Translational coincidence modelling - python scripts Version 1
SEEK ID: https://fairdomhub.org/models/298?version=1
2 items are associated with this Model:-
plot_translational_coincidence_model_predictions.py (Python script - 5.89 KB)
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seatongraf_utility_functions.py (Python script - 1.4 KB)
Organism: Arabidopsis thaliana
Model type: Algebraic equations
Model format: Not specified
Execution or visualisation environment: Not specified
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Views: 3569 Downloads: 156
Created: 4th Dec 2017 at 21:42
Last updated: 20th Feb 2018 at 22:03
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Version 1 (earliest) Created 4th Dec 2017 at 21:42 by Daniel Seaton
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https://orcid.org/0000-0002-5222-3893
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).
Projects: Millar group, PHYTOCAL: Phytochrome Control of Resource Allocation and Growth in Arabidopsis and in Brassicaceae crops, TiMet, POP - the Parameter Optimisation Problem, Regulation of flowering time in natural conditions, PlaSMo model repository
Web page: http://www.synthsys.ed.ac.uk
Andrew Millar's research group, University of Edinburgh
Programme: SynthSys
Public web page: http://www.amillar.org
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.
Submitter: Andrew Millar
Studies: Modelling and analysis of translational coincidence, Photoperiod-specific proteome data for Arabidopsis, Proteome and translation rate data for the Ostreococcus alga and for cya..., Rhythmic and photoperiod-specific transcriptome datasets for Arabidopsis
Assays: Aryal et al, 2011, metabolic labelling of Cyanothece protein synthesis, Blasing et al, 2005, diurnal microarray in 12L:12D, Estimation of rates of translation and turnover from proteomics datasets, Martin et al, 2012, Ostreococcus N15 labelling proteomics data, Photoperiod proteomics, Stitt lab, TiMet photoperiod microarrays, Translational coincidence model
Data analysis and modelling scripts and results for the Seaton et al. 2017 study, from Daniel Seaton.
Submitter: Andrew Millar
Investigation: Photoperiodic control of the Arabidopsis proteo...
Assays: Translational coincidence model
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.
Submitter: Daniel Seaton
Biological problem addressed: Model Analysis Type
Investigation: Photoperiodic control of the Arabidopsis proteo...
