# Models

What is a Model?**395**Models visible to you, out of a total of

**637**

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson4) predicts the steady-state ...

**Creators: **Franco du Preez, Jacky Snoep, Dawie van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

The model includes glycolysis, pentosephosphate pathway, purine salvage reactions, purine de novo synthesis, redox balance and biomass growth. The network balances adenylate pool as opened moiety.

**Creator: **Maksim Zakhartsev

**Submitter**: Maksim Zakhartsev

**Model type**: Metabolic network

**Model format**: SBML

**Environment**: Copasi

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson1) predicts the limit ...

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson2) predicts the damped ...

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson3) predicts the steady-state ...

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez1) is the basis kinetic model derived from that published by Teusink et al., 2000 (PMID: 10951190).

**Creators: **Franco du Preez, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez2) is an oscillating version of the basis kinetic model (dupreez1) derived from that published by Teusink et al., 2000 (PMID: 10951190).

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez3) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes data for glycolytic intermediates in oscillating yeast cultures reported by Richard et al., 1996 (PMID: 8813760).

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez4) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes data for glycolytic intermediates in oscillating yeast cultures reported by Richard et al., 1996a (PMID: 8813760) as well as the rapid synchronization following the mixing of two yeast cultures that oscillate 180 degrees out of ...

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez5) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes the amplitude bifurcation of oscillating yeast cultures in a CSTR setup reported by Hynne et al., 2001 (PMID: 11744196).

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez6) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes data for glycolytic intermediates in cell free extracts of oscillating yeast cultures reported by Das and Busse, 1991 (PMCID: 1260073).

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. This model (dupreez7) is an oscillating version of the model published by Teusink et al., 2000 (PMID: 10951190), which describes the fluorescence signal of NADH in oscillating yeast cultures reported by Nielsen et al., 1998 (PMID: 17029704).

**Creators: **Franco du Preez, Jacky Snoep, David D van Niekerk

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

The zip file contains model files and an experiment file. Unpack it in a directory and navigate with matlab to there. Use the 'matlab_execution_guide.m' for simulation and visualisation of the model. This file is written in matlab cell mode, so it is not a stand alone function.

Three models have been developed to test their capacity to reproduce the experimental data from Study: 'Controlled sigmaB induction in shake flask' with Assay: 'IPTG induction of sigmaB in BSA115'. One model assumes a ...

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

**Model type**: Ordinary differential equations (ODE)

**Model format**: Matlab package

**Environment**: Matlab

input: array of investigated quenching temperatures and volumetric flows output: quenching time and coil length as function of quenching temperature, and quenching time as function of temperature for varying coil lengths

**Creator: **Sebastian Curth

**Submitter**: Sebastian Curth

**Model type**: Algebraic equations

**Model format**: Matlab package

**Environment**: Matlab

The kinetic model includes sugar uptake, degradation of glucose into pyruvate and the fermentation of pyruvate.

**Creator: **Jennifer Levering

**Submitter**: Jennifer Levering

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: JWS Online

The kinetic model includes sugar uptake, degradation of glucose into pyruvate and the fermentation of pyruvate.

**Creators: **Jennifer Levering, Mark Musters

**Submitter**: Jennifer Levering

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: JWS Online

The zip-folder contains files for execution in matlab that allow for the simulation of stressosome dynamics and reproduction of published data on the stressosome. The important file for execution is 'liebal_stressosome-model_12_workflow-matlab.m'.

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

**Model type**: Agent based modelling

**Model format**: Matlab package

**Environment**: Matlab

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. ...

**Creators: **Sara Jabbari, John King, Adrian Koerber, Paul Williams

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An ODE model representing the metabolic network governing acid and solvent production by Clostridium acetobutylicum (Haus et al. BMC Systems Biology 2011, 5:10), incorporating the effect of pH upon gene regulation and subsequent end-product levels. This model describes the third of four experiments in which the pH of the culture was shifted. For this experiment acidogenesis at pH 5.7 was maintained for 121 hours, after which the pH control was stopped, allowing the natural metabolic shift to the ...

