Models

README

This package contains code that generates and analyses tracks in 2D given three distinct condition; non-persistent, persistent and directed by the Ornstein-Uhlenbeck process. Full details on the analysis can be found in the article "Svensson et al., Untangling cell tracks: quantifying cell migration by time lapse image data analysis, Cytometry Pt A, 2017". The paper can be found on ...

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).

The state-based model (SBM) allow to simulate the interplay between innate immune cells, such as monocytes and PMN, and Candida albicans and to quantify immune reaction rates like phagocytosis and killing rates. It is implemented in C++.

DynaCoSys models the complement system by using a combination of Ordinary and Partial Differential Equations.

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 ...

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 ...

In this model we condider a B cell and its surrounding area and simulate movement and binding of receptors and ligands. The receptors can either be membrane bound (B cell receptors) or soluble (antibodies). Furthermore, receptors can either have a simple spherical shape or a more realistic Y-shape. This allows comparison of the binding dynamics with regard to the dimensionality of motion (membrane-bound vs. soluble), morphology and the binding valency.

This is the image analysis algorithm used in Cseresnyes, Kraibooj and Figge, Hessian-based quantitative image analysis of host-pathogen confrontation assays Cytometry A. 2018 Mar;93(3):346-356. doi: 10.1002/cyto.a.23201. Code is being maintained at https://github.com/applied-systems-biology/ACAQ3.

Read.me

This package contains code that implements the Hessian-based macrophage segmentation algorithm. In addition, the ...

Code for Bayesian analysis of pseudo-time resolved somatic hyper-mutations. Hirth et al., "Regulation of the germinal center reaction and somatic hypermutation dynamics by homologus recombination", Jounal of Immunology. 9, 2019, ji1900483; DOI: https://doi.org/10.4049/jimmunol.1900483

README

This package contains code and data that calulates and tests differences in realative mutation frequancy between Control and ...

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 ...

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.