Models

127 Models visible to you, out of a total of 207

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

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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Creator: Hao Bai

Contributor: Hao Bai

Mathematica notebook for the parameterisation of the ALD rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Mathematica notebook for the ATPase reaction.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creator: Jacky Snoep

Contributor: Jacky Snoep

No description specified

Creator: Jacky Snoep

Contributor: Jacky Snoep

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

This is BIOMD0000000005.

Creators: Ron Henkel, Dagmar Waltemath

Contributor: Ron Henkel

BPG stability notebook

Creator: Jacky Snoep

Contributor: Jacky Snoep

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

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

only lacZ synthesis reduced by inhibitor in BSA115

Creator: Ulf Liebal

Contributor: Ulf Liebal

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

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

Creator: Ulf Liebal

Contributor: Ulf Liebal

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

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

Creator: Mark Musters

Contributor: Mark Musters

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

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

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

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

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

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

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

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

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

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: JERM

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

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

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

Contributor: Franco Du Preez

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

E.coli Core model, with additional reactions added to generate the beta-oxadation cycle. This is the basic model used in RobOKoD: microbial strain design for (over)production of target compounds (http://fairdomhub.org/publications/236).

Creator: Natalie Stanford

Contributor: Natalie Stanford

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

Mathematica notebook for the parameterisation of the ENO rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

Mathematical model for FBPAase kinetics, saturation with DHAP and GAP

Creator: Jacky Snoep

Contributor: Jacky Snoep

Mechanistical model of the catalytic cycle of Trypanothione Synthetase

Creators: Jurgen Haanstra, Alejandro Leroux

Contributor: Jurgen Haanstra

Mathematica notebook for the parameterisation of the G3PDH rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

Creator: Jacky Snoep

Contributor: Jacky Snoep

Mathematica notebook for the parameterisation of the GAPDH rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

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

Mathematica notebook for the parameterisation of the glucose transport rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Mathematica notebook for the glycerol transport rate equation.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creator: Robert Muetzelfeldt

Contributor: Robert Muetzelfeldt

No description specified

Creator: Jacky Snoep

Contributor: Jacky Snoep

No description specified

Creator: Matthias König

Contributor: Matthias König

Mathematical model for HK kinetics, GLC and ATP saturation, and inhibition with G6P and ADP.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Butanol producing iNS142, redesigned using RobOKoD.

Creator: Natalie Stanford

Contributor: Natalie Stanford

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Glycolytic model for Plasmodium falciparum; closed system

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Glycolytic model for Plasmodium falciparum; open system

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

No description specified

Creator: Nadine Veith

Contributor: Nadine Veith

Exponential decay model of gluconeogenic intermediates

Creator: Jacky Snoep

Contributor: Jacky Snoep

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

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

Mathematica notebook for the lactate transport rate equation, based on literature data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Mathematica notebook for the parameterisation of the LDH rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

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

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

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Metabolic model of Sulfolobus solfataricus P2 in the SBML (xml) and metano (txt, sce, fba) format. Scenarios are specific for growth on D-glucose or L-fucose as sole carbon source. Different theoretical routes of L-fucose degradation were modeled (E. coli-like, Xanthomonas-like and lactaldehyde-forming). Highest overall agreement between the model and experimental data was observed for the lactaldehyde-forming route.

Creators: Jacqueline Wolf, Helge Stark, Dietmar Schomburg

Contributor: Jacqueline Wolf

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

Creator: Jacky Snoep

Contributor: Jacky Snoep

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

SBML file supplementary material of the publication.

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

Contributor: Fiona Achcar

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

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

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

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

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

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

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

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

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

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

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

Mathematica notebook for the parameterisation of the PFK rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

Mathematica notebook for the parameterisation of the PGI rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

PGK 70C model, Fig 3 in manuscript

Creator: Jacky Snoep

Contributor: Jacky Snoep

PGK 70C SBML

Creator: Jacky Snoep

Contributor: Jacky Snoep

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

Creator: Jacky Snoep

Contributor: Jacky Snoep

Mathematica notebook for the parameterisation of the PGK rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

PGK yeast Fig1a

Creator: Jacky Snoep

Contributor: Jacky Snoep

PGK yeast with/without recycling

Creator: Jacky Snoep

Contributor: Jacky Snoep

PGK-GAPDH model Sulfolobus kouril8

Creator: Jacky Snoep

Contributor: Jacky Snoep

PGK-GAPDH model yeast kouril7

Creator: Jacky Snoep

Contributor: Jacky Snoep

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

Creator: Jacky Snoep

Contributor: Jacky Snoep

Mathematica notebook for the parameterisation of the PGM rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

Mathematica notebook for the parameterisation of the PK rate equation based on the experimental SEEK data set

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

No description specified

Creator: Paul Heusden

Contributor: JERM

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

Creators: Jennifer Levering, Mark Musters

Contributor: Jennifer Levering

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

Creator: Jennifer Levering

Contributor: Jennifer Levering

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

Mathematica notebook for the pyruvate transport rate equation, based on literature data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

Creator: Mark Musters

Contributor: Mark Musters

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

No description specified

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

Contributor: Jay Moore

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

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

No description specified

Creator: Jacky Snoep

Contributor: Jacky Snoep

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

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

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

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

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

No description specified

Creator: Malgorzata Adamczyk

Contributor: Malgorzata Adamczyk

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

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

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

Creator: Jacky Snoep

Contributor: Jacky Snoep

Mathematica notebook for the parameterisation of the TPI rate equation based on SEEK linked experimental data.

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

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

No description specified

Creators: Dawie Van Niekerk, Jacky Snoep

Contributor: Dawie Van Niekerk

The model presents a multi-compartmental (mesophyll, phloem and root) metabolic model of growing Arabidopsis thaliana. The flux balance analysis (FBA) of the model quantifies: sugar metabolism, central carbon and nitrogen metabolism, energy and redox metabolism, proton turnover, sucrose translocation from mesophyll to root and biomass growth under both dark- and light-growth conditions with corresponding growth either on starch (in darkness) or on CO2 (under light). The FBA predicts that
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Creators: Maksim Zakhartsev, Olga Krebs, Irina Medvedeva, Ilya Akberdin, Yuriy Orlov

Contributor: Maksim Zakhartsev

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