Models

Quorum sensing(QS) allows the bacteria to monitor their surroundings and the size of their population. Staphylococcus aureus makes use of QS to regulate the production of virulence factors. This mathematical model of the QS system in S aureus was presented and analyzed (Journal of Mathematical Biology(2010) 61:17–54) in order to clarify the roles of the distinct interactions that make up the QS process, demonstrating which reactions dominate the behaviour of the system at various timepoints.
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Bacillus subtilis cells may opt to forgo normal cell division and instead form spores if subjected to certain environmental stimuli, for example nutrient deficiency or extreme temperature. The gene regulation net-work governing sporulation initiation accordingly incorporates a variety of signals and is of significant complexity. The present model (Bulletin of Mathematical Biology (2011) 73:181–211) includes four of these signals: nutrient levels, DNA damage, the products of the competence genes,
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An ODE model of the gene regulation network governing sporulation initiation in Bacillus subtilis to be run in Matlab.

The network incorporates four sporulation-related signals: nutrient supply, DNA damage, the products of the competence genes and the bacterial population size.

Run execute_bacillus_sporulation_initiation.m to simulate the model. This file also contains the signal-related parameters which can be altered to investigate the effect of competing signals.

Some results for this model
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Metabolic model of Sulfolobus solfataricus P2 in the SBML (xml) and metano (txt, sce, fba) format. Scenarios are specific for growth on D-glucose or L-fucose as sole carbon source. Different theoretical routes of L-fucose degradation were modeled (E. coli-like, Xanthomonas-like and lactaldehyde-forming). Highest overall agreement between the model and experimental data was observed for the lactaldehyde-forming route.

The model describes the Entner-Doudoroff pathway in Sulfolobus solfataricus under temperature variation. The package contains source code written in FORTRAN as well as binaries for Mac OSX, Linux, and Windows. If compiling from source code, a FORTRAN compiler is required.
On-line versions of the model are also available at:
http://bioinfo.ux.uis.no/sulfosys
http://jjj.biochem.sun.ac.za/sysmo/projects/Sulfo-Sys/index.html

This mechanistic ODE model describes the growth dynamics of P. aeruginosa, including an antibiotic-induced morphological transition to a fragile spherical form.

Model of reconstituted gluconeogenesis system in S. solfataricus based on the individual kinetic models for PGK, GAPDH, TPI, FBPAase.

This model assumes a phenotypic switch between an acid- and solvent-forming population caused by the changing pH levels. The two phenotypes differ in their transcriptomic, proteomic, and ,thus, their metabolomic profile. Because the growth rates of these phenotypes depends on the extracellular pH, the initiation of the pH-shift results in a significant decline of the acidogenic population. Simultaneously, the solvent-forming population rises and establishes an new steady state.

The model is build
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SBML file supplementary material of the publication.

Fixed parameter model, where the glycolysis model of bloodstream form T. brucei is extended with the pentose phosphate pathway and an ATP:ADP antiporter over the glycosomal membrane.
Non-final version.

Fixed parameter model, where the glycolysis model of bloodstream form T. brucei is extended with the pentose phosphate pathway and a ribokinase in the glycosome.
Non-final version.

SBML models without activity of the glycolytic enzymes in the cytosol:

Glycolysis_noActivityInCytosol_1a.xml Model 1a
Glycolysis_noActivityInCytosol_1b.xml Model 1b
Glycolysis_noActivityInCytosol_2.xml Model 2
Glycolysis_noActivityInCytosol_3.xml Model 3
Glycolysis_noActivityInCytosol_4.xml Model 4
Glycolysis_noActivityInCytosol_5.xml Model 5
Glycolysis_noActivityInCytosol_6.xml Model 6

SBML models with activity of the glycolytic enzymes in the cytosol:

Glycolysis_withActivityInCytosol_1a.xm Model
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The zip folder contains files that allow simulation of stressosome dynamics. The models are based on a cellular automaton approach. Each protein of RsbR and RsbS is located in the crystal structure of the stressosome. The proteins can be phosphorylated or not and these states determine the future of neighbouring proteins. To simulate the model open the file 'liebal_stressosome-model_12_workflow-matlab.m' in Matlab. It is written in the cell-model, put the cursor into a cell that you wish to
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The model is adapted from A.P. Kupinski, I. Raabe, M. Michel, D. Ail, L. Brusch, T. Weidemann, C. Bökel (2013) Phosphorylation of the Smo tail is controlled by membrane localization and is dispensable for clustering, J. Cell Sci., 126, 20, 4684-4697 doi: 10.1242/​jcs.128926

The model format is MorpheusML that can readily be loaded and run in Morpheus: https://imc.zih.tu-dresden.de//wiki/morpheus

Morpheus is the modelling and simulation framework for multicellular systems biology developed at Technische Universität Dresden.
Manual, examples and binaries for Windows, Linux, MacOS at: https://imc.zih.tu-dresden.de/wiki/morpheus
Open source code at: https://gitlab.com/morpheus.lab/morpheus