Our aim is to fill a surprising and glaring gap in the software available today for biomedical simulation: the lack of a standard library for stochastic simulation usable from a variety of programming languages and able to capitalize on various hardware acceleration options. We will produce a free, efficient, portable library for scalable stochastic simulation, featuring selectable algorithms ranging from a fast implementation of the exact algorithm introduced by Gillespie to recent faster but approximate algorithms. The library will feature support for C, C++, Java, Python, Perl, MATLAB and Mathematica under Windows, MacOS, Linux and FreeBSD. Keeping the same high-level front-end API (application programming interface), we will provide multiple back ends suitable for different hardware scenarios: typical desktop single-processor systems, multicore and multiprocessor systems, and FPGA-based (field programmable gate array) hardware acceleration boards. We will also provide an SBML (Systems Biology Markup Language) interface layer, allowing easy, direct simulation of models expressed in SBML. All software, as well as hardware designs and configuration software, will be released under the open-source GNU LGPL license for research use. By introducing such a portable, robust and flexible library, we hope to simultaneously reduce the effort wasted on repeated reimplementation of the same software by different groups, and provide a baseline reference that more advanced researchers can use as a jumping-off point for new and improved algorithm and software development. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB007511-01
Application #
7234899
Study Section
Special Emphasis Panel (ZRG1-BST-D (51))
Program Officer
Peng, Grace
Project Start
2007-05-01
Project End
2010-04-30
Budget Start
2007-05-01
Budget End
2008-04-30
Support Year
1
Fiscal Year
2007
Total Cost
$618,529
Indirect Cost
Name
California Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
009584210
City
Pasadena
State
CA
Country
United States
Zip Code
91125
Daigle Jr, Bernie J; Roh, Min K; Petzold, Linda R et al. (2012) Accelerated maximum likelihood parameter estimation for stochastic biochemical systems. BMC Bioinformatics 13:68
Wallace, E W J; Gillespie, D T; Sanft, K R et al. (2012) Linear noise approximation is valid over limited times for any chemical system that is sufficiently large. IET Syst Biol 6:102-15
Wu, Sheng; Fu, Jin; Cao, Yang et al. (2011) Michaelis-Menten speeds up tau-leaping under a wide range of conditions. J Chem Phys 134:134112
Roh, Min K; Daigle Jr, Bernie J; Gillespie, Dan T et al. (2011) State-dependent doubly weighted stochastic simulation algorithm for automatic characterization of stochastic biochemical rare events. J Chem Phys 135:234108
Sanft, Kevin R; Wu, Sheng; Roh, Min et al. (2011) StochKit2: software for discrete stochastic simulation of biochemical systems with events. Bioinformatics 27:2457-8
Mauch, Sean; Stalzer, Mark (2011) Efficient formulations for exact stochastic simulation of chemical systems. IEEE/ACM Trans Comput Biol Bioinform 8:27-35
Meeker, Kirsten; Harang, Richard; Webb, Alexis B et al. (2011) Wavelet measurement suggests cause of period instability in mammalian circadian neurons. J Biol Rhythms 26:353-62
Daigle Jr, Bernie J; Roh, Min K; Gillespie, Dan T et al. (2011) Automated estimation of rare event probabilities in biochemical systems. J Chem Phys 134:044110
Roh, Min K; Gillespie, Dan T; Petzold, Linda R (2010) State-dependent biasing method for importance sampling in the weighted stochastic simulation algorithm. J Chem Phys 133:174106
Drawert, Brian; Lawson, Michael J; Petzold, Linda et al. (2010) The diffusive finite state projection algorithm for efficient simulation of the stochastic reaction-diffusion master equation. J Chem Phys 132:074101

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