Getting Started
Download the GUI and simulation programs, licensed under the
GNU GPL.
To run the GUI, you will need the following:
To run the simulation programs, you will need the following:
The Matlab GUI files, simulation programs, and source code are available on the Sourceforge official download site.
The latest source code is available at our SourceForge
project CVS.
Features
- More efficiently simulates the stochastic dynamics by describing the system as a hybrid coupled jump/continuous Markov process
- Solves the resulting system of chemical Langevin and differential Jump equations using either fixed or adaptive stochastic numerical
integrators
- Automatic and dynamic partitioning of the system of bio/chemical reactions
- No a priori knowledge of separation of time-scales needed
- Accuracy of solution governed by well-characterized stochastic numerical integrators of SDEs
- Supports many rate laws. Additional rate laws are easily added
- Supports non-Markovian events, such as gamma or Gaussian distributed transitions
- Cell replication is included as a discrete event occurring at Gaussian distributed replication times
- Combinatorial exploration of kinetic parameters and initial conditions
- System perturbations of both kinetic parameters and species concentrations
- Uses the NetCDF file format: Enables fast write/read of extremely large model and solution data sets
- Targetted production platforms: MPI-capable clusters running Linux (other platforms supported on demand)
- An easy-to-use (but simple) Graphical User Interface is available to quickly define systems of bio/chemical reactions
- Solution data may be directly read into MATLAB or other scientific softwares, analyzed, and plotted for high research productivity
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Documentation
Questions, comments, suggestions? Email me. (Address is below)
Bug Alerts!
- Attempting to use the GUI's Plot solution window when the solution variable exists, but contains no data, results in a Matlab Java
exception (crash!). I suggest using the NetCDF Toolbox to analyze solution data.
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News
- February 26th, 2006: The article "Multiscale Hy3S: Hybrid stochastic simulation for supercomputers" appears in BMC Bioinformatics. We hope that this article
provides a detailed explanation of how the HyJCMSS methods work. We expect, though, that the Hy3S project will continue to evolve, adding more advanced algorithms and
useful features. And, remember, this is an open source project: feel free to reuse code, but please reference this project if you do. Feel free to contact us with
any questions about the code or the algorithms. Thank you!
- October 20th, 2005: Massive additions: Added three additional ways to numerical integrate the SDEs, including the fixed time step
Milstein method (1st order strong accuracy) and adaptive time step methods using Brownian bridges with either the Euler-Maruyama or Milstein
integrators. There's now a total of 5 different algorithms for computing the stochastic dynamics of reaction networks (10 if you include MPI
parallelized versions). In order to accommodate all of the different methods, we went back to using compiler declarative statements within the
source that turns on/off different parts of the code instead of creating many different versions of the source code. Consequently, the Makefile
only works for the Intel Fortran compiler for Linux. Many other compilers support simple declarative statements so adapting the code for other
systems should not be too difficult. The changes are in the CVS tree, but binaries for x86 and ia64 will be released shortly.
- More News!
Examples -- Natural and Synthetic Systems
List of Examples
- Update: The examples from the BMC Bioinformatics paper have been added.
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