# Models

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

**666**

In order to estimate whether Pi has an activatory or an inhibitory effect on the enzymes, the computed probe binding energies (from GRID results, Part 4) were compared with those for the LDH from L. plantarum whose activity is known to be unaffected by Pi.

The binding energies of the Pi probe in the allosteric binding site (AS) and the COO probe in the catalytic binding site (CS) of LDH from L. plantarum were defined as E¬AS,threshold and ECS,threshold, respectively. For the other LDH enzymes, ...

**Creator: **Anna Feldman-Salit

**Submitter**: Anna Feldman-Salit

**Model type**: Algebraic equations

**Model format**: Not specified

**Environment**: Not specified

The zip file contains two executable Matlab functions.

File named 'fnct_gen_tfcompmod.m' generates a Simbiology model based on the following interactions: R + X <-> RX -> R + X + Px R + Y <-> RY -> R + Y + Py R + Z <-> RZ -> R + Z + Pz Y + Pz -> Pz Px -> Py -> Pz ->

We assume much higher reaction speeds of sigma factor RNApol binding/unbinding compared to protein expression. Protein expression can therefore be represented by Michaelis-Menten like kinetic ...

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

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

**Model format**: Matlab package

**Environment**: Matlab

The model describes the catabolism of Escherichia coli and its regulation. The metabolic reactions are modeled by the thermokinetic model formalism. The model is simplified by assuming rapid equilibrium of many reactions. Regulation is modeled by phenomenological laws describing the activation or repression of enzymes and genes in dependence of metabolic signals. The model is intended to describe the behavior of E. coli in a chemostat culture in depedence on the oxygen supply.

The model is described ...

**Creators: **Michael Ederer, David Knies

**Submitter**: Michael Ederer

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

**Model format**: Mathematica

**Environment**: Not specified

Structural models of the LAB PYKs of L. lactis, L. plantarum, S. pyogenes and E. faecalis including the "best" docking solutions of potential allosteric ligands. The structures were derived by homology modeling based on the template of E. coli and B. stearothermophilus. PYK models and ligands are provided as .pdb files and can be displayed by using the program PyMOL, for instance.

**Creators: **Nadine Veith, Anna Feldman-Salit, Stefan Henrich, Rebecca Wade

**Submitter**: Nadine Veith

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

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

**Creator: **Jacky Snoep

**Submitter**: Jacky Snoep

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

**Model format**: SBML

**Environment**: JWS Online

Exponential decay model of gluconeogenic intermediates

**Creator: **Jacky Snoep

**Submitter**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

Mathematical model for TPI kinetics, GAP and DHAP saturation, and inhibition with 3PG and PEP.

**Creator: **Jacky Snoep

**Submitter**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

Mathematical model for GAPDH kinetics, BPG, NADPH, NADP, GAP and Pi saturation.

**Creator: **Jacky Snoep

**Submitter**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

Mathematical model for FBPAase kinetics, saturation with DHAP and GAP

**Creator: **Jacky Snoep

**Submitter**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

Mathematical model for PGK kinetics, ADP, ATP, 3PG and BPG saturation.

**Creator: **Jacky Snoep

**Submitter**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

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 ...

**Creator: **Fiona Achcar

**Submitter**: Fiona Achcar

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

**Model format**: SBML

**Environment**: Not specified

This ordinary-differential equation model is a spatially lumped model showing the behaviour of oxygen in the three compartments medium, membrane and cytoplasm and its impact on FNR inactivation, hereby showing the effects of different oxygen concentrations, diffusion coefficients and reaction rates. The model was created with the Matlab SimBiology toolbox.

**Creator: **Samantha Nolan

**Submitter**: David Knies

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

**Model format**: SBML

**Environment**: Not specified

This partial-differential equations model focuses on the oxygen gradients in consideration of the three-dimensional cell and environment.

**Creator: **Samantha Nolan

**Submitter**: David Knies

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

**Model format**: Mathematica

**Environment**: Not specified

Code for joint probabilistic inference of transcription factor behaviour and gene-transcription factor as well as metabolite-transcription factor interaction based on genome and metabolite data.

**Creators: **Botond Cseke, Guido Sanguinetti

**Submitter**: Botond Cseke

**Model type**: Not specified

**Model format**: Matlab package

**Environment**: Matlab

The model presents the response of E.coli to different levels of oxygen supply, in which the oxidases, Cyo and Cyd, and their regulators, FNR and ArcBA systems, are included. The initial file 0.xml and supporting documents are for the model with FNR only. Four 0.xml files provided are at AAU level 31, 85, 115 and 217 respectively. The ArcBA system can be activated by revising the number of agents, ArcB, ArcA dimer, ArcA monomer, ArcA tetramer and ArcA octamer, in the initial file. The model needs ...

only lacZ synthesis reduced by inhibitor in BSA115

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

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

**Model format**: SBML

**Environment**: JWS Online

The model file represents the expression of beta-gal from a sigB dependent promoter after sigb production was stimulated by IPTG. The model is based on an assumption that a hypothetical protein degrates the sigb factor.

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

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

**Model format**: SBML

**Environment**: JWS Online

This is a JWS model of the successful model for data representation. It realises regulation by a hypothetical sigB dependent protein that degrades beta-Gal.

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

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

**Model format**: SBML

**Environment**: JWS Online

The model represents a hypothetical situation in which an anti-sigmafactor reduces sigB efficacy.

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

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

**Model format**: SBML

**Environment**: JWS Online

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 ...

**Creator: **Ulf Liebal

**Submitter**: Ulf Liebal

**Model type**: Agent based modelling

**Model format**: Matlab package

**Environment**: Matlab

Bayesian model for inference of the activity of transcription factors from targets' mRNA levels. A standalone C sharp package (runs on linux and mac under MONO).

**Creator: **Guido Sanguinetti

**Submitter**: Guido Sanguinetti

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

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 ...

**Creators: **Thomas Millat, Graeme Thorn, Olaf Wolkenhauer, John King

**Submitter**: Thomas Millat

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

**Model format**: Matlab package

**Environment**: Matlab

The fitted function describes the pH-drop during 'forward'-shift experiments and the increase of the pH during 'reverse'-shift experiments. The estimated parameters are used to compute the changing pH level in the models of the pH.induced metabolic shift in continuous cultures under phosphate limitation of C. acetobutylicum. Furthermore, the parameters can be applied to join different independent experiments into a single data set.

To fit the changing pH level, an exponential function and a ...

**Creator: **Thomas Millat

**Submitter**: Thomas Millat

**Model type**: Not specified

**Model format**: Matlab package

**Environment**: Matlab

First darft of a model including glycolysis and the transcription and translation of the enzymes. See the datafile "Information on the darft transcription/translation model." for information.

**Creator: **Fiona Achcar

**Submitter**: Fiona Achcar

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

**Model format**: SBML

**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 (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