# 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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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

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

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

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

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 culturechemostat). Therefore oxygen is changed slowly

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**Creator: **Sebastian Henkel

**Contributor**: Sebastian Henkel

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

**Model format**: SBML

**Environment**: JWS Online

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

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