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This Notebook tests (and provides examples) of the different features in the bioscrape.lineages

  1. All Types of Volume Events are tested below




In [1]:



    


%matplotlib inline

from bioscrape.lineage import LineageModel
from bioscrape.types import Model
from bioscrape.lineage import LineageSSASimulator
from bioscrape.lineage import LineageCSimInterface
from bioscrape.lineage import LineageVolumeCellState
import numpy as np
import pylab as plt

ssa_simulator = LineageSSASimulator()
timepoints = np.linspace(0,50,100)

k = 1
d = .1
g = .5
rxn1 = (["A"], ["A", "X"], "massaction", {"k":k})
rxn2 = (["X"], [], "massaction", {"k":d})
rxns = [rxn1, rxn2]
LM1 = LineageModel(reactions = rxns, initialize_model = True, initial_condition_dict = {"X": 0, "A":2})
LM1.create_volume_event("linear volume", {"growth_rate":g}, "massaction", {"k":.1, "species":"X"}, print_out = True)
LM1.py_initialize()

print("Simulating Model 1")
stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM1)
stoch_sim_output = stoch_result.py_get_result()

X_ind = LM1.get_species_index('X')
volume = stoch_result.py_get_volume()


plt.figure()
plt.title("Linear Volume Event with unimolecular(X) massaction propensity")
plt.plot(timepoints, stoch_sim_output[:, X_ind], label = "X")
plt.plot(timepoints, volume, label = "Volume")
plt.legend()

LM2 = LineageModel(reactions = rxns, initialize_model = True, initial_condition_dict = {"X": 0, "A":2})
LM2.create_volume_event("multiplicative", {"growth_rate":g/10}, "massaction", {"k":.1, "species":"X"}, print_out = False)
LM2.py_initialize()

print("Simulating Model 2")
stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM2)
stoch_sim_output = stoch_result.py_get_result()

X_ind = LM2.get_species_index('X')
volume = stoch_result.py_get_volume()

plt.figure()
plt.title("Multiplicative Volume Event with unimolecular(X) massaction propensity")
plt.plot(timepoints, stoch_sim_output[:, X_ind], label = "X")
plt.plot(timepoints, volume, label = "Volume")
plt.legend()

LM3 = LineageModel(reactions = rxns, initialize_model = False, initial_condition_dict = {"X": 0, "A":2})
LM3.create_volume_event("general", {"equation": "volume+"+".01*(X-20)^2"}, "massaction", {"k":.1, "species":"X"})
LM3.py_initialize()

print("Simulating Model 3")
stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM1)
stoch_sim_output = stoch_result.py_get_result()

X_ind = LM3.get_species_index('X')
volume = stoch_result.py_get_volume()

plt.figure()
plt.title("GeneralVolumeEvent (v = v+.01*(X-20)^2) with a first order massaction hill function of X")
plt.plot(timepoints, stoch_sim_output[:, X_ind], label = "X")
plt.plot(timepoints, volume, label = "Volume")
plt.legend()



    


















    






Simulating Model 1
Simulating Model 2
Simulating Model 3










    






c:\users\apand\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:21: UserWarning: Creating New Volume event
	type=linear volume
	params={'growth_rate': 0.5}
	prop_type=massaction
	prop_params={'k': 0.1, 'species': 'X'}
c:\users\apand\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:21: UserWarning: Creating Propensity: prop_type=massaction params={'k': 0.1, 'species': 'X'}










    
Out[1]:








<matplotlib.legend.Legend at 0x1fa77a73cc0>
  1. All types of Volume Rules are test below




In [2]:



    


%matplotlib inline

from bioscrape.lineage import LineageModel
from bioscrape.types import Model
from bioscrape.lineage import LineageSSASimulator
from bioscrape.lineage import LineageCSimInterface
from bioscrape.lineage import LineageVolumeCellState
import numpy as np
import pylab as plt

ssa_simulator = LineageSSASimulator()
timepoints = np.linspace(0,50,500)

k = 1
d = .1
g = .5
rxn1 = (["A"], ["A", "X"], "massaction", {"k":k})
rxn2 = (["X"], [], "massaction", {"k":d})
rxns = [rxn1, rxn2]
LM1 = LineageModel(reactions = rxns, initialize_model = True, initial_condition_dict = {"X": 0, "A":2})
LM1.create_volume_rule("linear", {"growth_rate":g})
LM1.py_initialize()

print("Simulating Model 1")
stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM1)
stoch_sim_output = stoch_result.py_get_result()

