Investigation of host-pathogen interactions using individual-based modeling
In the present day, new standards grant diversely applicable experiments in the field of immunology and collected data becomes more easily available supporting manifold research objectives. Such gained data supports complex relations between pathogens and immunological responses which cannot be fully understood without quantitative analysis. Mathematical modeling represents the intersection of systems biology and bioinformatics and, therefore, is an ideal tool to investigate causal relationships in the data. Since more multiresistant pathogens are continuously detected, diseases caused by fungi and bacteria present a high risk for patients. Because of this, it is highly important to achieve a better understanding of the human immune system. This would give us the possibility to react in an appropriate manner by giving antibiotics and drugs to minimize damage and maximize the support for the patient’s immunological capabilities.
As part of this thesis, data from wet-lab experiments, such as infected samples of human whole-blood with Candida albicans, Candida glabrata and Staphylococcus aureus, serve as the basis for investigations done with a state-based model of whole-blood infections. In the context of this work, additional biological processes will be taken into account, depending on given experimental data, and the impact of immune deficiencies will be investigated. Furthermore, the model will be extended to a non-spatial agent-based model and optimized in its functionality and usability. The aim of this implementation is a deeper understanding of interacting microbiota as well as the modulation by therapeutical interventions.
(2018) Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood. Front Immunol 9, 560.
(2018) Quantitative Simulations Predict Treatment Strategies Against Fungal Infections in Virtual Neutropenic Patients. Front Immunol 9, 667.
Start of PhD
June 1, 2016