StochPy Stochastic modeling in Python
StochPy is a versatile stochastic modeling package which is designed for stochastic simulation of molecular control networks
- File releases 2.3 and earlier: http://sourceforge.net/projects/stochpy
- File releases 2.4+: PyPI and Github.
- Source code: https://github.com/SystemsBioinformatics/stochpy
StochPy is open source software distributed under the BSD 3-Clause License, see LICENSE file for more details.
Documentation
Documentation can be found in the user guide (see Documentation directory or in sourceforge)
Installation
The following software is required before installing StochPy (see user guide for more details):
- Python 2.6+ or Python 3.4+
- NumPy 1.x+
- SciPy
- Matplotlib (optional)
- libsbml (optional)
- libxml2 (optional)
- mpmath (optional)
Install StochPy and dependencies with PIP using the following command (in your StochPy Python virtual environment):
pip install scipy matplotlib python-libsbml jedi==0.17.2 ipython stochpyIf you are using Anaconda, create a custom conda environment for StochPy, for example:
conda create -n "stochpy39" -c sbmlteam python=3.9 pip scipy matplotlib sympy ipythonactivate your new environment, install StochPy (only required once per environment) and start ipython.
conda activate stochpy39
pip install stochpy
ipython
Linux/MAC OS/Cygwin from source.
In the directory where you downloaded/cloned the StochPy source, for example, the git main branch:
sudo python setup.py installWindows
Use the available windows installer or use PyPI (described above).
Getting Started
You can run ipython and import stochpy:
import stochpy
smod = stochpy.SSA()Basic Simulation with the Direct method
smod.DoStochSim(IsTrackPropensities=True)
smod.data_stochsim.simulation_endtime
smod.data_stochsim.simulation_timesteps
smod.GetWaitingtimes()
smod.PrintWaitingtimesMeans()Do some Plotting
smod.PlotSpeciesTimeSeries()
smod.PlotWaitingtimesDistributions()
smod.PlotPropensitiesTimeSeries()Write data to a text file
smod.Export2File()
smod.Export2File(analysis='distribution')
smod.Export2File(analysis='distribution',datatype='species')
smod.Export2File(analysis='mean',datatype='species')
smod.Export2File(analysis='std',datatype='species')
smod.Export2File(analysis='autocorrelation',datatype='species')Show the means from the data of 3-th trajectory
smod.DoStochSim(trajectories=3) # multiple trajectories
smod.data_stochsim.simulation_trajectory
smod.PrintSpeciesMeans()
smod.PrintSpeciesStandardDeviations()Switch to data from trajectory 1 and show the means of each species
smod.GetTrajectoryData(1)
smod.PrintSpeciesMeans()
smod.PrintSpeciesStandardDeviations()Do one long simulation
smod.DoStochSim(trajectories=1,end=1000000,mode='steps')
smod.PrintSpeciesMeans()
smod.PrintSpeciesStandardDeviations()Plot the PDF for different bin sizes
smod.PlotSpeciesDistributions()
smod.PlotSpeciesDistributions(bin_size=5) # larger bin size
smod.PlotSpeciesDistributions(bin_size=10) # again a larger bin size
smod.Export2File(analysis='distribution',datatype='species')Usage of the Reload Function: Ksyn = 20, kdeg = 0.2
smod.ChangeParameter('Ksyn',20.0)
smod.ChangeParameter('Kdeg',0.2)
smod.DoStochSim()
smod.PrintSpeciesMeans() # should be ~Ksyn/KdegUse another model to show the Interpolation features
smod.Model('dsmts-001-01.xml.psc')
smod.DoStochSim(trajectories=1000,end=50,mode='time')
smod.GetRegularGrid(npoints=51)
smod.PlotAverageSpeciesTimeSeries()
smod.PrintAverageSpeciesTimeSeries()
smod.Export2File(datatype='species',analysis='timeseries',IsAverage=True)Test each method for different models:
smod.Model('Autoreg.psc')
smod.DoStochSim(trajectories=1,end=1000,mode='steps')
smod.Method('NextReactionMethod')
smod.DoStochSim(trajectories=1,end=1000,mode='steps')
smod.data_stochsim.species
smod.PlotWaitingtimesDistributions()
smod.Method('FirstReactionMethod')
smod.DoStochSim(trajectories=1,end=1000,mode='steps')
smod.Method('TauLeaping')
smod.DoStochSim(trajectories=1,end=1000,mode='steps')smod.Model('DecayingDimerizing.psc')
smod.DoStochSim(method = 'Direct',trajectories=1,end=50,mode='time')
smod.DoStochSim(method = 'NextReactionMethod',trajectories=1,end=50,mode='time')
smod.DoStochSim(method = 'FirstReactionMethod',trajectories=1,end=50,mode='time')
smod.PlotWaitingtimesDistributions()
smod.DoStochSim(method = 'TauLeaping',trajectories=1,end=50,mode='time',epsilon=0.03) # Should outperform all other implementations
smod.PlotSpeciesTimeSeries()
#smod.PlotWaitingtimesDistributions() # Should give an errorsmod.Model('chain500.psc')
smod.DoStochSim(method = 'Direct',trajectories=1,end=10000,mode='steps')
smod.DoStochSim(method = 'NextReactionMethod',trajectories=1,end=10000,mode='steps') # should outperform the direct method and all other implementationsUse the Next Reaction Method to test a model with a time event
smod.Model('dsmts-003-03.xml.psc')
smod.DoStochSim(method = 'NextReactionMethod')
smod.DoTestsuite()Use the First Reaction method to test a model with a concentration event
smod.Model('dsmts-003-04.xml.psc')
smod.DoStochSim(method = 'FirstReactionMethod')
smod.DoTestsuite()Volume Models
smod.Model('dsmts-001-11.xml.psc')
smod.DoStochSim(method = 'Direct',trajectories=1000,end=50,mode ='time')
smod.PrintAverageSpeciesTimeSeries()Author information
Timo R. Maarleveld, Brett G. Olivier, and Frank J. Bruggeman Centrum Wiskunde en Informatica, Amsterdam, Netherlands VU University, Amsterdam, Netherlands
e-mail: [email protected]
Publication
StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes http://dx.doi.org/10.1371/journal.pone.0079345
Licence
Copyright (c) 2011-2021, Timo R. Maarleveld, Brett G. Olivier, and Frank J. Bruggeman Vrije Universiteit Amsterdam. All rights reserved.
StochPy is open source software distributed under the BSD 3-Clause License see LICENSE file for more details.