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Python bindings for pSSAlib that supports running temporal simulations of chemical reaction networks using any of the following stochastic simulation algorithms (SSAs):
- Gillespie’s direct method (DM) [1] as a reference;
- partial-propensity direct method (PDM) [2];
- sorting partial-propensity direct method (SPDM) [2];
- partial-propensity SSA with Composition-Rejection Sampling (PSSA-CR) [3];
Package provides an interface to sample individual trajectories as well as steady-state populations. Currently, only a number of build-in models are supported and provided with the package:
- Cyclic Linear Chain (CLC) model [2]
- Colloidal Aggregation (CA) model [2]
- Homoreaction model [4]
- Single-Species Birth-Death (SBD) model
- The bacterial Two-Component System [5]
- The Enzymatic Degradation process [6]
This package requires that GNU Scientific Library (GSL) is installed on the machine.
Get latest released version from Test PyPI:
pip install --extra-index-url https://test.pypi.org/simple/ pypssalib
You can also install the in-development version with:
pip install https://github.com/breezerider/pypssalib/archive/main.zip
This requires a modern C++ compiler (tested with gcc and clang) as well as that Boost C++ Headers are available.
https://pypssalib.readthedocs.io/
- Source code: BSD-3-Clause license unless noted otherwise in individual files/directories
- Documentation: Creative Commons Attribution-ShareAlike 4.0 license
To run all the tests issue this command in a terminal:
tox
| [1] | Gillespie DT. Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry. 1977;81(25):2340–2361. doi: 10.1021/j100540a008 |
| [2] | (1, 2, 3, 4) Ramaswamy R, Gonzalez-Segredo N, Sbalzarini IF. A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks. J Chem Phys. 2009;130(24):244104 doi: 10.1063/1.3154624 |
| [3] | Ramaswamy R, Sbalzarini IF. A partial-propensity variant of the composition-rejection stochastic simulation algorithm for chemical reaction networks. The Journal of Chemical Physics. 2010;132(4):044102 doi: 10.1063/1.3297948 |
| [4] | Erban R, Chapman SJ. Stochastic modelling of reaction—diffusion processes: algorithms for bimolecular reactions. Physical Biology. 2009;6(4):046001 doi: 10.1088/1478-3975/6/4/046001 |
| [5] | Kim, J.-R. & Cho, K.-H. The multi-step phosphorelay mechanism of unorthodox two-component systems in e. coli realizes ultrasensitivity to stimuli while maintaining robustness to noises. Comput. Biol. Chem. 2006, doi: 10.1016/j.compbiolchem.2006.09.004 |
| [6] | Fröhlich, F. et al. Inference for stochastic chemical kinetics using moment equations and system size expansion. PLOS Comput. Biol. 2016, doi: 10.1371/journal.pcbi.1005030 |