General Technique

Dizzy is a chemical kinetics simulation software package written in the Java programming language. It provides a model definition environment and an implementation of the Gillespie, Gibson-Bruck, and Tau-Leap stochastic algorithms, as well as several ordinary differential equation (ODE) solvers. Dizzy is capable of importing and exporting the SBML model definition language, as well as graphically displaying models using the Cytoscape software system. The various simulation engines can be used to solve the dynamics of a kinetic model from specified initial data. A model consists of a system of interacting chemical species, and the reactions through which they interact. When a model is solved, the results can be plotted, displayed in tabular format, or saved to a file.

Dizzy requires the Java 2 Runtime Environment (JRE) version 1.4 or newer, or an equivalent JRE. Dizzy has been tested and used successfully on Windows XP, Mac OSX, and Linux 2.6.X computer systems. To use Dizzy, the software needs to be downloaded and installed.

To download the software, follow these steps:

1. Go to the Dizzy home page and click on "Download Dizzy."
   
2. Select the link corresponding to the type of computer you have (e.g., Windows, Mac, Linux, etc.), and based on whether or not you already have a suitable version of Java installed on your computer (see above).
   
3. Execute the downloaded file (on most computers, this is done by double-clicking on it), which will run a graphical installer program. The graphical installer program will walk you through the installation process.

For more information about the Dizzy system, please consult the Dizzy User Manual.

Purpose/use/application of the technique:

In a network of interacting biochemical species, the stochasticity of chemical reaction events can be important whenever a species has a low copy number, as can be the case for transcription factors. This stochasticity can lead to cell-to-cell phenotypic variation and temporal fluctuations in the concentration of a species in the system. In complex genetic regulatory networks in which multiple genes are being transcribed (and thus there are multiple sources of transcriptional noise), and in which there may be feedback loops, it can be difficult to determine the phenotypic variation and/or species fluctuations by studying the kinetic model and the topology of the interaction network. In this case, stochastic simulations (e.g., using Monte Carlo techniques) can be used to model the stochastic kinetics. Dizzy is designed for this purpose. A model is described using the Dizzy language, which is designed to be intuitive for biochemists. The model is loaded into the Dizzy program, and the user can choose one of several functions. The model can be exported to one of several formats (e.g., SBML). The model can be visualized using one of several visualized using Cytoscape. Finally, the model can be simulated using one of the stochastic or ODE simulators available.

For more information about the Dizzy system, please consult the Dizzy User Manual.

Example(s) of projects at ISB that use this technique:

1. A collaborative study of how the dual feedback loops in the yeast galactose utilization pathway suppress heterogeneity of GAL induction.
   Aitchison and Bolouri Labs

2. A cross-species comparison study of how transcriptional/translational noise scales with genome complexity, cellular volume, and cellular division time. For additional projects, see the list of publications below.

Ongoing area of technology development:

We are investigating how to incorporate process algebra techniques into Dizzy. In addition, we are studying different ways of using Dizzy in a cluster computing environment.

Representative publication(s):

de Atauri P, Orrell D, Ramsey S, Bolouri H, Is the regulation of galactose 1-phosphate tuned against gene expression noise? Biochemical Journal, Vol. 387, pp. 77-84, 2005

Orrell D, Ramsey S, de Atauri P, Bolouri H, A method to estimate stochastic noise in large genetic regulatory networks. Bioinformatics, Vol.21, pp.208-217, 2005

Ramsey S, Orrell D, Bolouri H, Dizzy: Stochastic simulation of large-scale genetic regulatory networks. J. Bioinformatics Comput. Biol. Vol. 3. No. 2, pp. 415-436, 2005

Orrell D and Bolouri H, Control of internal and external noise in genetic regulatory networks, Journal of Theoretical Biology, Vol.230, No.3,pp. 301-312, 2004

de Atauri P, Orrell D, Ramsey S, Bolouri H, Evolution of "design" principles in biochemical networks, IEE Journal of Systems Biology, June, Vol.1, pp. 28-40, 2004

	Related Information
			Contact information: Stephen Ramsey