` Duke University - Laboratory of Biological Networks: Software - Dynetica

Dynetica - A Simulator of Dynamic Networks

(written in Java; Java 1.7 preferred)
Copyright © 2000-2003 by Lingchong You.

Last updated:

Please contact Lingchong You (you@duke.edu) if you run into any problems or identify any bugs.

Installation

Download the current version of Dynetica 2.0 Beta

Unzip Dynetica2.0.zip. The unzipped folder will contain two subfolders: "Current Distribution" and "tutorial_models". Go into "Current distribution", you'll see a jar file, Dynetica.jar, which is the binary file of Dynetica. It depends on several libraries, which are put in the directory "lib". Do not change the name of the folder or make any changes to the .jar files in the folder.

In Windows (7 or 8) or MacOS (10.6 or above), double-clicking Dynetica.jar should run the program.

Alternatively,you can use a command window, go to the folder "Current Distribution", and then use the following command:

java -jar Dynetica.jar

This appears to be required in Linux (tested in Ubuntu)

When you first run Dynetica, no models are loaded. You can get a sense of how to run/build models by opening the input files in "tutorial_models". The documentation included here is somewhat outdated but is still useful to give the initial guidance for constructing and analyzing Dynetica models. This documentation will be updated in the near future.

Previous version:

Download previous version

Screenshots

The main window showing a simplified model of phage T7 intracellular growth cycle

A figure window showing the simulated time-courses for selected components.

Click here for the User's guide of Dynetica (PDF). You may also check out a preprint (PDF) describing this software.

Features

  • Graphic construction of kinetic models of various biological systems.
  • The user needs only specify the components in the system and their interactions. The program automatically generates the ordinary differential equations.
  • Time course simulations using either deteriministic algorithms or stochastic algorithms (currently only the Gillespie algorithm has been implemented).
  • Basic sensitivity analysis

Duke University ©2008