Image data analysis and agent-based modeling of the spatio-temporal interaction between immune cells and human-pathogenic fungi
B4 focuses on the analysis and modeling of image data acquired within the CRC/TR FungiNet. This is accomplished by an image-based systems biology approach consisting of three steps: (i) automated image processing, (ii) derivation of quantitative measures, and (iii) construction of mathematical models to perform predictive computer simulations. The main objective is to advance this approach to investigate the interaction between host immune cells and human-pathogenic fungi, ultimately contributing to the development of a virtual infection model.
In the first funding period, we followed three lines of research: (i) Development of a pipeline for the automated analysis of microscopic images from endpoint experiments to investigate phagocytosis assays for Aspergillus fumigatus (collaboration with A1) and invasion assays for Candida albicans (C1). (ii) Computer simulation of virtual infection models for epithelial invasion of C. albicans (C1) and host-pathogen interactions in human whole-blood (C3). (iii) Mathematical modeling of A. fumigatus infection in the human lung by evolutionary game theory on graphs (A1, B1, C5).
Building on our achievements, in the second funding period we will design new tools and workflows to investigate morphological, functional and dynamic aspects of host-pathogen interactions. For example, the automated analysis of endpoint images will be developed into a comprehensive processing framework of time-resolved image data generated by live cell microscopy. We will also apply advanced computer simulations towards three main goals: (i) the generation of synthetic data representing a well-defined ground truth that we can use to validate our new algorithms for automated image analysis, (ii) the support of the design of experimental assays in order to avoid ambiguities, and (iii) the ability to identify mechanisms that govern the immune response in virtual infection scenarios.
Furthermore, we want to combine Image-based systems biology with the bioinformatics analysis of omics data in order to arrive at a more comprehensive view of infection scenarios across various scales.
|Prauße MTE, Lehnert T, Timme S, Hünniger K, Leonhardt I, Kurzai O, Figge MT||2018||Predictive virtual infection modeling of fungal immune evasion in human whole blood.||Front Immunol 9: 560||Frontiers|
|Timme S, Lehnert T, Prauße MTE, Hünniger K, Leonhardt I, Kurzai O, Figge M||2018||Quantitative simulations predict treatment strategies against fungal infections in virtual neutropenic patients.||Front Immunol 9: 667||Frontiers|
|Meinel C, Spartà G, Dahse H-M, Hörhold F, König R, Westermann M, Cseresnyés Z, Coldewey SM, Figge MT, Hammerschmidt S, Skerka C, Zipfel PF||2018||Streptococcus pneumoniae from patients with hemolytic uremic syndrome binds human plasminogen via the surface protein PspC and uses plasmin to damage human endothelial cells.||J Infect Dis 217: 358-70||PubMed|
|Cseresnyes Z, Kraibooj K, Figge MT||2018||Hessian-based quantitative image analysis of host-pathogen confrontation assays.||Cytometry A 93: 346-56||PubMed|
|Svensson C-M, Medyukhina A, Belyaev I, Al-Zaben N, Figge MT||2018||Untangling cell tracks: Quantifying cell migration by time lapse image data analysis.||Cytometry A 93: 357-70||PubMed|
|Schaarschmidt B, Vlaic S, Medyukhina A, Neugebauer S, Nietzsche S, Gonnert FA, Rödel J, Singer M, Kiehntopf M, Figge MT, Jacobsen ID, Bauer M, Press AT||2018||Molecular signatures of liver dysfunction are distinct in fungal and bacterial infections in mice.||Theranostics 8: 3766-80|
|Allert S*, Förster TM*, Svensson CM, Richardson JP, Pawlik T, Hebecker B, Rudolphi S, Juraschitz M, Schaller M, Blagojevic M, Morschhäuser J, Figge MT, Jacobsen ID, Naglik JR, Kasper L, Mogavero S, Hube B; *authors contributed equally||2018||Candida albicans-induced epithelial damage mediates translocation through intestinal barriers.||mBio 9: e00915-18||PubMed|
