Hy3S is now a part of a larger project, SynBioSS, a software suite for synthetic biology.
SynBioSS utilizes Hy3S to conduct the simulations, but wraps a cross-platform user interface around it. SynBioSS also includes two web-based pieces of software to help design
and create model files for simulation. Continued development of SynBioSS and Hy3S will be documented at the SynBioSS web site.
What is Hy3S?
hi-three-ess) is an open-source project aimed
at developing, integrating, and disseminating software that simulates a
chemical or biochemical system as quickly as
possible, using hybrid or other approximate algorithms to greatly reduce the computational time, but still retain accuracy.
We are interested in the computational design of interesting biological
especially ones that rely on regulated gene expression to produce useful
behavior. By combining quantitatively predictive simulations and design algorithms, one will eventually be able to use computers to identify the
exact DNA sequence that produces a desired function, greatly reducing the amount of necessary experimental work. Of course, that goal requires mature
simulation algorithms and this project seeks to provide them.
Questions, comments, suggestions? Email me. (Address is below)
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.
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.