Jupyter notebook Predicting Protein Numbers

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

The dataset presents mathematical models of the gene regulatory network of the circadian clock, in the plant Arabidopsis thaliana. The work will be published as Urquiza-Garcia, Molina, Halliday and Millar, title "Abundant clock proteins point to missing molecular regulation in the plant circadian clock", in Molecular Systems Biology, 2025.

Starting from the U2019.3 and U2020.3 models, this project rescales parameters to match protein levels that were predicted using a simple model from the TiMet ...

This time series were obtained from the literature by perroming rough quantiftification from western blot images. In some cases the data was quantified by the authors and graphs were provided in the publications. In this case we used ImageJ or https://automeris.io/WebPlotDigitizer/

This files contains the predicions generated using a simple model of translation, described in the manuscript. This synthetic data was used to rescale U219.3 resulting in U2019.4 and U2020.3 into U2020.4. The .4 models are only resceled for the mass scale of protein and still present the dynamics of the .3 version

Parameters rescaled and scaling factors set to 1

This is the scaled version of U2020.4 in sbml file. It already contains the scaling factors

Paramters rescaled and scaling factors set to 1

Paramteres rescaled and scaling factors set to 1

Parameters rescaled and scaling factors set to 1

Derived from U2019.3 from Testing the inferred rate of dynamic, gene regulatory network in absolute units

Sbml version of U2019.4 with reacaling factors values already incoporated in the model. This was generated autmatically using tellurium python package

This file was derived from U2020.3 by introducing the scalig factors in the required locations in the model. This files is used then for numerically rescaling the model for matching synthetic protein data.

This describes how models were linked to in vitro data and then from there also linked to in-vivo data by detrending and rescaling in vivo data to match in vitro data for CCA1 and TOC1. The detrending was also derived by performing a long LD experiemnt fro servarl days and using the expression peaks of TOC1 to extract the trend in NLUC decay and plant growth.

equations in PDF

U2020.5 equations in PDF

The jupyter notebook contains the code that predicts the number of monomers of several clock proteins and rescales the U2019.4 model which was published https://doi.org/10.1093/insilicoplants/diab022

This file contains the scaling factors that can be used with U2019.4 that will match synthetic protein data generated with the simple translation model.

The jupyter notebook contains the code that predicts the number of monomers of several clock proteins and rescales the U2019.4 model which was published https://doi.org/10.1093/insilicoplants/diab022

PDF of U2019.4 equations

PDF of U2020.4 equations

Jupyter notebook file that describes how the models were finally linked to produce several plots were model predictions and data are compared

This scaling factors can be pluged into U2020.4. They were derived by numerically matching the synthetic protein data