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Parameter balancing default configuration files

Parameter balancing is an application with many configuration options and its Bayesian estimation relies on the provision of a prior distribution. All these information are provided per default in our software. Experienced users may wish to change the configuration options or provide an alternative prior distribution which better suits their individual needs. They can provide this information in form of SBtab files. We suggest to first read Section 2.1.2 in the parameter balancing Manual and then use the default SBtab files as a starting point for making alterations:

SBtab file with default prior distributions

SBtab file with default configuration options

Source Code of the Software

The Python3 source code of parameter balancing can be found on the parameter balancing Github page.
Also, parameter balancing is part of the Metabolic Networks Toolbox for Matlab.

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