**Creators: **Sara Jabbari, Sylvia Haus

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An ODE model representing the metabolic network governing acid and solvent production by Clostridium acetobutylicum (Haus et al. BMC Systems Biology 2011, 5:10), incorporating the effect of pH upon gene regulation and subsequent end-product levels. This model describes the last of four experiments in which the pH of the culture was shifted. For this experiment the pH shift was reversed compared to the first three (shift from pH 4.5 to 5.7), with the pH control switched off after 129 hours. ...

**Creators: **Sara Jabbari, Sylvia Haus

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An ODE model representing the metabolic network governing acid and solvent production by Clostridium acetobutylicum (Haus et al. BMC Systems Biology 2011, 5:10), incorporating the effect of pH upon gene regulation and subsequent end-product levels. This model describes the second of four experiments in which the pH of the culture was shifted. For this experiment acidogenesis at pH 5.7 was maintained for 137.5 hours, after which the pH control was stopped, allowing the natural metabolic shift to ...

**Creators: **Sara Jabbari, Sylvia Haus

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

An ODE model representing the metabolic network governing acid and solvent production by Clostridium acetobutylicum (Haus et al. BMC Systems Biology 2011, 5:10), incorporating the effect of pH upon gene regulation and subsequent end-product levels. This model describes the first of four experiments in which the pH of the culture was shifted. For this experiment acidogenesis at pH 5.7 was maintained for 137 hours, after which the pH control was stopped, allowing the natural metabolic shift to the ...

**Creators: **Sara Jabbari, Sylvia Haus

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

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, ...

**Creators: **Sara Jabbari, John Heap, John King

**Submitter**: Franco du Preez

**Model type**: Ordinary differential equations (ODE)

**Model format**: Not specified

**Environment**: JWS Online

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.

**Creators: **Eduard Kerkhoven, Fiona Achcar

**Submitter**: Eduard Kerkhoven

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: Copasi

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.

**Creators: **Eduard Kerkhoven, Fiona Achcar

**Submitter**: Eduard Kerkhoven

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: JWS Online

SBML file supplementary material of the publication.

**Creators: **Fiona Achcar, Barbara Bakker, Mike Barrett, Rainer Breitling, Eduard Kerkhoven

**Submitter**: Fiona Achcar

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: Not specified

The model describes the electron transport chain (ETC) of Escherichia coli by ordinary differential equations. Also a simplified growth model based on an abstract reducing potential describing the balance of electron donor (glucose) and electron acceptors is coupled to the ETC. The model should reproduce and predict the regulation of the described system for different oxygen availability within the aerobiosis scale (glucose limited continuous culture<=>chemostat). Therefore oxygen is changed ...

**Creator: **Sebastian Henkel

**Submitter**: Sebastian Henkel

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: JWS Online

A model of E. coli central carbon core metabolism, used as starting point for B. subtilis modelling. It is developed by Chassagnole et al. doi:10.1002/bit.10288.

**Creators: **Ulf Liebal, Fei He

**Submitter**: Ulf Liebal

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: Not specified

Simplified model of the electron-transport chain(s) (ETC) of Escherichia coli and its regulation by ArcA and FNR. The goal is to demonstrate a hypothetical design principle in the regulatory structure (->partly qualitative parameter values). Oxygen is changed slowly (100% aerobiosis at 1000000 time units) thus the basis variable is not the time but the oxygen flux voxi.

**Creator: **Sebastian Henkel

**Submitter**: Sebastian Henkel

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: Not specified

An ODE model representing the metabolic network governing acid and solvent production by Clostridium acetobutylicum, incorporating the effect of pH upon gene regulation and subsequent end-product levels.

The zip file containes 4 models (in SBML), each representing slightly different experimental conditions.

**Creators: **Sara Jabbari, Sylvia Haus

**Submitter**: The JERM Harvester

**Model type**: Ordinary differential equations (ODE)

**Model format**: SBML

**Environment**: Not specified