# Models

**118**Models visible to you, out of a total of

**194**

SBML description of L. lactis glycolysis. Same as the uploaded Copasi file

**Creator: **Mark Musters

**Contributor**: Mark Musters

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

**Model format**: SBML

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

**Contributor**: Maksim Zakhartsev

**Model type**: Metabolic network

**Model format**: SBML

**Creators: **Jay Moore, David Hodgson, Veronica Armendarez, Emma Laing , Govind Chandra, Mervyn Bibb

**Contributor**: Jay Moore

**Model type**: Metabolic network

**Model format**: BioPAX

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

**Contributor**: Sebastian Curth

**Model type**: Algebraic equations

**Model format**: Matlab package

The model can simulate the the dynamics of sigB dependent transcription at the transition to starvation. It is was developed along the comic in <data> 'sigB-activation-comic_vol1'. Parameters were partly taken from Delumeau et al., 2002, J. Bact. and Igoshin et al., 2007, JMB. Parameter estimation was performed using experimental data from <assay> '0804_shake-flask'.

Use the .m-file with matlab as:

% reading initial conditions from the file:

inic = sigb_model_liebal;

% performing the

...

**Creator: **Ulf Liebal

**Contributor**: Ulf Liebal

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

**Model format**: Matlab package

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

**Contributor**: Ulf Liebal

**Model type**: Agent based modelling

**Model format**: Matlab package

**Creators: **Dawie Van Niekerk, Jacky Snoep

**Contributor**: Dawie Van Niekerk

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

**Model format**: Mathematica

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

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

**Contributor**: Franco Du Preez

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

**Model format**: Not specified

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

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

**Contributor**: Franco Du Preez

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

**Model format**: Not specified

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

**Contributor**: Franco Du Preez

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

**Model format**: Not specified

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

**Contributor**: Franco Du Preez

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

**Model format**: Not specified