# Models

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

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**Creators: **Michael Ederer, David Knies

**Contributor**: Michael Ederer

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

**Model format**: Mathematica

**Environment**: Not specified

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

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BPG stability notebook

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Mathematica

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

**Contributor**: 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

**Contributor**: Ulf Liebal

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

**Model format**: SBML

**Environment**: JWS Online

only lacZ synthesis reduced by inhibitor in BSA115

**Creator: **Ulf Liebal

**Contributor**: Ulf Liebal

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

**Model format**: SBML

**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

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**Creator: **Ulf Liebal

**Contributor**: Ulf Liebal

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

**Model format**: Matlab package

**Environment**: Matlab

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

**Creator: **Ulf Liebal

**Contributor**: Ulf Liebal

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

**Model format**: SBML

**Environment**: JWS Online

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

**Contributor**: Sebastian Henkel

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

**Model format**: SBML

**Environment**: Not specified

Here is a kinetic model (in COPASI format) of L. lactis glycolysis.

**Creator: **Mark Musters

**Contributor**: Mark Musters

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

**Model format**: Copasi

**Environment**: Copasi

This is a model about a ROS network that exhibits five design principles, and has been calibrated so as to predict quantitatively various steady state concentrations. 10191125.

Instructions

RUN the model for steady state.

For the Menadione experiment set the initial concentration of 'Menadione' species to experimental dosing i.e. 100 000 nM (0.1 mM) and make the simulation type "reaction" for both the species i.e. 'Menadione' and 'Menadione_internal'. Then run for 24 hr i.e. 1500 minutes approx.

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**Creators: **Hans Westerhoff, Raju Prasad Sharma, Alexey Kolodkin

**Contributor**: Hans Westerhoff

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

**Model format**: Copasi

**Environment**: Copasi

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

**Contributor**: David Knies

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

**Model format**: SBML

**Environment**: Not specified

Copasi file chronic inflammation Abulikemu et al 2020 (altered units TNF and MMP8); see supplemntal material:

TNF and MMP7 concentration upgrade of the models

All computations for the present paper were completed by using the model prepared and tested in Abulikemu et al 2018. Then, little attention was paid to the unit in which concentrations were expressed, except for the concentration of fibroblasts, which we found important for modelling the effect of confluency. This led to a predicted TNF

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**Creators: **Hans Westerhoff, Abulikemu Abudukelimu and Matteo Barberis

**Contributor**: Hans Westerhoff

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

**Model format**: Copasi

**Environment**: Copasi

Copasi file chronic inflammation Abulikemu et al 2020 (altered units TNF and MMP8); see supplemntal material:

TNF and MMP7 concentration upgrade of the models

All computations for the present paper were completed by using the model prepared and tested in Abulikemu et al 2018. Then, little attention was paid to the unit in which concentrations were expressed, except for the concentration of fibroblasts, which we found important for modelling the effect of confluency. This led to a predicted TNF

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**Creators: **Hans Westerhoff, Ablikim Abulikemu, Matteo Barberis

**Contributor**: Hans Westerhoff

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

**Model format**: Copasi

**Environment**: Copasi

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

**Contributor**: 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

**Contributor**: 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

**Contributor**: 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

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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

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

**Contributor**: 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

**Contributor**: 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

**Contributor**: Franco Du Preez

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

**Model format**: Not specified

**Environment**: JWS Online

The agent-based model involves the representation of each individual molecule of interest as an autonomous agent that exists within the cellular environment and interacts with other molecules according to the biochemical situation. FLAME environmet has beem used for agent-based development. The FLAME framework is an enabling tool to create agent-based models that can be run on high performance computers (HPCs). Models are created based upon extended finite state machines that include message input

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**Creator: **Afsaneh Maleki-Dizaji

**Contributor**: Afsaneh Maleki-Dizaji

**Model type**: Agent based modelling

**Model format**: Not specified

**Environment**: FLAME

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

**Contributor**: Fiona Achcar

**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

**Contributor**: The JERM Harvester

**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 (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

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**Creators: **Sara Jabbari, Sylvia Haus

**Contributor**: 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.

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**Creators: **Sara Jabbari, Sylvia Haus

**Contributor**: 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

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**Creators: **Sara Jabbari, Sylvia Haus

**Contributor**: 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

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**Creators: **Sara Jabbari, Sylvia Haus

**Contributor**: Franco Du Preez

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

**Model format**: Not specified

**Environment**: JWS Online

This function estimates the parameters of growth functions of the acid-forming and solvent-forming population observed in 'forward'-shift experiments of phosphate-limited continuous cultures of C. acetobutylicum. The parameters are used in the 'Two-Populations'-Model of the pH-induced metabolic shift.

It assumed that the found behaviour of the optical density during these experiments results from a phenotypic switch caused by the changing pH level.

