ReDyMo is a Python-coded, stochastic dynamic model simulator that reproduces the DNA replication process of cellular organisms belonging to the kinetoplastida group. Initially, we focused on Trypanosoma brucei strain TREU927.
This simulator is capable of:
- Calculation of various relevant properties about the replication process, including S-phase duration, mean inter-origin distance between replication origin sites, the achieved replication percentage given a time limit, etc.
- Storage of information about each step of the simulation, such as the replication stage at each model simulation iteration and whether any collisions happened during a given iteration.
- Storage of information regarding the replication timing program, that is, for each base pair it is recorded at which simulation iteration it was replicated.
- Simulation of replication-transcription conflicts with a variety of input parameters like replication machinery speed and transcription frequency.
- Simulation of dormant origins firing.
To run ReDyMo, you only need a system with Python3 installed, which is done by default in most Linux distributions.
The system uses a simple SQLite database. Python already has plenty of functionalities to access and modify SQLite databases. However, if it is necessary to visualize the data in a more intuitive fashion, we recommend the usage of a third-party software such as SQLiteStudio.
In this version of ReDyMo, most parameters are mandatory and are listed below:
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--cells number_of_cells: Number of independent simulations to be made. number_of_cells is a positive integer.
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--dormant dormant_flag: Flag that either activates ('True') or disables ('False') the firing of dormant origins. dormant_value is a Boolean flag. It is noteworthy that the dormant origing firing does not work when constitutive origins are used (parameter --constitutive).
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--organism 'organism_name': Name of the parasite species, as saved in the database. 'organism_name' is a string (in space-separated names, use single quotation marks).
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--resources number_of_forks: Number of available forks for the replication process. number_of_forks is a positive integer.
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--speed speed_value: Velocity of each replication fork (in number of nucleotides per iteration). speed_value is a positive integer.
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--timeout timeout_value: Maximum allowed number of iterations of a simulation; if this value is reached, then a simulation is ended even if DNA replication is not completed yet.
The two optional parameters are:
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--constitutive range: When this parameter is provided, a DNA replication must use the set of constitutive origins within the database instead of the probability landscape. range is a positive integer, and specifies the range of nucleotides around each constitutive origin that can initiate replication.
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--period period_value: Period (in number of simulation iterations) between two consecutive activations (i.e. RNAP binding) of a transcription region. period_value is a positive integer. If this parameter is not set, then the simulation is carried out without transcription.
To run the program, the syntax of the main simulator program is the following one:
$ ./src/main.py --cells number_of_cells --dormant dormant_flag --organism 'organism_name' --resources number_of_forks --speed speed_value --timeout timeout_value [--constitutive range] [--period period_value]
The command above must be executed within the project main directory. For example, to run a simulation of 7 cells of T. brucei TREU927, with 10 forks, replisome speed of 65 bp/iteration, transcription period of 150 iterations between two transcription initiations, a timeout of one million iterations and with dormant origin firing, one must type at the project main directory:
$ ./src/main.py --cells 7 --organism 'Trypanosoma brucei brucei TREU927' --resources 10 --speed 65 --period 150 --timeout 1000000 --dormant True
The simulation results will be stored into a directory named output/True_10_150/, in which "output" is the outer directory name and the inner directory name of composed of the concatenation of the used parameter values for dormant origin firing, resources and period.
Another example: if one wants to simulate 30 cells of T. brucei TREU927, with 50 forks, replisome speed of 1 bp/iteration, no transcription, no dormant origin firing, using constitutive origins with a firing initiation range of 200 Kb, and the same timeout of the previous example:
$ ./src/main.py --cells 30 --organism 'Trypanosoma brucei brucei TREU927' --resources 50 --speed 1 --timeout 1000000 --dormant False --constitutive 200000
In this case, the simulation results will be stored into the directory output/False_50_0.
If more than one cell is simulated at once, then the results may be averaged through the usage of an aggregator script, whose syntax is the following:
$ ./script/cell_output_aggregator.py *output_directory* > *aggregation_file_and_path*
where output_directory is the output directory of the simulation and aggregation_file_and_path is both the path and name for the file containing the result of data aggregation. For example, to aggregate the aforementioned first example, one could just type:
$ ./script/cell_output_aggregator.py output/False_10_150 > output/aggregated.txt
If you have any bug report and/or want to contact for other subjects (e.g., to collaborate in this project), please do not hesitate to contact us!
Please, address your message to:
msreis at butantan dot gov dot br.