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Parameter balancing is a tool for metabolic modelling in systems biology. It is implemented in Python3 and its code underlies the PEP8 guidelines. These are the possible ways of employing parameter balancing for your project:

  1. The online version The tool can be employed via All required knowledge can be found here on the webpage.
  2. Parameter balancing as Pypi package and commandline tool

    To install Parameter Balancing as a Python3 package, first of all you need Python3. Next, you will need the pip3 installer. You can find information on how to do this here: Afterwards, install Parameter Balancing by typing in your command line:

    sudo pip3 install pbalancing
    This will also install libsbml and tablib on your computer if these libraries are missing. You can now employ Parameter Balancing as a Python3 package by, e.g., writing a script such as
    from pbalancing import parameter_balancing
    In this example case, 'model.xml' is the file name of an SBML model. Further optional arguments are an SBtab parameter file, an SBtab prior distribution file, and an SBtab configuration file (example files at the bottom of this page).

    To run parameter balancing as a commandline tool, the package needs to be installed as explained above. Then, it can be executed in the commandline as follows:

     python3 -m pbalancing.parameter_balancing model.xml
    where model.xml corresponds to the path of your SBML model. It is also possible to provide further input files, such as an SBtab parameter files (.tsv), an SBtab prior information file (.tsv), and an SBtab options file (.tsv) for the configuration of parameter balancing. Providing complete file information would look like this:
     python3 -m pbalancing.parameter_balancing model.xml --sbtab_data data_file.tsv --sbtab_prior prior_file.tsv --sbtab_options options_file.tsv
    You can create a log file by setting the flag -l, you can use pseudo values to account for a lack of data by setting the flag -p, you can watch program outputs on your commandline by setting the flag -v. Information on the SBtab format can be found on, more information on the mentioned file types can be found in the parameter balancing manual, and example files can be found at the bottom of this page.

  3. Python code for parameter balancing Python3 source code for parameter balancing can be freely downloaded. Please see the instructions that come with the code.
  4. Web2py application The web2py application for parameter balancing can be freely downloaded from the Github repository. Please see the instructions that come with the code.
  5. Matlab code for parameter balancing MATLAB code for parameter balancing is included in the Metabolic Networks Toolbox. For more information, install the MNT toolbox and type 'help mnt_parameter_balancing'.

Models and data sets

This collection of models was assembled as an exemplary starting point for parameter balancing. They are mainly taken from the BioModels Database, others taken from chosen publications. Please note that these models are not altered from their original state in order to make them more compliant to the parameter balancing procedure. As stated in the FAQ, parameter balancing does not work well with, e.g., biomass reactions, transport reactions, or enzymes that are modelled as species (instead of parameters). Furthermore, if modifiers lack characterising SBO terms, they cannot be identified as either catalyst or inhibitor. If you properly want to employ the models below for parameter balancing, it is advisable to keep these remarks in mind.
The corresponding parameter collections are mainly supposed to exemplify the data format SBtab. Their origins are given in the file itself. They are by no means considered to be exhaustive.

Phosphofructokinase Reaction (PFK)

Our SBML model of the Phosphofructokinase reaction was constructed from the KEGG Reaction R04779 with semanticSBML-fill.

Glycolysis (Teusink et al, 2000)

Teusink's glycolysis model (Teusink et al, (2000)) has been assembled from enzymatic rate laws determined in vitro.

Glycolysis model (Hynne et al., 2001)

Hynne's glycolysis model (Hynne et al., 2001) was created to describe glycolysis in yeast at the onset of metabolic oscillations. In the encoded version from BioModels Database, SBO terms for allosteric inhibitors are missing. We provide a second version in which these SBO terms have been added.

Pancreatic Beta Cell Model (Jiang et al.,2007)

Jiang's model of the glucose-stimulated secretion system in pancreatic beta cells (Jiang et al.,2007) is one of the largest kinetic models in BioModels Database.

E.coli Model (Noor et al.,2016)

Noor's model of E.coli (Noor et al.,2016).

E.coli Model (Wortel et al.,2016)

Wortel's model of E.coli (Wortel et al.,2018).

E coli central metabolism

Additional data files concerning E. coli central metabolism can be found here.

A collection of transformed standard Gibbs free energies of reaction

The collection is an extract of the eQuilibrator project and was kindly provided by Elad Noor. It can be downloaded here.

Data provenance

Data for the examples were collected from the following public data sources:
  1. Brenda: Brenda Enzyme Database

  2. NIST: Thermodynamics of Enzyme-Catalyzed Reactions, see R.N. Goldberg, Y.B. Tewari and T.N. Bhat (2004), Thermodynamics of enzyme-catalyzed reactions - a database for quantitative biochemistry, Bioinformatics 20 (16), 2874-2877

  3. yeastGFP: Yeast GFP Fusion Localization Database

  4. Alberty: Alberty, R.A. (1998), Calculation of standard transformed Gibbs energies and standard transformed enthalpies of biochemical reactants, Archives of Biochemistry and Biophysics, 353 (1), 116-130