This model is a variant of the previously developed SBM of whole-blood infection assay. While in the previous model a spontaneous immune evasion mechanism (A) was implemented in this model immune evasion was caused by PMN molecule secretion (B).
Creators: Teresa Lehnert, Maria T. E. Prauße
Contributor: Sandra Timme
Model type: Not specified
Model format: Not specified
Environment: Not specified
Organism: Candida albicans
Investigations: No Investigations
Studies: No Studies
Modelling analyses: No Modelling analyses
Adaptation of the previously developed virtual infection model of Aspergillus fumigatus in a *human* alveolus.
This model comprises a hybrid agent-based model of a single murine alveolus. The alveolus is represented in a realistic to-scale representation and contains the cell types of alveolar epithelial cells (AEC) of type 1 and 2 as well as the pores of Kohn (PoK).
Furthermore, in this model, depending on the infection dose multiple A. fumigatus conidium are inserted into the alveolus and the
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Creators: Sandra Timme, Marco Blickensdorf
Contributor: Sandra Timme
Model type: Agent based modelling
Model format: Not specified
Environment: Not specified
Organism: Aspergillus fumigatus
Investigations: No Investigations
Studies: No Studies
Modelling analyses: No Modelling analyses
This model comprises a hybrid agent-based model of a single human alveolus. The alveolus is represented in a realistic to-scale representation and contains the cell types of alveolar epithelial cells (AEC) of type 1 and 2 as well as the pores of Kohn (PoK). A single A. fumigatus conidium in inserted into the alveolus and the AEC, where the conidium is located secretes chemokines. Chemokine secretion is modelled using the partial differential equation of the diffusion equation and numerically
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Creator: Johannes Pollmächer
Contributor: Sandra Timme
Model type: Agent based modelling
Model format: Not specified
Environment: Not specified
Organism: Aspergillus fumigatus
Investigations: No Investigations
Studies: No Studies
Modelling analyses: No Modelling analyses
Dynamic optimization model to study control points in metabolic pathways to identify general strategies behind pathway regulation.
Toxic intermediates determine which enzyme control pathway flux and prevent their accumulation.
Therefore those enzymes are drug targets since deregulation leads to self-poisoning of pathogens.
Creator: Jan Ewald
Contributor: Jan Ewald
Model type: Ordinary differential equations (ODE)
Model format: Matlab package
Environment: Not specified
Organism: Not specified
Investigations: No Investigations
Studies: No Studies
Modelling analyses: No Modelling analyses
Primary hepatocytes were exposed to a stimulus by exchanging culture medium, thereby simulating changes in the blood composition. The expression of genes at different time points was recorded. Differentially expressed genes were clustered using fuzzy c-means algorithm into five groups. The arcs of the possible network were identified using the NetGenerator algorithm under the restriction of biological knowledge. The analysis was restricted to the main metabolic pathways of hepatocytes. The reverse
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Creators: Thomas Wolf, Wolfgang Schmidt-Heck, Reinhard Guthke
Contributor: Thomas Wolf
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: JWS Online
Organism: Not specified
Investigations: No Investigations
Studies: No Studies
Modelling analyses: No Modelling analyses
Abstract (Expand)
Authors: J. Pollmacher, Marc Thilo Figge
Date Published: 16th Jun 2015
Journal: Front Microbiol
PubMed ID: 26074897
Citation: Front Microbiol. 2015 May 28;6:503. doi: 10.3389/fmicb.2015.00503. eCollection 2015.
Abstract (Expand)
Authors: Teresa Lehnert, Marc Thilo Figge
Date Published: 19th Dec 2017
Journal: Front Immunol
PubMed ID: 29250071
Citation: Front Immunol. 2017 Nov 30;8:1692. doi: 10.3389/fimmu.2017.01692. eCollection 2017.
Abstract (Expand)
Authors: A. Tille, Teresa Lehnert, Peter Zipfel, Marc Thilo Figge
Date Published: 5th Oct 2020
Journal: Front Immunol
PubMed ID: 33013842
Citation: Front Immunol. 2020 Sep 2;11:1911. doi: 10.3389/fimmu.2020.01911. eCollection 2020.