Medical Systems Biology

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JANet (Jmol AgeFactDB Network-viewer) is a specialized Jmol-based 3D network viewer for the visualization of ageing-related network data from AgeFactDB.

 JANet 3D viewer interface screenshot


  • Interactive 3D network visualization (rotate, zoom, etc.)
    • 2D representation
    • Stereo representation (e.g.: side by side, red/cyan)
    • 3D beamer mode (condensed side by side stereo, converted for shutter glasses)
      JANet 3D beamer mode example
  • Ageing-related network data (AgeFactDB)
    • Lifespan observations
    • Ageing factors
    • Augmentation nodes (e.g.: allele types, citations)
  • Augmentation with external domain knowledge
    • Gene Ontology (GO)
    • KEGG Pathways
  • Genes of interest analysis (e.g.: differentially expressed genes)
  • Scripting language
    • Jmol scripting language
    • JANet-specific API


The viewer can be started directly from the server at
or downloaded including all necessary network data for local installation

For a local installation there are two options:

  1. Extract the archive somewhere in the file system.
  2. Install a web server locally and extract the archive somewhere below the server root directory.

Option 1:  JANet can be started by opening the file \textit{index.html} from the top level directory in a web browser as a local file. In some browsers, like Safari, this requires to switch off a default restriction for local file usage. The Javascript version of JANet will not work in browsers that prevent loading local binary files using Javascript, like Safari and Opera.

Option 2: JANet can be started by adding the relative directory path of the top-level viewer directory to the server name \textit{localhost} for the local web server and open the web address in a web browser.
  (Example:  http://localhost/tools/janet)

Citing JANet

To cite the network viewer in publications, use:

[submitted, no publication available yet]


Automatic Screening of Perturbations in Boolean networks

Changing internal and external conditions can influence the long term behavior of the Boolean network model. The perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another component can lead to different attractors. Obviously, the number of possible perturbations and combinations of perturbations increases with the size of the network. Manual screening a set of possible components for combinations that have a desired effect on the longterm behavior can be time consuming. We developed a method to automatically screen for perturbations that lead to a user-specified change in the networks behavior.


Downloads :

 runnable jar-file



Getting Started : 

A model of the mammalian cellcycle (Fauré et al., Bioinformatics, 2006) is integrated as exemplary network model. 
After launching the application the example can be loaded by clicking "load example network" in the middle of the window. 
A network model of the mammalian cellcycle and an exemplary simulation setup are loaded.
You can also download the example file cellcycle.visibool (Right click and save link as..). In some browsers the file extension might be changed. The file then has to be renamed to cellcycle.visibool again. 
To test the automatic screening for perturbations switch to the exhaustive simulation panel via the simulation menubar. On the right side the attractors of the network are displayed. The button "Perturbation Screening" starts the automatic screening routine. This routine is sperated into three major steps : 
1. Selection of attractors of interest
Select the desired changes in the long-term behavior of the system. Attractors can be either selected to delete, to be kept or unselected if this attractors is not relevant.
2. Selection of perturbation candidates
After pressing "Next", choose the maximum number of components to be in the set of perturbations. Below the components that are possible targets for the perturbation can be selected.
-Grey means normal behavior
-Red means permanent knock-out
-Green means permanent over-expression
-Blue mean knock-out and over-expression is tested in the set (default for perturbation)
3. Selection of components of interest for attractor comparison
After pressing "Next", the components of interest can be selected. The selected components are used for comparison of the long-term behavior.
Pressing "Simulate" starts the screening process.
Finally the perturbation sets which show the previously selected effects on the long-term behavior of the network.
By double-clicking a perturbation set of interest the resulting attractors are displayed. 
The "Save"-Button stores a log-file with the settings of the perturbation screening and its results.




Latest News

Our paper "Big Data and Precision Medicine - Challenges and Strategies with Health Care Data" has been published in Journal of Data Science and Analytics.

Our paper "Stability of Signaling Pathways during Aging - A Boolean Network Approach" has been published in MDPI Biology.