4- Parameter estimation (estimation.py module).¶
The estimation.py module combines ODE solving with the DE (differential evolution) genetic optimizer.
import matplotlib as mpl
import stimator
#To make sure we have always the same matplotlib settings
#(the ones in comments are the ipython notebook settings)
mpl.rcParams['figure.figsize']=(9.0,5.0) #(6.0,4.0)
mpl.rcParams['font.size']=14 #10
mpl.rcParams['savefig.dpi']=100 #72
4.1. Example: glyoxalase system¶
An example with two time courses
mdl = """
title Glyoxalase system in L. Infantum
glx1 : HTA -> SDLTSH, V1*HTA/(Km1 + HTA)
glx2 : SDLTSH ->, V2*SDLTSH/(Km2 + SDLTSH)
find V1 in [0.00001, 0.0001]
find Km1 in [0.01, 1]
find V2 in [0.00001, 0.0001]
find Km2 in [0.01, 1]
init : SDLTSH = 7.69231E-05, HTA = 0.1357
"""
m1 = stimator.read_model(mdl)
print mdl
best = m1.estimate(['TSH2a.txt', 'TSH2b.txt'], names=['SDLTSH', 'HTA'])
print best.info()
best.plot()
## #save predicted timecourses to files
## redsols = solver.optimum.optimum_tcs
## redsols.saveTimeCoursesTo(['TSH2a_pred.txt', 'TSH2b_pred.txt'], verbose=True)
title Glyoxalase system in L. Infantum glx1 : HTA -> SDLTSH, V1*HTA/(Km1 + HTA) glx2 : SDLTSH ->, V2*SDLTSH/(Km2 + SDLTSH) find V1 in [0.00001, 0.0001] find Km1 in [0.01, 1] find V2 in [0.00001, 0.0001] find Km2 in [0.01, 1] init : SDLTSH = 7.69231E-05, HTA = 0.1357 -- reading time courses ------------------------------- file C:UserstonhoDesktopstgitdoctutorialTSH2a.txt: 244 time points, 1 variables file C:UserstonhoDesktopstgitdoctutorialTSH2b.txt: 347 time points, 2 variables Solving Glyoxalase system in L. Infantum... 0 : 0.001590 1 : 0.001590 2 : 0.000133 3 : 0.000133 4 : 0.000133 5 : 0.000133 6 : 0.000133 7 : 0.000133 8 : 0.000133 9 : 0.000133 10 : 0.000133 11 : 0.000133 12 : 0.000133 13 : 0.000133 14 : 0.000133 15 : 0.000133 16 : 0.000133 17 : 0.000133 18 : 0.000133 19 : 0.000133 20 : 0.000133 21 : 0.000133 22 : 0.000133 refining last solution ... Done! Too many generations with no improvement in 23 generations. best score = 0.000011 best solution: [ 2.57587430e-05 2.23315654e-05 9.80378174e-02 2.52514985e-01] Optimization took 2.018 s (00m 02.018s) --- PARAMETERS ----------------------------- V1 2.57587e-05 +- 2.76715e-07 V2 2.23316e-05 +- 3.10525e-06 Km2 0.0980378 +- 0.0197222 Km1 0.252515 +- 0.00707576 --- OPTIMIZATION ----------------------------- Final Score 1.06654e-05 generations 23 max generations 200 population size 80 Exit by Too many generations with no improvement --- TIME COURSES ----------------------------- Name Points Score TSH2a.txt 244 4.1791e-06 TSH2b.txt 347 6.48635e-06
An example with an unknown initial value
m2 = m1.copy()
# Assume init.HTA is uncertain
m2.init.HTA.set_bounds((0.05,0.25))
# do not estimate Km1 and Km2, just to help the analysis
m2.parameters.Km1.set_bounds(None)
m2.parameters.Km2.set_bounds(None)
m2.parameters.Km1 = 0.252531
m2.parameters.Km2 = 0.0980973
# VERY IMPORTANT:
# only one time course can be used:
# cannot fit one initial value using several timecourses!
best = m2.estimate(['TSH2a.txt'], names=['SDLTSH', 'HTA'], opt_settings=dict(pop_size=60))
print best.info()
best.plot()
-- reading time courses ------------------------------- file C:UserstonhoDesktopstgitdoctutorialTSH2a.txt: 244 time points, 1 variables Solving Glyoxalase system in L. Infantum... 0 : 0.000146 1 : 0.000146 2 : 0.000025 3 : 0.000025 4 : 0.000025 5 : 0.000021 6 : 0.000021 7 : 0.000021 8 : 0.000021 9 : 0.000021 10 : 0.000021 11 : 0.000021 12 : 0.000021 13 : 0.000021 14 : 0.000021 15 : 0.000008 16 : 0.000008 17 : 0.000008 18 : 0.000008 19 : 0.000008 20 : 0.000008 21 : 0.000008 22 : 0.000008 23 : 0.000008 24 : 0.000008 25 : 0.000008 26 : 0.000008 27 : 0.000008 28 : 0.000008 29 : 0.000008 30 : 0.000008 31 : 0.000008 32 : 0.000008 33 : 0.000008 34 : 0.000008 35 : 0.000008 refining last solution ... Done! Too many generations with no improvement in 36 generations. best score = 0.000003 best solution: [ 3.36574664e-05 1.87056447e-05 9.25226437e-02] Optimization took 1.898 s (00m 01.898s) --- PARAMETERS ----------------------------- V1 3.36575e-05 +- 1.02919e-05 V2 1.87056e-05 +- 5.94082e-06 init.HTA 0.0925226 +- 0.0400775 --- OPTIMIZATION ----------------------------- Final Score 3.11054e-06 generations 36 max generations 200 population size 60 Exit by Too many generations with no improvement --- TIME COURSES ----------------------------- Name Points Score TSH2a.txt 244 3.11054e-06