**Creator: **Thomas Millat

**Contributor**: Thomas Millat

**Model type**: Not specified

**Model format**: Matlab package

**Environment**: Matlab

Mathematical model for FBPAase kinetics, saturation with DHAP and GAP

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

Mechanistical model of the catalytic cycle of Trypanothione Synthetase

**Creators: **Jurgen Haanstra, Alejandro Leroux

**Contributor**: Jurgen Haanstra

**Model type**: Linear equations

**Model format**: Copasi

**Environment**: Copasi

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

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

A reconstruction of the cellular metabolism of the opportunistic human pathogen Enterococcus faecalis V583 represented as stoichiometric model and analysed using constraint-based modelling approaches

**Creators: **Nadine Veith, Margrete Solheim, Koen Van Grinsven, Jennifer Levering, Jeroen Hugenholtz, Helge Holo, Ingolf Nes, Bas Teusink, Ursula Kummer, Brett G Olivier, Ruth Grosseholz

**Contributor**: Nadine Veith

**Model type**: Linear equations

**Model format**: SBML

**Environment**: Not specified

Preliminary metabolic network of S. pyogenes including primary metabolism, polysaccharide metabolism, purine and pyrimidine biosoynthesis, teichoic acid biosynthesis, fatty acid and phospholipid bioynthesis, amino acid metabolism, vitamins and cofactors. The model still needs to be validated.

**Creator: **Jennifer Levering

**Contributor**: Jennifer Levering

**Model type**: Metabolic network

**Model format**: SBML

**Environment**: Not specified

Model that can be used to obtain the figures of Abudulikemu et al 2018:

Abudukelimu, A., Barberis, M., Redegeld, F.A., Sahin, N., and Westerhoff, H.V. (2018). Predictable Irreversible Switching Between Acute and Chronic Inflammation. Front Immunol 9, 1596.

**Creators: **Hans Westerhoff, Ablikim Abudukelimu

**Contributor**: Hans Westerhoff

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

**Model format**: Copasi

**Environment**: Copasi

(Abudulikemu et al 2000 (also 2018) Standard model of acute mode Figure 32.

**Creators: **Hans Westerhoff, Ablikim Abudukelimu

**Contributor**: Hans Westerhoff

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

**Model format**: SBML

**Environment**: JWS Online

**Creator: **Nadine Veith

**Contributor**: Nadine Veith

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

**Model format**: SBML

**Environment**: Not specified

Exponential decay model of gluconeogenic intermediates

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**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

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**Creator: **Sebastian Henkel

**Contributor**: Sebastian Henkel

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

**Model format**: SBML

**Environment**: JWS Online

Batch and chemostat model of L lactis. Scope of the model is to provide a mechanistic explanation of the switch between mixed acid and homolactic fermentation.

**Creator: **Domenico Bellomo

**Contributor**: Domenico Bellomo

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

**Model format**: Matlab package

**Environment**: Matlab

The principles of Stealthy Engineering (Adamczyk et al.: Biotechnology Journal 2012; 7(7):877-83) are illustrated in this model by emulating a cross engineering intervention between L. lactis and S. cerevisiae.

The case study consists of replacing the native glucose uptake system of L. lactis with that native to the yeast S. cerevisiae. A modified version of Hoefnagel et al.’s model of L. lacrtis’ central metabolism was used as starting point. The total functional replacement of the PTS with the

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**Creators: **Malgorzata Adamczyk, Hans Westerhoff, Ettore Murabito

**Contributor**: Ettore Murabito

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

**Model format**: Copasi

**Environment**: Copasi

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

**Contributor**: Franco Du Preez

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

**Model format**: Not specified

**Environment**: JWS Online

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

**Contributor**: Franco Du Preez

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

**Model format**: Not specified

**Environment**: JWS Online

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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**Creator: **Sara Jabbari

**Contributor**: Sara Jabbari

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

**Model format**: Matlab package

**Environment**: Not specified

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

**Creator: **Peter Ruoff

**Contributor**: Peter Ruoff

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

**Model format**: Not specified

**Environment**: Not specified

SBML file supplementary material of the publication.

