Documents

Construct fof TOC1 for comparision with NanoLUC

Binary vector used for transforming with Agro ABI the elf3-2 CCA1p:LUC for rescuing elf3-2 mutation. In this particular case the plants selected were based on hypocotyl length. Based on the data of LUX in which we observed that rhytmicity was rescued in all lines, hypocotyl elongation varied between lines. Therefore, we used hypocotyl length for assesing complementation

Donor plasmid for Gatway cloning

https://benchling.com/s/02xiS4zk?m=slm-xxhynNMU6VkdMdqdfpFr

Donor plasmid for Gatway cloning

https://benchling.com/s/3OtoUun0?m=slm-pL4lmqV9bCWOW3WvoSFL

Binary vector used for transforming with Agro ABI the cca1-1/lhy-1p:LUC for rescuing cca1-1 mutation.

https://benchling.com/s/Zu0nJONs?m=slm-5zxnih8JIOujB9nYIG8E

Binary vector used for transforming with Agro ABI the cca1-11/lhy-1 CCA1p:LUC for rescuing lhy-1 mutation.

https://benchling.com/s/GP25kROo?m=slm-ocCyiEfwuTW5mJsbNQGn

Binary vector used for transforming with Agro ABI the cca1-1/lhy-1p:LUC for rescuing lhy-11 mutation. This was an alternative to NanoLUC and also for testing the behaviour of LHY.

Binary vector used for transforming with Agro ABI the prr9/7-9 CCR2:LUC for rescuing prr7-9 mutation.

Binary vector for transformation with Agro. This construct was intended for comparision with NanoLUC

Binary vector used for transforming with Agro ABI the toc1-2 CCA1p:LUC for rescuing the toc1-2 mutant.

List of python packages for reproducing the modelling and data analysis results

Donor plasmid for Gatway cloning for CCA1 genomic region

https://benchling.com/s/HcUdo0bf?m=slm-v3TyQ8AupdMj0SCqtZ2J

Donor plasmid for Gatway cloning

https://benchling.com/s/PpY2tH3D?m=slm-pRd1S0YhNNVNUhMkT4N1

Donor plasmid for Gatway cloning

https://benchling.com/s/g08AhXBj?m=slm-bGbYOjeh3kiwQQUdokaH

Jupyter notebook that contains the linear regression for inferring numnber of molecules from NanoLUC biolumiescent data in plant extracts using as calibration curve recombinant MBP-NanoLUC-3FLAG-10His

Documents the model paramter rescaling and set the scaling factors to 1

The promoter regions for clock genes that present a ChIP-seq signal were extracted from TAIR10 using costume python scripts using the gene list for Kamioka et al CCA1 or Daphne Ezer et al for LUX. The promoter was considered from the TSS of the gene until the annotated end of the upstream gene. Then, this region was scanned using the Energy Matrix derived using EMA working as a classifier for bound or unbound. After classification the calibrated PBM data calibrated using in vitro data was used ...

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

With this file the user can type

docker-compose up

and will be able to run the operating system were the modelling and analysis took place

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