X_ind = LM1.get_species_index('X')
volume = stoch_result.py_get_volume()


plt.figure()
plt.title("Linear Volume Rule")
plt.plot(timepoints, stoch_sim_output[:, X_ind], label = "X")
plt.plot(timepoints, volume, label = "Volume")
plt.legend()

LM2 = LineageModel(reactions = rxns, initialize_model = True, initial_condition_dict = {"X": 0, "A":2})
LM2.create_volume_rule("multiplicative", {"growth_rate":(g/10.)})
LM2.py_initialize()


print("Simulating Model 2")
stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM2)
stoch_sim_output = stoch_result.py_get_result()

X_ind = LM2.get_species_index('X')
volume = stoch_result.py_get_volume()

plt.figure()
plt.title("Multiplicative Volume Rule")
plt.plot(timepoints, stoch_sim_output[:, X_ind], label = "X")
plt.plot(timepoints, volume, label = "Volume")
plt.legend()

LM3 = LineageModel(reactions = rxns, initialize_model = False, initial_condition_dict = {"X": 0, "A":2})
LM3.create_volume_rule("assignment", {"equation": ".1*(X-20)^2"})
LM3.py_initialize()

print("Simulating Model 3")
stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM3)
stoch_sim_output = stoch_result.py_get_result()

X_ind = LM3.get_species_index('X')
volume = stoch_result.py_get_volume()

plt.figure()
plt.title("Assignment (v = .01*(X-20)^2)")
plt.plot(timepoints, stoch_sim_output[:, X_ind], label = "X")
plt.plot(timepoints, volume, label = "Volume")
plt.legend()


LM4 = LineageModel(reactions = rxns, initialize_model = False, initial_condition_dict = {"X": 0, "A":2})
LM4.create_volume_rule("ode", {"equation": ".025*(X-20)^2"})
LM4.py_initialize()


print("Simulating Model 4")
stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM4)
stoch_sim_output = stoch_result.py_get_result()

X_ind = LM4.get_species_index('X')
volume = stoch_result.py_get_volume()

plt.figure()
plt.title("GeneralVolumeRule (dv/dt = .025*(X-20)^2")
plt.plot(timepoints, stoch_sim_output[:, X_ind], label = "X")
plt.plot(timepoints, volume, label = "Volume")
plt.legend()



    


















    






Simulating Model 1
Simulating Model 2
Simulating Model 3
Simulating Model 4










    
Out[2]:








<matplotlib.legend.Legend at 0x1fa78c11b70>
  1. Death and Division Events (in a Single Cell Simulation) are tested below. When a death/division event occurs, the simulation should stop. These two events both use a positive hill function with the same underlying CRN, so the death and division plots should look similar.




In [3]:



    


%matplotlib inline

from bioscrape.lineage import LineageModel
from bioscrape.types import Model
from bioscrape.lineage import LineageSSASimulator
from bioscrape.lineage import LineageCSimInterface
from bioscrape.lineage import LineageVolumeCellState
from bioscrape.simulator import VolumeSplitter
import numpy as np
import pylab as plt

ssa_simulator = LineageSSASimulator()
timepoints = np.linspace(0,50,500)
plt.figure()
trials = 10
color_list = ["green", "blue", "cyan"]
k = 1
d = .1
g = .5
rxn1 = (["A"], ["A", "X"], "massaction", {"k":k})
rxn2 = (["X"], [], "massaction", {"k":d})
rxns = [rxn1, rxn2]

