- computer simulation of virtual infection models with Candida albicans
- population-based, state-based and agent-based modelling of immune responses to C. albicans
- estimation of a priori unknown model parameter using global optimization algorithms
|since 2016||Postdoctoral researcher in the group Applied Systems Biology at Hans Knöll Institute|
|2017||PhD in Bioinformatics at the Friedrich Schiller University Jena|
|2011||Diploma in Bioinformatics at the Friedrich Schiller University Jena|
(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.
(2017) Dimensionality of motion and binding valency govern receptor-ligand kinetics as revealed by agent-based modeling. Front Immunol 8, 1692.
(2015) Neutrophil activation by Candida glabrata but not Candida albicans promotes fungal uptake by monocytes. Cell Microbiol 17(9), 1259-1276.
(2015) A second stimulus required for enhanced antifungal activity of human neutrophils in blood is provided by anaphylatoxin C5a. The Journal of Immunology 194(3), 1199-1210.
(2015) Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions. Frontiers in Microbiology 6(608), *authors contributed equally.
(2014) A virtual infection model quantifies innate effector mechanisms and Candida albicans immune escape in human blood. PLOS Comput Biol 10(2), e1003479, */⁺authors contributed equally.
(2014) Epithelial invasion outcompetes hypha development during Candida albicans infection as revealed by an image-based systems biology approach. Cytometry A 85(2), 126-139.