stimator 0.9.90
Analysis of ODE models with focus on model selection and parameter estimation.
Latest Version: 0.9.95
Stimator is a Python library to analyse ODEbased models (also known as dynamic or kinetic models). These models are often found in many scientific fields, particularly in Physics, Chemistry, Biology and Engineering.
Features include:
 A model description mini language: models can be input as plain text following a very simple and humanreadable language.
 Basic analysis: numerical solution of ODE’s, parameter scanning.
 Parameter estimation and model selection: given experimental data in the form of time series and constrains on model operating ranges, builtin numerical optimizers can find parameter values and assist you in the experimental design for model selection.
Stimator is in an alpha stage: many new features will be available soon.
Requirements
Stimator supports Python versions 2.6 and up, but support of 3.x is coming soon.
Stimator depends on the “scientific python stack”. The mandatory requirements for Stimator are the following libraries:
 Python (2.6 or 2.7)
 numpy
 scipy
 matplotlib
 pip
One of the following “scientific python” distributions is recommended, as they all provide an easy installation of all requirements:
 Anaconda (or Miniconda followed by the necessary conda install’s)
 Python (x,y)
 Enthought Canopy
The installation of these Python libraries is optional, but strongly recommended:
 sympy: necessary to compute dynamic sensitivities, error estimates of parameters and other symbolic computations.
 IPython and all its dependencies: some Stimator examples are provided as IPython notebooks.
 wxPython: although Stimator is a python library meant to be used for scripting or in IPython literate programming interface, a simple GUI is included. This interface requires wxPython.
Installation
After installing the required libraries, (Python, numpy, scipy, matplotlib and pip) the easiest way to install Stimator is with pip:
$ pip install stimator
The classical way also works, but is not recomended:
$ python setup.py install
Basic use: solution of ODE models
This is a warmup example that illustrates model description, ODE numerical solving and plotting:
from stimator import read_model, solve, plot import pylab as pl mdl = """# Example file for Stimator title Example 1 #reactions (with stoichiometry and rate) vin : > x1 , rate = k1 v2 : x1 > x2 , rate = k2 * x1 vout : x2 > , rate = k3 * x2 #parameters and initial state k1 = 1 k2 = 2 k3 = 1 init = state(x1=0, x2=0) #filter what you want to plot !! x1 x2""" m = read_model(mdl) print '========= model ========================================' print mdl print '' s1 = solve(m, tf=5.0) plot(s1) pl.show()
Parameter estimation
Model parameter estimation, based on experimental timecourse data (run example par_estimation_ex2.py):
from stimator import * from stimator.deode import DeODESolver import pylab as pl mdl = """# Example file for Stimator title Example 2 vin : > x1 , rate = k1 v2 : x1 > x2 , rate = k2 * x1 vout : x2 > , rate = k3 * x2 k1 = 1 k2 = 2 k3 = 1 init = state(x1=0, x2=0) !! x2 find k1 in [0, 2] find k2 in [0, 2] find k3 in [0, 2] timecourse ex2data.txt generations = 200 # maximum generations for GA genomesize = 60 # population size in GA """ m1 = read_model(mdl) print mdl optSettings={'genomesize':60, 'generations':200} timecourses = readTCs(['ex2data.txt'], verbose=True) solver = DeODESolver(m1,optSettings, timecourses) solver.Solve() print solver.reportResults() fig1 = pl.figure() solver.draw(fig1) m2 = m1.clone() best = solver.optimum.parameters best = [(n,v) for n,v,e in best] m2.update(best) s2 = solve(m2, tf=20.0) plot(s2) pl.show()
This produces the following output:
 11 time points for 2 variables read from file .../examples/ex2data.txt Solving Example 2... 0 : 3.837737 1 : 3.466418 2 : 3.466418 ... (snip) 39 : 0.426056 refining last solution ... DONE! Too many generations with no improvement in 40 generations. best energy = 0.300713 best solution: [ 0.29399228 0.47824875 0.99081065] Optimization took 8.948 s (00m 08.948s)  PARAMETERS  k3 0.293992 + 0.0155329 k2 0.478249 + 0.0202763 k1 0.990811 + 0.0384208  OPTIMIZATION  Final Score 0.300713 generations 40 max generations 200 population size 60 Exit by Too many generations with no improvement  TIME COURSES  Name Points Score ex2data.txt 11 0.300713
Model selection (experimental design)
One of the examples included in Stimator solves an experimental design problem: finding a feasible set of experimental conditions that lead to the clear selection between 2 models.
Run example glyoxalase_discrim_2m.py.
Summary of road map
 Improve documentation
 I/O to other model description formats (SBML, etc)
File  Type  Py Version  Uploaded on  Size  

stimator0.9.90.zip (md5)  Source  20140603  208KB  
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 Author: António Ferreira
 Home Page: http://enzymology.fc.ul.pt/software.htm
 Keywords: ODEmodels estimation dynamics
 License: BSD

Categories
 Development Status :: 3  Alpha
 Intended Audience :: Education
 Intended Audience :: Science/Research
 License :: OSI Approved :: BSD License
 Natural Language :: English
 Operating System :: OS Independent
 Programming Language :: Python
 Programming Language :: Python :: 2
 Programming Language :: Python :: 2.6
 Programming Language :: Python :: 2.7
 Programming Language :: Python :: 2 :: Only
 Topic :: Scientific/Engineering :: Artificial Life
 Topic :: Scientific/Engineering :: BioInformatics
 Topic :: Scientific/Engineering :: Chemistry
 Topic :: Scientific/Engineering :: Physics
 Package Index Owner: aeferreira
 DOAP record: stimator0.9.90.xml