|Dasari P, Shopova IA, Stroe M, Wartenberg D, Dahse HM, Beyersdorf N, Hortschansky P, Dietrich S, Cseresnyés Z, Figge MT, Westermann M, Skerka C, Brakhage AA, Zipfel PF||2018||Aspf2 from Aspergillus fumigatus recruits human immune regulators for immune evasion and cell damage.||Front Immunol 9: 1635||Frontiers|
|Brandes S, Dietrich S, Hünniger K, Kurzai O, Figge MT||2017||Migration and interaction tracking for quantitative analysis of phagocyte-pathogen confrontation assays.||Med Image Anal 356: 172-83||PubMed|
|Lehnert T, Figge MT||2017||Dimensionality of motion and binding valency govern receptor-ligand kinetics as revealed by agent-based modeling.||Front Immunol 8: 1692||PubMed|
|Figge MT||2017||Systems Biology of Infection||NOVA ACTA LEOPOLDINA (ed.) Crossing Boundaries in Science. Documentation of the Workshop of the German National Academy of Sciences Leopoldina, Weimar, 06/30/2016-07/02/2016, 419, pp. 45-51. Wissenschaftliche Verlagsgesellschaft Stuttgart, Halle (Saale).||Leopoldina|
|Pollmächer J, Timme S, Schuster S, Brakhage AA, Zipfel PF, Figge MT||2016||Deciphering the counterplay of Aspergillus fumigatus infection and host inflammation by evolutionary games on graphs.||Sci Rep 6: 27807||PubMed|
|Buhlmann D, Eberhardt HU, Medyukhina A, Prodinger WM, Figge MT, Zipfel PF, Skerka C||2016||Complement factor H related protein 3 (FHR3) blocks C3d-mediated co-activation of human B cells.||J Immunol 197: 620-9||PubMed|
|Hünniger K, Bieber K, Martin R, Lehnert T, Figge MT, Löffler J, Guo RF, Riedemann NC, Kurzai O||2015||A second stimulus required for enhanced antifungal activity of human neutrophils in blood is provided by anaphylatoxin C5a.||J Immunol 194: 1199-210||PubMed|
|Brandes S, Mokhtari Z, Essig F, Hünniger K, Kurzai O, Figge MT||2015||Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.||Med Image Anal 20: 34-51||PubMed|
|Duggan S, Essig F, Hünniger K, Mokhtari Z, Bauer L, Lehnert T, Brandes S, Häder A, Jacobsen ID, Martin R, Figge MT, Kurzai O||2015||Neutrophil activation by Candida glabrata but not Candida albicans promotes fungal uptake by monocytes.||Cell Microbiol 17: 1259-76||PubMed|
|Lehnert T, Timme S, Pollmächer J, Hünniger K, Kurzai O, Figge MT||2015||Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.||Front Microbiol 6: 608||PubMed|
|Medyukhina A, Timme S, Mokhtari Z, Figge MT||2015||Image-based systems biology of infection.||Cytometry A 87: 462-70||PubMed|
|Kraibooj K, Schoeler H, Svensson CM, Brakhage AA, Figge MT||2015||Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm.||Front Microbiol 6: 549||PubMed|
|Mattern DJ, Schoeler H, Weber J, Novohradská S, Kraibooj K, Dahse HM, Hillmann F, Valiante V, Figge MT, Brakhage AA||2015||Identification of the antiphagocytic trypacidin gene cluster in the human-pathogenic fungus Aspergillus fumigatus.||Appl Microbiol Biotechnol 99: 10151-61||PubMed|
|Pollmächer J, Figge MT||2015||Deciphering chemokine properties by a hybrid agent-based model of Aspergillus fumigatus infection in human alveoli.||Front Microbiol 6: 503||PubMed|
|Hünniger K, Lehnert T, Bieber K, Martin R, Figge MT, Kurzai O||2014||A virtual infection model quantifies innate effector mechanisms and Candida albicans immune escape in human blood.||PLoS Comput Biol 10: e1003479||PubMed|
|Kraibooj K, Park HR, Dahse HM, Skerka C, Voigt K, Figge MT||2014||Virulent strain of Lichtheimia corymbifera shows increased phagocytosis by macrophages as revealed by automated microscopy image analysis.||Mycoses 57 Suppl 3: 56-66||PubMed|
|Mech F, Wilson D, Lehnert T, Hube B, Figge MT||2014||Epithelial invasion outcompetes hypha development during Candida albicans infection as revealed by an image-based systems biology approach.||Cytometry A 85: 126-39||PubMed|