Chair of Bioinformatics


    JimenaC is an extension version of Jimena, which offers us a toolbox to implement cooperativity by gated proteins, coordinated pathway changes and coopeative behavior in artificial systems

    JimenaC is a Java genetic regulatory network simulation framework which focuses on computational efficacy and a modularized architecture to facilitate the development and testing of new algorithms and models surrounding GRNs. It features

    • Network import from yED graph files (Example 1: Download (source), Example 2: Download)
    • Full support of the exponential interpolation model implemented in SQUAD
    • Full support of the polynomial interpolation models implemented in Odefy, including a significantly better implementation of the interpolation algorithm
    • Several discrete update schemes
    • Perturbation support in all models
    • Several algorithms for the search of steady stable states in all models (fully multithreaded)
    • In development: Network stability analyses, etc.

    It is developed by Stefan Karl (stefan[dot]karl[at]stud-mail[dot]uni-wuerzburg[dot]de) at the Department of Bioinformatics of the University of Würzburg and supervised by Thomas Dandekar (head of the department).



    JimenaC is publically avaible via here

    It requires an environment of JRE version higher than 11 to run it, 

    in windows, simply click it after you have installed JRE. In linux, you need to "java -jar jimenaC.jar" to run it. 

    Network can be edited with yed software (URL: http://www.yworks.com/en/products_yed_download.html). A local backup for education is available here: yed(windows)yed(linux).


    DEMO files

    demo model (interaction_bacillus.graphml) is available here.

    simulation result in a tsv (tab-separated values) format is here. The file can be generated directly using JimenaC export function, however, please edit it using excel or spreadsheet to remove all the protein columns you are interested to minimize the number of proteins for the downstream validation and comparison. e.g., in the demo file, you can find only four interesting proteins. We suggest you choose four to sixteen proteins. 

    experiment observation for validation in a tsv format is here.

    the validation R-script is available here. Please change the two file names according to your input file.  Note the script requires four R library, dplyr, reshape2, ggplot2 and patchwork, you might need to install it before you run the script.





    Cooperative changes are critical to achieve switches in systems. We offer molecular and modelling tools to design these including four key principles to achieve cooperativity in different systems: Efficient task distribution, cooperative planning and perception together with information exchange between components. We calculate and engineer cooperative changes in proteins using light as switching signal. We monitor and predict cooperative pathway changes in B. subtilis biofilm differentiation and oncolytic vaccinia virus acting on cancer cells applying Boolean semi-quantitative simulations. Future extensions include smart nanobiotechnology, active DNA storage and improved oncolysis in cancer. Technical and cellular process switches are designed and compared using GoSynthetic database. We demonstrate how technical systems cooperate using multi-level communication and self-organization in networked robots and satellites.



    • Fixed a bug where duplicate stable states were not recognized
    • Multiple result windows can now be open at the same time
    • States (e.g. stable states) can now be copied (Ctrl+C) from any window (e.g. stable state search) and entered into the nodes window (Ctrl+V)
    • The charts window now exports the time sequence data as a tab-separated table (compatible with common spreadsheet software)
    • Network states can now be loaded from text files and saved to them
    • The user may now choose whether to include the discrete stable states in a random search for stable states
    • Stable states can now be exported to a file and to the clipboard

    Network input format

    Activating influences are modeled using the yED standard arrow tip, inhibiting influences using any other tip (e.g. a diamond). Boolean nodes are modeled using nodes with the captions AND, OR, NOT, &&, ||, !. Cf. the examples above.


    The screenshow shows a simulation of the T-helper network in the jIMENA GUI (which is not yet included in the release). The simulation was started from random values and the NFAT node was set to 1 between the time indices 1 and 2 by a perturbation.

    Large figure



    Compare  Jimena Simulation to experimental data

    Exampole data: See this exel file. Just load up your experimental data file and then JimenaC will compare the experimental data to the simulation.



    Prof. Thomas Dandekar

    Dandekar group, Functional Genomics and Systems Biology department,
    Lehrstuhl für Bioinformatik
    Biocerter, University of Wuerzburg Am Hubland,
    D-97074 Würzburg
    Tel.: +49 (0)931 31 84551
    Fax: +49 (0)931 31 84552

    Mail: dandekar@biozentrum.uni-wuerzburg.de