ToyMaker 🤖

This library is designed to facilitate research of stochasticity effects in genetic circuits.

Installation

ToyMaker can be installed using pip:

    pip install ToyMaker

Usage:

ToyMaker uses 6 types of parameters: k_values, propensities, species, reactions and simulation_parameters.

• k_values:

  This parameter is for setting the constants that you're going to use in your model.

• propensities:

  This parameter is used to set the propensities calculation equations.

• species:

  This parameter is to set the species that are involved in the system.

• reactions:

  This parameter is to set what kind of reaction occurs (create, destroy).

• simulation_parameters:

  This parameter is used to set maximum simulation time (tmax), lenght per step (sampling_time), and population ammount (cells) to run the simulation.

• Setting up the model:

  Once the model is stablished it's time to set up the model by using:

    model = tmk.ToyModel(toy_name='My_first_model')
    model.setup(species, reactions, propensities, k_values, simulation_parameters)
  

  This generates a Pythonscript with your implemented system, take into account that the generated python file name is the toy_name parameter in ToyModel. Finally, you can use your generated file in a Jupyter Notebook.

• Example:

   from ToyMaker.Assembler import ToyModel
  
   k_values = {
               'kr' : 5,
               'kp' : 10,
               'gamma_r' : 1/5,
               'gamma_p' : 0.4
               }
  
   propensities = {
                   'R1': 'kr',
                   'R2': 'kp * r',
                   'R3': 'gamma_r * r',
                   'R4': 'gamma_p * p',
                   }
  
   species = {
           'r':    0.,
           'p':    0.
               }
  
   reactions = {
  
       'R1':  {'create':  ['r']},
       'R2':  {'create':  ['p']},
       'R3':  {'destroy': ['r']},
       'R4':  {'destroy': ['p']}
               }
  
   simulation_parameters = {
       'tmax': 300.,
       'sampling_time': 1.,
       'cells': 1000.
   }
  
   model = ToyModel(toy_name='My_first_model')
   model.setup(species, reactions, propensities, k_values, simulation_parameters)
   }
  

• Running the model

  Once My_first_model is setted up, it's time to create a jupyter notebook to import the dynamic library created. Then you can use the "runpopulation" method

    import My_first_model as model
    import numpy as np
    import matplotlib.pyplot as plt
  
    simulation = np.array(model.run_population(
        tmax = 300, 
        cells=1000, 
        kr=5, 
        gamma_r=1/5, 
        kp=10, 
        gamma_p=0.4))
  

  Notice that it's possible to change constants values like kr, gamma_r, kp or gamma_p.

• Data analysis

  For data analysis it's suggested to convert output of model.runpopulation() to an np.array() to visualize data by using:

    fig, ax = plt.subplots(figsize = (7, 4))
    plt.plot(simulation[:].T[2,:], lw = 0.1, color='red', alpha=0.2) # Multiple Cells
    plt.plot(np.mean(simulation[:].T[2,:], axis=1), color='black') # Mean
    plt.xlabel('Time (a.u.)')
    plt.ylabel('RNA')