def run():
reactants0 = np.array([[1, 1]])
products0 = np.array([[0, 2]])
reactants1 = np.array([[1, 0]])
products1 = np.array([[0, 1]])
x0 = np.array([40, 3])
t_max = 0.1
k0 = np.array([2.3])
k1 = np.array([30.0])
nr = 5
output = ssa.output.FullOutput()
model0 = ssa.Model(reactants0, products0, x0, t_max, k0, output, n_procs=2)
result0 = model0.simulate(n_reps=nr)
_, axes = plt.subplots(1, 2)
ssa.plot(result0, show=False, ax=axes[0])
model1 = ssa.Model(reactants1, products1, x0, t_max, k1, output, n_procs=2)
result1 = model1.simulate(n_reps=nr)
ssa.plot(result1, show=False, ax=axes[1])
plt.savefig("ex3.png")
def run():
reactants = np.array([[1, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]])
products = np.array([[0, 0, 1, 0], [1, 1, 0, 0], [0, 1, 0, 1]])
volume = 1e-15
x0 = np.array([5e-7, 2e-7, 0, 0])
x0 = ssa.util.molar_concentration_to_molecule_number(x0, volume=volume)
k = np.array([1e6, 1e-4, 0.1])
k = ssa.util.k_det_to_k_stoch(k, reactants=reactants, volume=volume)
print(x0, k)
t_max = 50
model = ssa.Model(reactants=reactants,
products=products,
x0=x0,
k=k,
t_max=t_max)
result = model.simulate(n_reps=20)
x_names = ["S", "E", "SE", "P"]
ssa.plot(result, x_names=x_names, show=False)
plt.savefig("michaelis_menten.png")
def run():
reactants = np.array([[2, 0], [0, 1]])
products = np.array([[0, 1], [2, 0]])
volume = 1e-15
use_na = True
k_det = np.array([5e5, 0.2])
k1 = ssa.util.k_det_to_k_stoch(
k_det, reactants=reactants, volume=volume, use_na=use_na
)
k2 = np.array([1.66e-3, 0.2])
print(k1, k2)
x0_molar_concentration = np.array([5e-7, 0])
x01 = ssa.util.molar_concentration_to_molecule_number(
x0_molar_concentration, volume=volume, use_na=use_na
)
x02 = np.array([301.1, 0])
print(x01, x02)
t_max = 10.0
model = ssa.Model(
reactants=reactants, products=products, k=k2, x0=x02, t_max=t_max, n_procs=2
)
result = model.simulate(n_reps=5)
ssa.plot(result, show=False)
plt.savefig("dimerization_kinetics.png")
def run():
reactants = np.array([[0, 0], [1, 0], [1, 0], [0, 1]])
products = np.array([[1, 0], [1, 1], [0, 0], [0, 0]])
x0 = np.array([0, 0])
k = np.array([1.0, 1.0, 0.1, 0.04])
t_max = 2e2
timepoints = np.linspace(0, t_max, 1000)
# model = ssa.Model(reactants, products, x0, t_max, k, output = ssa.output.FullOutput(), n_procs=2)
model = ssa.Model(
reactants,
products,
x0,
t_max,
k,
output=ssa.output.ArrayOutput(timepoints),
n_procs=2,
)
result = model.simulate(n_reps=100)
for j in range(len(result.list_ts)):
pass
ssa.plot(result, show=False)
plt.savefig("ex0.png")
def run():
reactants = np.array([[1, 1, 0], [0, 1, 0]])
products = np.array([[0, 2, 0], [0, 0, 1]])
x0 = np.array([299, 1, 0])
t_max = 1.0
k = np.array([0.3, 5])
x_names = ["Susceptible", "Infected", "Recovered"]
model = ssa.Model(reactants, products, x0=x0, t_max=t_max, k=k)
result = model.simulate(n_reps=20)
ssa.plot(result, x_names=x_names, set_xlim_to_t_max=False, show=False)
plt.savefig("sir.png")
def run():
reactants = np.array([[0, 0], [1, 0], [0, 1]])
products = np.array([[1, 0], [0, 1], [0, 0]])
k = np.array([1.0, 0.1, 0.05])
x0 = np.array([100, 0])
t_max = 100
model = ssa.Model(reactants=reactants, products=products, k=k, x0=x0, t_max=t_max)
result = model.simulate(n_reps=20)
ssa.plot(result, show=False)
plt.savefig("mono_molecular_chain.png")
def run():
model = ssa.Model(reactants,
products,
x0,
t_max,
k,
output=ssa.output.FullOutput())
# model = ssa.Model(reactants, products, x0, t_max, k, output = ssa.output.ArrayOutput(timepoints))
result = model.simulate(n_reps=10)
for j in range(len(result.list_ts)):
# print(result.list_ts[j][-10:])
# print(result.list_xs[j][-10:])
pass
def model(p):
# anything (e.g. pd.DataSeries) -> OrderedDict
p = OrderedDict([('transcription', p.get('transcription', 10)),
('decay', p.get('decay', 0.1))])
