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Stochastic chemical network simulators

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The MOSAIC Group moved from ETH Zurich to the Max Planck Institute in Dresden (Germany) in summer 2012. This web site is no longer maintained and will be discontinued soon. Please visit our new site for all software downloads, publications, and information about our research and teaching.

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Here you can download the C++ source code of the partial-propensity methods to simulate chemical reaction networks. These methods are exact stochastic simulation algorithms that sample the chemical master equation. The computational cost of partial-propensity methods is significantly lower than that of standard SSA algorithms.

The following implementations are available: partial-propensity direct method (PDM), sorting partial-propensity direct method (SPDM), partial-propensity SSA with composition-rejection sampling (PSSA-CR), delay partial-propensity direct method (dPDM).

PDM is recommended for strongly coupled chemical reaction networks (see publication below for a definition of "strongly coupled").
SPDM is recommended for multi-scale reaction networks.
PSSA-CR is recommended for weakly coupled reaction networks.
dPDM is recommended for reactions with time delays.

The source codes are available here:

PDM.tar.bz2

SPDM.tar.bz2

PSSACR.tar.bz2

dpdm.tar.bz2

The archives contain a README with instruction how to compile and use the simulation codes. The following publications describe PDM/SPDM (first reference), PSSA-CR (second reference), dPDM (third reference), and the family of partial-propensity methods in general (fourth reference). Please cite them whenever you use these methods or the software published here.

This article introduces the general concept of partial propensities and presents PDM and SPDM:

R. Ramaswamy, N. González-Segredo, and I. F. Sbalzarini. A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks. J. Chem. Phys., 130(24):244104, 2009. (PDF)

Important notice: Unfortunately there has been a typo in Table 1 of the published paper. Please see the Notes and Corrections for a corrected version of the algorithm.

This article combines partial propensities with composition-rejection sampling in order to further reduce the computational cost on weakly coupled networks and introduces PSSA-CR:

R. Ramaswamy and I. F. Sbalzarini. A partial-propensity variant of the composition-rejection stochastic simulation algorithm for chemical reaction networks. J. Chem. Phys., 132(4):044102, 2010. (PDF)

This article extends partial-propensity methods to reaction networks with delays and introduces dPDM:

R. Ramaswamy and I. F. Sbalzarini. A partial-propensity formulation of the stochastic simulation algorithm for chemical reaction networks with delays. J. Chem. Phys., 134(1):014106, 2011. (PDF)

This article gives an overview of the entire family of partial-propensity SSAs and shows how they are modularly composed from different building blocks:

R. Ramaswamy and I. F. Sbalzarini. Fast exact stochastic simulation algorithms using partial propensities. In Proc. ICNAAM, Numerical Analysis and Applied Mathematics, International Conference, pages 1338–1341, Rhodes, Greece. AIP, 2010. (PDF)

In order to ensure financial support for our project at ETH and allow further development of this software, please cite above publications in all your documents and manuscripts that made use of this software. Thanks a lot!
IN NO EVENT SHALL THE ETH BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE ETH HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. THE ETH SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE ETH HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

 

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© 2014 ETH Zurich | Imprint | Disclaimer | 20 April 2011
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