def test_run_simulation():
run_simulation(model)
simulated_value = np.load(
os.path.join(
model.path,
"simulation_data",
"simulations_original.npy",
))
assert np.isfinite(simulated_value).all()
def test_example_plot():
assert run_simulation(model, viz_type="original") is None
res = np.load(
os.path.join(model.path, "simulation_data",
"simulations_original.npy"))
fig = plt.figure(figsize=(9, 9))
plt.rcParams["font.family"] = "Arial"
plt.rcParams["font.size"] = 18
plt.rcParams["axes.linewidth"] = 2
plt.rcParams["lines.linewidth"] = 3
plt.rcParams["lines.markersize"] = 16
plt.subplots_adjust(wspace=0, hspace=0.3)
for i, obs_name in enumerate(model.observables):
plt.subplot(2, 1, i + 1)
for j, (color,
label) in enumerate(zip(["k", "r"], ["TNFα", "TNFα + DCF"])):
plt.plot(model.problem.t, res[i, :, j], color=color, label=label)
plt.title(f"{obs_name}".replace("_", " "))
plt.xticks([0, 50, 100, 150, 200])
if i == 0:
plt.yticks([0, 0.05, 0.10, 0.15])
plt.legend(loc="upper left", frameon=False)
fig.text(0.5, 0.05, "time [min]", ha="center")
fig.text(0.0,
0.5,
"concentration [a.u.]",
va="center",
rotation="vertical")
plt.show()
def test_example_plot():
assert run_simulation(model) is None
res = np.load(
os.path.join(model.path, "simulation_data",
"simulations_original.npy"))
plt.figure(figsize=(8, 8))
plt.rcParams["font.family"] = "Arial"
plt.rcParams["xtick.direction"] = "in"
plt.rcParams["ytick.direction"] = "in"
plt.rcParams["font.size"] = 18
plt.rcParams["lines.linewidth"] = 4
plt.rcParams["lines.markersize"] = 16
plt.subplots_adjust(wspace=0.25, hspace=0.3)
for i, obs_name in enumerate(model.observables):
plt.subplot(2, 2, i + 1)
for j, color in enumerate([
"darkblue", "cornflowerblue", "yellowgreen", "goldenrod",
"brown"
]):
plt.plot(model.problem.t, res[i, :, j], color=color)
plt.xlim(0, 480)
plt.xticks([0, 120, 240, 360, 480], fontweight="bold")
plt.ylim(0, 1)
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0], fontweight="bold")
plt.title(f"{obs_name}", fontweight="bold")
if i % 2 == 0:
plt.ylabel("Intensity (A.U.)", fontweight="bold")
if i > 1:
plt.xlabel(" time (min)",
fontweight="bold")
plt.show()
def test_external_optimizer():
optimizer = ExternalOptimizer(model, differential_evolution)
res = optimizer.run(
model.problem.objective,
model.problem.bounds,
strategy="best2bin",
maxiter=5,
popsize=3,
tol=1e-4,
mutation=0.1,
recombination=0.5,
polish=False,
workers=-1,
)
assert isinstance(res, OptimizeResult)
optimizer.import_solution(res.x, x_id=0)
n_iter: int = 0
with open(
os.path.join(model.path, "out", "0", "optimization.log"),
mode="r",
encoding="utf-8",
) as f:
log_file = f.readlines()
for message in log_file:
if len(message.strip()) > 0:
n_iter += 1
for fname in [
"count_num.npy",
"generation.npy",
f"fit_param{n_iter}.npy",
]:
assert os.path.isfile(os.path.join(model.path, "out", "0", f"{fname}"))
assert run_simulation(model, viz_type="0") is None
assert os.path.isdir(os.path.join(model.path, "figure", "simulation", "0"))
def test_run_simulation():
with pytest.warns(RuntimeWarning) as record:
run_simulation(model, viz_type="original")
# check that the message matches
assert record[-1].message.args[0] in [
"Simulation failed. #original",
"invalid value encountered in true_divide",
]
run_simulation(model, viz_type="average", stdev=True)
assert os.path.isfile(
os.path.join(
model.path,
"simulation_data",
"simulations_all.npy",
))
for obs_name in model.observables:
assert os.path.isfile(
os.path.join(model.path, "figure", "simulation", "average",
f"{obs_name}.png"))
def test_run_simulation():
assert run_simulation(model) is None
res = np.load(
os.path.join(model.path, "simulation_data",
"simulations_original.npy"))
plt.figure(figsize=(10, 10))
plt.rcParams["font.size"] = 14
plt.rcParams["font.family"] = "Arial"
plt.rcParams["mathtext.fontset"] = "custom"
plt.rcParams["mathtext.it"] = "Arial:italic"
plt.rcParams["axes.linewidth"] = 2
plt.rcParams["xtick.major.width"] = 2
plt.rcParams["ytick.major.width"] = 2
