In [1]:

    

import numpy as np
import pylab as plt
from bioscrape.simulator import *
from bioscrape.types import *
import warnings


#Parameter ranges to randomly choose parameters (on a log scale)
param_min = -2
param_max = 2

#Names of different supported propensities
propensity_types = ['massaction', 'hillpositive', 'proportionalhillpositive', 'hillnegative', 'proportionalhillnegative', 'massaction']#, 'general']

#parameter names required for each propensity (general will be treated by itself)
propensity_param_requirements = {
	'massaction':['k'], 'hillpositive':['k', 'K', 'n'], 'hillnegative':['k', 'K', 'n'],
	'proportionalhillpositive':["k", "K", "n"], 'proportionalhillnegative':["k", "K", "n"]
}
#species (passed in as parameters) requires for each propensity (general will be treated by itself)
propensity_specie_requirements = {
	'hillpositive':['s1'], 'hillnegative':['s1'], 'proportionalhillpositive':['s1', 'd'], 'proportionalhillnegative':['s1', 'd'], "massaction":[]
}


species = ['A', 'B', 'C', 'D', 'E', 'F', 'G']
n_species = len(species)
n_reactions = np.random.randint(1, 3)

reactions = []
for r in range(n_reactions):

    try_again = True
    while try_again:#Loop ensures no positive feedback which leads to long simulations
        inputs = []
        outputs = []
        while(len(inputs) == 0 and len(outputs) == 0):

            n_inputs = np.random.randint(0, 5)
            for i in range(n_inputs):
                inputs.append(species[np.random.randint(0, len(species))])

            n_outputs = np.random.randint(0, 5)
            for i in range(n_outputs):
                outputs.append(species[np.random.randint(0, len(species))])

        inputs_in_outputs = len([i for i in inputs if i in outputs])
        if inputs_in_outputs >= len(inputs):
            try_again = True
        else:
            try_again = False

    prop_type = propensity_types[np.random.randint(0, len(propensity_types))]
    param_dict = {}
    if prop_type != 'general':
        required_params = propensity_param_requirements[prop_type]
        required_species = propensity_specie_requirements[prop_type]
        param_dict = {}
        for p in required_params:
            param_dict[p] = round(np.exp(np.random.uniform(low = param_min, high = param_max)), 3)
        for i in range(len(required_species)):
            k = required_species[i]
            param_dict[k] = species[np.random.randint(0, len(species))]

    elif prop_type == 'general': #Here we will use a random(ish) rational function
        rate_str = "(1+"
        numerator_terms = np.random.randint(0, 5)
        denominator_terms = np.random.randint(0, 5)
        for i in range(numerator_terms):
            coef = str(round(np.exp(np.random.uniform(low = param_min, high = param_max)), 3))
            exp = str(round(np.random.uniform(low = 0, high = param_max), 3))
            specie = species[np.random.randint(0, len(species))]
            rate_str += coef+"*"+specie+"^"+exp+"+"
        rate_str =  rate_str[:-1] + ")"
        rate_str += "/(1+"
        for i in range(denominator_terms):
            coef =str(round(np.exp(np.random.uniform(low = param_min, high = param_max)), 3))
            exp = str(round(np.random.uniform(low = 0, high = param_max), 3))
            specie = species[np.random.randint(0, len(species))]
            rate_str += coef+"*"+specie+"^"+exp+"+"
        rate_str =  rate_str[:-1] + ")"
        param_dict['rate'] = rate_str

    rxn = (inputs, outputs, prop_type, param_dict)
    reactions.append(rxn)


print("Bioscrape model #rxns=", len(reactions), "\nrxns=", reactions)
M = Model(reactions = reactions, initial_condition_dict = {s:np.random.randint(10, 100) for s in species})
M.write_bioscrape_xml('models/random_model.xml')
M2 = Model('models/random_model.xml')
M.write_sbml_model('models/random_model_sbml.xml')
M3 = Model(sbml_filename = 'models/random_model_sbml.xml')


    

    




Bioscrape model #rxns= 2 
rxns= [(['E', 'D'], [], 'proportionalhillnegative', {'k': 0.312, 'K': 0.586, 'n': 2.511, 's1': 'C', 'd': 'C'}), (['D', 'C', 'B'], ['C', 'D'], 'massaction', {'k': 0.372})]
Warning! SpeciesA not currently used in any rules or reactions.
Warning! SpeciesF not currently used in any rules or reactions.
Warning! SpeciesG not currently used in any rules or reactions.






    




C:\Users\apand\Anaconda3\lib\site-packages\bioscrape\sbmlutil.py:185: UserWarning: Compartments, UnitDefintions, Events, and some other SBML model components are not recognized by bioscrape. Refer to the bioscrape wiki for more information.
  warnings.warn('Compartments, UnitDefintions, Events, and some other SBML model components are not recognized by bioscrape. ' +

In [2]:

    

timepoints = np.arange(0, 1., .02)
results_1 = py_simulate_model(timepoints, Model = M, stochastic = False, safe = False)
print("first simulation successful")

results_2 = py_simulate_model(timepoints, Model = M2, stochastic = False, safe = False)
print("second simulation successful")

results_3 = py_simulate_model(timepoints, Model = M2, stochastic = False, safe = False)
print("third simulation successful")

plt.figure(figsize = (16, 6))
for s in species:
    plt.plot(timepoints, results_1[s], "-", label = "Model 1: "+s)
    plt.plot(timepoints, results_2[s], "o", label = "Model 2: "+s)
    plt.plot(timepoints, results_3[s], 'x', ms = 10, label = "Model 3: "+s)
plt.legend()
plt.show()


    

    




first simulation successful
second simulation successful
third simulation successful






    

In [3]:

    

from bioscrape.types import *
from bioscrape.sbmlutil import import_sbml


    

In [4]:

    

M = import_sbml('models/GFP.xml')


    

    




C:\Users\apand\Anaconda3\lib\site-packages\bioscrape\sbmlutil.py:89: UserWarning: SBML model contains reversible reaction!
Please check rate expressions and ensure they are non-negative before doing stochastic simulations.
  'Please check rate expressions and ensure they are non-negative before doing '+
C:\Users\apand\Anaconda3\lib\site-packages\bioscrape\sbmlutil.py:185: UserWarning: Compartments, UnitDefintions, Events, and some other SBML model components are not recognized by bioscrape. Refer to the bioscrape wiki for more information.
  warnings.warn('Compartments, UnitDefintions, Events, and some other SBML model components are not recognized by bioscrape. ' +

In [5]:

    

from bioscrape.simulator import py_simulate_model
timepoints = np.linspace(0,10000,100)

results = py_simulate_model(timepoints, Model = M)


    

    




C:\Users\apand\Anaconda3\lib\site-packages\scipy\integrate\odepack.py:247: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.
  warnings.warn(warning_msg, ODEintWarning)
odeint failed with mxstep=500...

In [6]:

    

%matplotlib inline 
import matplotlib.pyplot as plt
plt.plot(timepoints, results["mw6600ba7f_5b6d_4e5d_89a2_4d798f44476d"])


    

    
Out[6]:





[<matplotlib.lines.Line2D at 0x1c25b1a58c8>]






    

In [8]:

    




    

In [ ]: