twinTrans

Python implementation of the twin transcriptional-loop model using a Gillespie algorithm

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Usage

The code has been tested using Python 3.7.

Basic

The most basic usage consists in running:

bin/twin.py {results_directory}

This will run a simulation with default parameters (lasting $\sim$ 2 min on a 3.1 GHz Intel Core i7). The outcome is composed of two files written out in {results_directory}:

• param_var.txt: parameters and variables (and their value) of the simulation
• mean_properties.txt: values of various quantities of interest (written out every a fixed number of transcripts as specified by -Net optional argument):
  • transcripts_nb: number of transcripts
  • time: real time ($s$)
  • prod_rate: production rate (= transcripts_nb/time) ($s^{-1}$)
  • mean_prod_time: average time separating two successive productions of a transcript (should be equal to 1/prod_rate) ($s$)
  • mean_bind_time: average time separating two successive binding events ($s$)
  • mean_ocf_time: average time to form the open complex once the RNAP is bound at the promoter ($s$)
  • mean_esc_time: average time to escape the promoter once the open complex is formed ($s$)
  • mean_init_time: average time between two successive initiations of elongation ($s$)
  • mean_elong_time: average elongation time ($s$)

To specificy parameters such as those associated with the promoter ($k_b$, $k_o$, $\sigma_o$ and $k_e$), run:

bin/twin.py -h

Promoter-following mode

To follow the topological properties at the promoter, use the option -promfollow