University of Edinburgh

EU FP7 collaborative project TiMet, award number 245143. Funded 2010-2015. "TiMet assembles world leaders in experimental and theoretical plant systems biology to advance understanding of the regulatory interactions between the circadian clock and plant metabolism, and their emergent effects on whole-plant growth and productivity."

Click on Snapshot 2 to download data, models and analysis for Daniel Seaton et al. biorXiv 2017 https://doi.org/10.1101/182071 and Molecular Systems Biology, accepted Jan 2018, https://doi.org/10.15252/msb.20177962. Note that the published paper cannot be fully linked into this record as the DOI above was not live when we made the Research Object from this Investigation on FAIRDOMHub.

Experimental data reported in the Seaton et al. 2017 study; data processing by Alex Graf. Part of the EU FP7 TiMet project.

Data analysis and modelling scripts and results for the Seaton et al. 2017 study, from Daniel Seaton.

Plant material The same plant material used for transcriptome analysis in (Flis et al., 2016) was the basis of our proteome study. Briefly, Arabidopsis thaliana Col-0 plants were grown on GS 90 soil mixed in a ratio 2:1 (v/v) with vermiculite. Plants were grown for 1 week in a 16 h light (250 μmol m−2 s−1, 20 °C)/8 h dark (6 °C) regime followed by an 8 h light (160 μmol m−2 s−1, 20 °C)/16 h dark (16 °C) regime for one week. Plants were then replanted with five seedlings per pot, transferred for ...

These Python scripts define and simulate the translational coincidence model. This model takes measured transcript dynamics (Blasing et al, 2005) in 12L:12D, measured synthesis rates of protein in light compared to dark (Pal et al, 2013), and outputs predicted changes in protein abundance between short (6h) and long (18h) photoperiods. These are compared to the photoperiod proteomics dataset we generated.