• SBOS


    SBOS (Systems Biology Operational Software) is a Linux live DVD designed to fit the needs of systems biologists. It features a collection of up-to-date free software tools and kinetic models installed in a Linux environment. SB.OS is based on the popular Linux distribution Ubuntu 11.04 and ready to use for all non-commercial purposes.


  • SBtab


    SBtab is a set of conventions for data tables, facilitating data exchange in Systems Biology. Its aim is to simplify data processing by a standardized syntax and names. This includes, among other things, prescriptions for column names, semantic annotations, and physical units. On our website, we also offer tools for the processing of SBtab files: Their syntax can be validated online and SBtab files can be converted to SBML files (and vice versa).


  • Parameter balancing

    Parameter balancing

    Parameter balancing is a method to determine consistent parameter sets for kinetic metabolic models. Experimentally measured values, when directly inserted into a model, are likely to yield incomplete and inconsistent parameter sets. Balanced parameter sets, which are complete and consistent, are computed from kinetic constants and other data collected from experiments or the literature, based on constraints between biochemical quantities and assumptions about typical ranges, represented by prior values and bounds. On our website, users can employ an online interface of the parameter balancing tool. This requires mainly a metabolic model in either the SBML or the SBtab format.


  • Annotate your model

    Annotate your model

    In Systems Biology models are created in various formats. So far only few formats (like SBML) allow the annotation of the biological context of a model. This service will help you to link your model to biological web resources by creating a CSV file containing MIRIAM annotations.


  • rxncon


    The complexity of cellular networks is an outstanding challenge for documentation, visualisation and mathematical modeling. In this project, we develop a new way to describe these networks that minimizes the combinatorial complexity and allows an automatic visualisation and export of mathematical (ODE/rulebased) models. The development has been driven in collaboration with the Systems Biology Institute in Tokyo and partners at Linköping University and the University of Gothenburg.


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