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

**Contributor**: Fiona Achcar

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

**Model format**: SBML

**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

**Contributor**: Jacky Snoep

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

**Model format**: SBML

**Environment**: JWS Online

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

**Contributor**: Thomas Millat

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

**Model format**: Matlab package

**Environment**: Matlab

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

**Contributor**: Eduard Kerkhoven

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

**Model format**: SBML

**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

**Contributor**: Eduard Kerkhoven

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

**Model format**: SBML

**Environment**: Copasi

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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**Creator: **Fiona Achcar

**Contributor**: Fiona Achcar

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

**Model format**: SBML

**Environment**: Not specified

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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**Creator: **Ulf Liebal

**Contributor**: Ulf Liebal

**Model type**: Agent based modelling

**Model format**: Matlab package

**Environment**: Matlab

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

**Creator: **Samantha Nolan

**Contributor**: David Knies

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

**Model format**: Mathematica

**Environment**: Not specified

PGK model for S. solfataricus

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

**Model type**: Algebraic equations

**Model format**: Mathematica

**Environment**: Mathematica

PGK 70C SBML

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: SBML

**Environment**: JWS Online

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

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

PGK yeast Fig1a

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Mathematica

PGK yeast with/without recycling

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: SBML

**Environment**: JWS Online

PGK-GAPDH model Sulfolobus kouril8

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: SBML

**Environment**: JWS Online

PGK-GAPDH model yeast kouril7

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: SBML

**Environment**: JWS Online

PGK-GAPDH models yeast and Sulfolobus Fig. 4 in manuscript

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Mathematica

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

**Contributor**: Nadine Veith

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

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

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**Creator: **Thomas Millat

**Contributor**: Thomas Millat

**Model type**: Not specified

**Model format**: Matlab package

**Environment**: Matlab

3D structure prediction of LDH enzymes from four LAB by comparative modeling against x-ray structure of LDH from B. stearothermophilis (template, PDB ID: 1LDN). The computation was performed with a protocol that uses "automodel.very_fast" settings of Modeller program (http://salilab.org/modeller/).

**Creator: **Anna Feldman-Salit

**Contributor**: Anna Feldman-Salit

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

Computation is performed for the modeled 3D structures of LDH enzymes (in PDB format) with the UHBD program, for pH 6 and pH 7.

**Creator: **Anna Feldman-Salit

**Contributor**: Anna Feldman-Salit

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

Comparison of electrostatic potentials within the allosteric binding sites of LDH enzymes to estimate the binding affinity of the FBP molecule is performed with the PIPSA program. The program uses the structure of enzymes in the PDB format and computed electrostatic potentials in the GRD format.

**Creator: **Anna Feldman-Salit

**Contributor**: Anna Feldman-Salit

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

Binding energies of phosphate ions to the allosteric and catalytic sites were estimated with a program GRID (http://www.moldiscovery.com/soft_grid.php). The calculations were performed for the modeled LDH structures from four LABs, at pH 6 and 7, in presence and absence of the FBP molecule. The phosphate ion was presented as a probe.

**Creator: **Anna Feldman-Salit

**Contributor**: Anna Feldman-Salit

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

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,

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**Creator: **Anna Feldman-Salit

**Contributor**: Anna Feldman-Salit

**Model type**: Algebraic equations

**Model format**: Not specified

**Environment**: Not specified

**Creator: **Paul Heusden

**Contributor**: The JERM Harvester

**Model type**: Not specified

**Model format**: Not specified

**Environment**: Not specified

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

**Creators: **Jennifer Levering, Mark Musters

**Contributor**: 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.

**Creator: **Jennifer Levering

**Contributor**: Jennifer Levering

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

**Model format**: SBML

**Environment**: JWS Online

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

Particularly figure 2 of of Abudulikemu et al 2020 in press

**Creator: **Hans Westerhoff

**Contributor**: Hans Westerhoff

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

**Model format**: Copasi

**Environment**: Copasi

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

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

...

**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

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

**Contributor**: Guido Sanguinetti

**Model type**: Not specified

**Model format**: Not specified

**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

**Contributor**: Botond Cseke

**Model type**: Not specified

**Model format**: Matlab package

**Environment**: Matlab

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

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

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

**Model format**: Mathematica

**Environment**: Not specified

**Creator: **Malgorzata Adamczyk

**Contributor**: Malgorzata Adamczyk

**Model type**: Not specified

**Model format**: SBML

**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 laws with three competing inhibitors

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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**: Not specified

**Environment**: Not specified

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

**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

**Contributor**: Ulf Liebal

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

**Model format**: SBML

**Environment**: Not specified

**Creator: **Jacky Snoep

**Contributor**: Jacky Snoep

**Model type**: Not specified

**Model format**: SBML

**Environment**: JWS Online

Here, we use hyperbolic tangents to fit experimental data of AB fermentation in C. acetobutylicum in continous culture at steady state for different external pHs. The estimated parameters are used to define acidogenic and solventogenic phase. Furthermore, an transition phase is identified which cannot be assigned to acidogenesis or solventogenesis.

Several plots compare the fits to the experimental data.

**Creator: **Thomas Millat

**Contributor**: Thomas Millat

**Model type**: Not specified

**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 '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 '0804_shake-flask'.

Use the .m-file with matlab as:

% reading initial conditions from the file:

inic = sigb_model_liebal;

% performing the simulation:

[t,y] =

...

**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