k = np.array(list(p.values())) # order should be correct
ssa_model = ssa.Model(reactants=self.reactants,
products=self.products,
t_max=self.t_max,
x0=self.x0,
k=k,
output=self.output)
result = ssa_model.simulate(n_reps=1)
return {
't': result.list_ts[0],
'mrna': result.list_xs[0].flatten()
}
def model(p):
k = np.array([p['k1'], p['k2'], p['k3'], p['k4']])
k = np.exp(k)
ssa_model = ssa.Model(reactants=self.reactants,
products=self.products,
t_max=self.t_max,
x0=self.x0,
k=k,
output=self.output)
result = ssa_model.simulate(n_reps=1)
#import matplotlib.pyplot as plt
#plt.plot(result.list_xs)
res = {
't': result.list_ts[0],
'S1': result.list_xs[0][:, 0].flatten()
} # 'S2': result.list_xs[0][:, 1].flatten()}
print(res)
return res
def run():
reactants = np.array([[1, 0, 0], [0, 1, 0]])
products = np.array([[0, 1, 0], [0, 0, 1]])
x0 = np.array([100, 0, 0])
k = np.array([1.0, 1.0])
t_max = 10
timepoints = np.linspace(0, t_max, 1000)
model = ssa.Model(
reactants, products, x0, t_max, k, output=ssa.output.FullOutput(), n_procs=2
)
# model = ssa.Model(reactants, products, x0, t_max, k, output = ssa.output.ArrayOutput(timepoints), n_procs=1)
result = model.simulate(n_reps=10)
for j in range(len(result.list_ts)):
pass
ssa.plot(result, show=True, show_mean=True, show_std=True)
def run():
reactants = np.array([[0], [1]])
products = np.array([[1], [0]])
x_names = "mRNA"
t_max = 1e2
x0 = np.array([0])
k = np.array([10, 0.1])
model = ssa.Model(reactants=reactants,
products=products,
t_max=t_max,
x0=x0,
k=k)
result = model.simulate(n_reps=10)
ssa.plot(result)
plt.savefig("transcription.png")
def run():
reactants = np.array([[1, 0], [1, 1], [0, 1]])
products = np.array([[2, 0], [0, 2], [0, 0]])
x0 = np.array([50, 100])
k = np.array([1, 0.005, 0.6])
t_max = 30
output = ssa.output.FullOutput()
model = ssa.Model(reactants,
products,
x0=x0,
k=k,
t_max=t_max,
output=output,
n_procs=2)
result = model.simulate(n_reps=10)
ssa.plot(result, show=False)
plt.savefig("ex4.png")
def run():
reactants = np.array([[0, 0], [1, 0], [1, 0], [0, 1]])
products = np.array([[1, 0], [0, 0], [1, 1], [0, 0]])
t_max = 100
x0 = np.array([0, 0])
k = np.array([3, 0.1, 0.2, 0.05])
output = ssa.output.ArrayOutput(np.linspace(0, t_max, 100))
output = ssa.output.FullOutput()
model = ssa.Model(
reactants=reactants, products=products, t_max=t_max, x0=x0, k=k, output=output
)
result = model.simulate(n_reps=10)
x_names = ["mRNA", "Protein"]
ssa.plot(result, show=False, x_names=x_names)
plt.savefig("one_state_gene_expression.png")
def run():
# activation, deactivation, transcription-act,
# transcription-deact, translation, mRNA decay,
# protein decay
reactants = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0],
[1, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
])
products = np.array([
[0, 1, 0, 0],
[1, 0, 0, 0],
[0, 1, 1, 0],
[1, 0, 1, 0],
[0, 0, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
])
k = np.array([0.5, 0.5, 20, 0.0001, 15, 1, 2])
x0 = np.array([1, 0, 0, 0])
t_max = 30
output = ssa.output.ArrayOutput(np.linspace(0, t_max, 1000))
x_names = ["inactive promoter", "active promoter", "mRNA", "protein"]
model = ssa.Model(reactants=reactants,
products=products,
k=k,
x0=x0,
t_max=t_max,
output=output)
result = model.simulate(n_reps=1)
ssa.plot(result, x_names=x_names, show=False)
plt.savefig("two_state_gene_expression.png")
def run():
reactants = np.array([[0], [1]])
products = np.array([[1], [0]])
t_max = 120
x0 = np.array([0])
k = np.array([1, 0.05])
output = ssa.output.ArrayOutput(np.linspace(0, t_max, 100))
output = ssa.output.FullOutput()
model = ssa.Model(reactants=reactants,
products=products,
t_max=t_max,
x0=x0,
k=k,
output=output)
result = model.simulate(n_reps=10)
x_names = ["mRNA"]
ssa.plot(result, show=False, x_names=x_names)
plt.savefig("mrna_transcription.png")