LM1 = LineageModel(reactions = rxns, initialize_model = True)
vs = VolumeSplitter()
LM1.create_division_event("division", {}, "hillpositive", {"s1":"X", "k":.5, "n":2, "K":15}, vs, print_out = True)
LM1.py_initialize()
    
for ind in range(3):
    A0 = [1, 2, 10][ind]
    initial_condition_dict = {"X": 0, "A":A0}
    LM1.set_species(initial_condition_dict)
    s = LineageCSimInterface(LM1)
    for trial in range(trials):
        #raise RuntimeError()

        stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM1)
        stoch_sim_output = stoch_result.py_get_result()

        X_ind = LM1.get_species_index('X')
        volume = stoch_result.py_get_volume()
        output_length = stoch_sim_output[:, X_ind].shape[0]
        
        if trial == 0:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], label = "X [A0="+str(A0)+"]", color = color_list[ind], alpha = .5)
        else:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], color = color_list[ind], alpha = .5)

plt.title("Division Rule with a Hill Propensity (species = X, n = 2, K = 5)")
plt.legend()


LM2 = LineageModel(reactions = rxns, initialize_model = True)
vs = VolumeSplitter()
LM2.create_death_event("death", {}, "hillpositive", {"s1":"X", "k":.5, "n":2, "K":15})
LM2.py_initialize()
plt.figure()
for ind in range(3):
    A0 = [1, 2, 10][ind]
    initial_condition_dict = {"X": 0, "A":A0}
    LM2.set_species(initial_condition_dict)
    s = LineageCSimInterface(LM2)
    for trial in range(trials):
        s.py_set_initial_time(0)
        v = LineageVolumeCellState(v0 = 1, t0 = 0)
        stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM2)
        stoch_sim_output = stoch_result.py_get_result()

        X_ind = LM2.get_species_index('X')
        volume = stoch_result.py_get_volume()
        output_length = stoch_sim_output[:, X_ind].shape[0]
        
        if trial == 0:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], label = "X [A0="+str(A0)+"]", color = color_list[ind], alpha = .5)
        else:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], color = color_list[ind], alpha = .5)

plt.title("Death Rule with a Hill Propensity (species = X, n = 2, K = 5)")
plt.legend()



    


















    






('Adding New DivisionEvent with event_type=', 'division', 'params=', {}, 'propensity_type=', 'hillpositive', 'propensity_params=', {'s1': 'X', 'k': 0.5, 'n': 2, 'K': 15}, 'and VolumeSplitter=', <bioscrape.simulator.VolumeSplitter object at 0x000001FA78C638A0>)










    






c:\users\apand\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:24: UserWarning: The following species are uninitialized and their value has been defaulted to 0: A, X, 
c:\users\apand\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:26: UserWarning: Creating Propensity: prop_type=hillpositive params={'s1': 'X', 'k': 0.5, 'n': 2, 'K': 15}
c:\users\apand\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:53: UserWarning: The following species are uninitialized and their value has been defaulted to 0: A, X, 










    
Out[3]:








<matplotlib.legend.Legend at 0x1fa78dcecf8>

Test all Division Rule Types





In [4]:



    


%matplotlib inline

from bioscrape.lineage import LineageModel
from bioscrape.types import Model
from bioscrape.lineage import LineageSSASimulator
from bioscrape.lineage import LineageCSimInterface
from bioscrape.lineage import LineageVolumeCellState
from bioscrape.simulator import VolumeSplitter
import numpy as np
import pylab as plt

ssa_simulator = LineageSSASimulator()
timepoints = np.linspace(0,50,500)
plt.figure()
trials = 10
color_list = ["green", "blue", "cyan", "purple"]
k = 1
d = .1
g = 2.0
rxn1 = (["A"], ["A", "X"], "massaction", {"k":k})
rxn2 = (["X"], [], "massaction", {"k":d})
rxns = [rxn1, rxn2]



noise = .25
g = .5
rules = [("time", {"threshold":5, "noise":noise}), 
 ('volume', {"threshold":5, "noise":noise}), 
 ("deltaV", {"threshold":5, "noise":noise}), 
 ("general",{"equation":"-(X-20)^2+5"})]
for ind in range(4):
    rule_type, rule_dict = rules[ind]
    plt.figure()
    plt.title("Division type: "+str(rule_type)+" params="+str(rule_dict))
    LM1 = LineageModel(reactions = rxns, initialize_model = True, initial_condition_dict = {"X": 0, "A":2})
    vs = VolumeSplitter()
    LM1.create_volume_rule("linear", {"growth_rate":g, 'noise':noise*g*10})
    LM1.create_division_rule(rule_type, rule_dict, vs)
    LM1.py_initialize()
    s = LineageCSimInterface(LM1)
    for trial in range(trials):
        s.py_set_initial_time(0)
        v = LineageVolumeCellState(v0 = 1, t0 = 0)
        
        stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM1)
        stoch_sim_output = stoch_result.py_get_result()

