|SigTran: An Overview
SigTran is a modeling environment especially designed to enable biological researchers to
carry out large scale simulations and analysis of complex signal transduction networks.
Designed, written and tested at CSI originally by Dr. Michel F. Pettigrew and Peter Divalentin,
a third year student at University of Washington, SigTran is being used by the UW/CSI modeling team
to aid in their systems biology research into T cell signal tranduction events. Since then CSI has
had the invaluable assistance and support from UW students Ryan May, Afshin Hossien-Mashadi
and Khoi Che. The students have been involved in all aspects of development from development on the
SigTran engine to creating model test cases to creating UI interfaces.
SigTran has incorporated some of the latest developments in the field of stochastic simulation
of biological networks. Some of these major developments include the Firth-Bray algorithm with
molecular complexes treated as software objects with multistate functionality [1,2],
Shimizu's work on spatial organization of cell signaling pathways  and the algorithmic
enhancements of Gibson and others [4,5] to the Gillespie algorithm.
SigTran is built around a powerful and flexible stochastic engine, written entirely in Fortran
90/95. SigTran offers biological researchers a choice of four major stochastic algorithms
including (a) the Firth-Bray algorithm of StochSim (b) Gillespie (c) Gillespie-Gibson and (d)
a novel uniform time stepping algorithm developed at UW/CSI. In addition to running simulations
of biological network models in a stochastic mode, biological researchers may, with no additional
effort, switch SigTran into a deterministic mode. SigTran provides full simulation support for
systems of ordinary differential and algebraic equations (DAEs) based on the highly respected
software codes of E. Hairer and G. Wanner .
SigTran currently supports
- Complete elementary reaction capability up to and including ternary reactions
- Michaelis-Menton kinetics
- Clamping of species copy numbers
- Multistate specification and simulation
- Tagged molecule or reaction tracking
- DAE based kinetic simulations for comparison with stochastic simulations and model debugging
- Ensemble simulation with run time and post run time statistical processing
- Graphical display and plotting of simulation output.
A natural and intuitive graphical user interface first written in JAVA, then in Microsoft® .NET
supports all these features with graphical support enabled by Gnuplot
Current major development goals within SigTran include:
- Stochastic simulation over spatial domains in 2 and 3 dimensions
- Extention of multistate functionality to reactions between distinct multistate complexes
- Development of a callable version of SigTran.
SigTran is freely available for downloading and testing. Questions and comments should be directed to
the Modeling Group at Cell Systems Initiative email@example.com
 Morton-Firth, C. J. (1998), Stochastic Simulation of Cell Signalling Pathways, PhD thesis, University of Cambridge, Cambridge, UK.
 Morton-Firth, C. J. and Bray, D. (1998). Predicting temporal fluctuations in an intracellular signaling pathway. J. Theor. Biol. 192:117-128.
 Shimizu, T. S. (2002), The Spatial Organization of Cell Signalling Pathways - A Computer-Based Study, PhD thesis, University of Cambridge, Cambridge, UK.
 Gibson, M. A. (2000), Computational Methods for Stochastic Biological Systems, PhD thesis, California Inst. Technology.
 Gibson, M. A. and J. Bruck (2000), Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels, J. Phys. Chem. A, 104, 1876-1889.