# Models

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

**447**

Simplified model file for PLaSMo accession ID PLM_9, version 1 (use simplified if your software cannot read the file, e.g. Sloppy Cell)

**Creators: **BioData SynthSys, Andrew Millar, Andrew Millar

**Contributor**: BioData SynthSys

**Model type**: Not specified

**Model format**: SBML

**Environment**: Not specified

Originally submitted model file for PLaSMo accession ID PLM_9, version 1

**Creators: **BioData SynthSys, Andrew Millar, Andrew Millar

**Contributor**: BioData SynthSys

**Model type**: Not specified

**Model format**: SBML

**Environment**: Not specified

Simplified model file for PLaSMo accession ID PLM_9, version 2 (use simplified if your software cannot read the file, e.g. Sloppy Cell)

**Creators: **BioData SynthSys, Andrew Millar, Andrew Millar

**Contributor**: BioData SynthSys

**Model type**: Not specified

**Model format**: SBML

**Environment**: Not specified

Originally submitted model file for PLaSMo accession ID PLM_9, version 2

**Creators: **BioData SynthSys, Andrew Millar, Andrew Millar

**Contributor**: BioData SynthSys

**Model type**: Not specified

**Model format**: SBML

**Environment**: Not specified

Originally submitted model file for PLaSMo accession ID PLM_74, version 1

**Creators: **BioData SynthSys, Yin Hoon

**Contributor**: BioData SynthSys

**Model type**: Not specified

**Model format**: Simile XML v3

**Environment**: Not specified

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

**Environment**: Not specified

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

**Environment**: Copasi

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

**Contributor**: Jay Moore

**Model type**: Metabolic network

**Model format**: BioPAX

**Environment**: Not specified

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

**Environment**: Matlab

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

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**Creator: **Ulf Liebal

**Contributor**: Ulf Liebal

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

**Model format**: Matlab package

**Environment**: Matlab

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

**Model type**: Not specified

**Model format**: SBML

**Environment**: JWS Online

This model describes a core process during endocytosis. Intracellular vesicles called early endosomes contain the endocytosed cargo, e.g. signaling components like growth factors and RTKs, pathogens like viruses and nutrients like iron in transferrin. Early endosomes form an interacting pool of thousands of vesicles and jointly constitute the sorting and transport machinery in the endocytic pathway. Together with the cargo, membrane components travel to other compartments of the pathway which

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**Creator: **Lutz Brusch

**Contributor**: Lutz Brusch

**Model type**: Partial differential equations (PDE)

**Model format**: SBML

**Environment**: Not specified

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

**Environment**: Matlab

Originally submitted model file for PLaSMo accession ID PLM_24, version 1

**Creators: **BioData SynthSys, Jonathan Massheder

**Contributor**: BioData SynthSys

**Model type**: Not specified

**Model format**: Simile XML v3

**Environment**: Not specified

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

**Contributor**: Dawie Van Niekerk

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

**Model format**: Mathematica

**Environment**: 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 (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

**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

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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

**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

**Contributor**: 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 (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

**Environment**: JWS Online