        X_ind = LM1.get_species_index('X')
        volume = stoch_result.py_get_volume()
        output_length = stoch_sim_output[:, X_ind].shape[0]
        
        if trial == 0:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], label = "X", color = color_list[ind], alpha = .5)
            plt.plot(timepoints[:output_length], volume,":", label = "vol", color = color_list[ind], alpha = .5)

        else:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], color = color_list[ind], alpha = .5)
            plt.plot(timepoints[:output_length], volume, ":", color = color_list[ind], alpha = .5)
    plt.legend()

plt.legend()



    


















    
Out[4]:








<matplotlib.legend.Legend at 0x1fa78ce2cf8>










    








<Figure size 432x288 with 0 Axes>

Testing All Death Rule Types





In [5]:



    


%matplotlib inline

from bioscrape.lineage import LineageModel
from bioscrape.types import Model
from bioscrape.lineage import LineageSSASimulator
from bioscrape.lineage import LineageCSimInterface
from bioscrape.lineage import LineageVolumeCellState
from bioscrape.simulator import VolumeSplitter
import numpy as np
import pylab as plt

ssa_simulator = LineageSSASimulator()
timepoints = np.linspace(0,100,1000)
plt.figure()
trials = 10
color_list = ["green", "blue", "cyan", "purple"]
k = 1
d = .1
g = 2.0
rxn1 = (["A"], ["A", "X"], "massaction", {"k":k})
rxn2 = (["X"], [], "massaction", {"k":d})
rxns = [rxn1, rxn2]



noise = .1
g = .5
rules = [("species", {"specie":"X", "threshold":20, "noise":noise}), 
 ('param', {"param":"testParam", "threshold":20, "noise":noise}), 
 ("general",{"equation":"-(X-20)^2+5"})]
for ind in range(3):
    rule_type, rule_dict = rules[ind]
    plt.figure()
    plt.title("Death type: "+str(rule_type)+" params="+str(rule_dict))
    LM1 = LineageModel(reactions = rxns, initialize_model = True, initial_condition_dict = {"X": 0, "A":2}, parameters = [("testParam", 17)])
    LM1.create_volume_rule("linear", {"growth_rate":g, 'noise':noise*g*10})
    LM1.create_death_rule(rule_type, rule_dict)
    LM1.py_initialize()
    for trial in range(trials):

        stoch_result = ssa_simulator.py_SimulateSingleCell(timepoints, Model = LM1)
        stoch_sim_output = stoch_result.py_get_result()

        X_ind = LM1.get_species_index('X')
        volume = stoch_result.py_get_volume()
        output_length = stoch_sim_output[:, X_ind].shape[0]
        
        if trial == 0:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], label = "X", color = color_list[ind], alpha = .5)
            plt.plot(timepoints[:output_length], volume,":", label = "vol", color = color_list[ind], alpha = .5)

        else:
            plt.plot(timepoints[:output_length], stoch_sim_output[:, X_ind], color = color_list[ind], alpha = .5)
            plt.plot(timepoints[:output_length], volume, ":", color = color_list[ind], alpha = .5)
    plt.legend()

plt.legend()



    


















    






c:\users\apand\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:37: UserWarning: No comparison time added for SpeciesDeathRule in param dictionary. Defaulting to >.
c:\users\apand\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:37: UserWarning: No comparison time added for ParamDeathRule in param dictionary. Defaulting to >.










    
Out[5]:








<matplotlib.legend.Legend at 0x1fa78c194e0>










    








<Figure size 432x288 with 0 Axes>










    

















    

















    
























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Content source: ananswam/bioscrape

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