plt.rcParams["lines.linewidth"] = 2
plt.subplots_adjust(wspace=0.3, hspace=0.8)
yticks = {
"Ski": [0, 1, 2],
"Skil": [0, 1, 2, 3],
"Dnmt3a": [0, 0.5, 1, 1.5],
"Sox4": [0, 1, 2],
"Jun": [0, 1, 2, 3],
"Smad7": [0, 1, 2, 3],
"Klf10": [0, 2, 4],
"Bmp4": [0, 1, 2],
"Cxcl15": [-1.5, -1, -0.5, 0, 0.5],
"Dusp5": [-1.5, -1, -0.5, 0],
"Tgfa": [-1.5, -1, -0.5, 0, 0.5],
"Pdk4": [-3, -2, -1, 0],
}
for i, gene_name in enumerate(model.observables):
plt.subplot(4, 3, i + 1)
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
for j, color in enumerate(["brown", "royalblue", "darkgreen", "k"]):
plt.plot(model.problem.t, res[i, :, j], color=color, linewidth=1.5)
plt.title(fr"$\it{gene_name}$")
plt.yticks(yticks[gene_name])
if i >= 9:
plt.xlabel("time (min)")
plt.xticks([0, 200, 400, 600])
if i % 3 == 0:
plt.ylabel("gene expression(log2)")
plt.show()
def test_example_plot():
assert run_simulation(model, viz_type="original") is None
res = np.load(
os.path.join(model.path, "simulation_data",
"simulations_original.npy"))
plt.rcParams["font.size"] = 16
fig, ax1 = plt.subplots(figsize=(6, 4))
ax2 = ax1.twinx()
ax1.plot(model.problem.t, res[model.observables.index("Cry_mRNA"), :], "c")
ax1.plot(model.problem.t, res[model.observables.index("Per_mRNA"), :], "m")
ax1.set_xlim([0, 72])
ax1.set_xticks([0, 12, 24, 36, 48, 60, 72])
ax1.set_xlabel("Time (h)")
ax1.set_ylim([0, 5])
ax1.set_ylabel(r"$\it{Per}$" + " " + r"$\sf{(M_P)}$" + " and " +
r"$\it{Cry}$" + " " + r"$\sf{(M_C)}$" + "\nmRNAs, nM")
ax2.plot(model.problem.t, res[model.observables.index("Bmal1_mRNA"), :],
"y")
ax2.set_ylim([7, 10])
ax2.set_yticks([7, 8, 9, 10])
ax2.set_ylabel(r"$\it{Bmal1}$" + " mRNA " + r"$\sf{(M_B)}$" + ", nM")
plt.show()
def test_run_simulation():
run_simulation(model, viz_type="original")
run_simulation(model, viz_type="1")
run_simulation(model, viz_type="2")
run_simulation(model, viz_type="3")
run_simulation(model, viz_type="average", stdev=True)
run_simulation(model, viz_type="best", show_all=True)
for npy_file in [
"simulations_original.npy",
"simulations_1.npy",
"simulations_2.npy",
"simulations_3.npy",
"simulations_all.npy",
"simulations_best.npy",
]:
simulated_value = np.load(os.path.join(model.path, "simulation_data", npy_file))
assert np.isfinite(simulated_value).all()
for viz_type in ["original", "1", "2", "3", "average", "best"]:
for obs_name in model.observables:
assert os.path.isfile(
os.path.join(model.path, "figure", "simulation", f"{viz_type}", f"{obs_name}.png")
)
def test_run_simulation():
run_simulation(mapk_cascade, viz_type='original')
run_simulation(mapk_cascade, viz_type='average', stdev=True)
run_simulation(mapk_cascade, viz_type='best', show_all=True)
#Model construction
import Hass_2017
Hass_2017.show_info()
model = Hass_2017.create()
#Parameter estimation
from biomass import optimize
# Estimate 10 parameter sets simultaneously
optimize(model=model,
start=1,
end=10,
options={
"popsize": 5,
"max_generation": 1000,
"allowable_error": 0.5,
"local_search_method": "mutation",
"n_children": 200
})
#Visualization of simulation results
from biomass import run_simulation
run_simulation(model, viz_type="average", show_all=False, stdev=True)
#Sensitivity analysis
from biomass import run_analysis
run_analysis(model, target="reaction", metric='integral', style='barplot')
def test_run_simulation():
run_simulation(circadian_clock, viz_type='original')
def test_run_simulation():
run_simulation(tgfb_smad, viz_type='original')
import sys
import os
sys.path.append("Hass_2017")
from biomass import run_simulation
import Hass_2017
model = Hass_2017.create()
#run_simulation(model, viz_type='original') #defult pdf format
run_simulation(model, viz_type="original", save_format="png")
#run_simulation(model, viz_type="original", save_format="png",show_all=True)
#run_simulation(model, viz_type="average", save_format="png")
#run_simulation(model, viz_type="best", save_format="png")
#run_simulation(model, viz_type="experiment", save_format="png")
