Image‐based systems biology of infection

The successful treatment of infectious diseases requires interdisciplinary studies of all aspects of infection processes. The overarching combination of experimental research and theoretical analysis in a systems biology approach can unravel mechanisms of complex interactions between pathogens and the human immune system. Taking into account spatial information is especially important in the context of infection, since the migratory behavior and spatial interactions of cells are often decisive for the outcome of the immune response. Spatial information is provided by image and video data that are acquired in microscopy experiments and that are at the heart of an image‐based systems biology approach. This review demonstrates how image‐based systems biology improves our understanding of infection processes. We discuss the three main steps of this approach—imaging, quantitative characterization, and modeling—and consider the application of these steps in the context of studying infection processes. After summarizing the most relevant microscopy and image analysis approaches, we discuss ways to quantify infection processes, and address a number of modeling techniques that exploit image‐derived data to simulate host‐pathogen interactions in silico. © 2015 International Society for Advancement of Cytometry

[1]  Sarah E Henrickson,et al.  Towards estimating the true duration of dendritic cell interactions with T cells. , 2009, Journal of immunological methods.

[2]  Marc Thilo Figge,et al.  Automated Characterization and Parameter-Free Classification of Cell Tracks Based on Local Migration Behavior , 2013, PloS one.

[3]  R. Guthke,et al.  Automated Image Analysis of the Host-Pathogen Interaction between Phagocytes and Aspergillus fumigatus , 2011, PloS one.

[4]  Myungjoo Kang,et al.  An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification , 2013, PloS one.

[5]  Marc Thilo Figge,et al.  Agent-Based Model of Human Alveoli Predicts Chemotactic Signaling by Epithelial Cells during Early Aspergillus fumigatus Infection , 2014, PloS one.

[6]  S. Bartnicki-Garcia,et al.  Computer simulation of fungal morphogenesis and the mathematical basis for hyphal (tip) growth , 1989, Protoplasma.

[7]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[8]  Michael Meyer-Hermann,et al.  Germinal center B cells govern their own fate via antibody feedback , 2013, The Journal of experimental medicine.

[9]  N. Hamilton,et al.  High-throughput quantification of early stages of phagocytosis. , 2013, BioTechniques.

[10]  I. Sbalzarini,et al.  Cell-Free Transmission of Human Adenovirus by Passive Mass Transfer in Cell Culture Simulated in a Computer Model , 2012, Journal of Virology.

[11]  D. Sherrington Stochastic Processes in Physics and Chemistry , 1983 .

[12]  Jacco van Rheenen,et al.  Tissue-resident memory CD8+ T cells continuously patrol skin epithelia to quickly recognize local antigen , 2012, Proceedings of the National Academy of Sciences.

[13]  S. Shorte,et al.  Quantitative four-dimensional tracking of cytoplasmic and nuclear HIV-1 complexes , 2006, Nature Methods.

[14]  Marc Thilo Figge,et al.  Stochastic discrete event simulation of germinal center reactions. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  P. Verveer,et al.  Quantitative microscopy and systems biology: seeing the whole picture , 2008, Histochemistry and Cell Biology.

[16]  Robert F Murphy,et al.  CellOrganizer: Image-derived models of subcellular organization and protein distribution. , 2012, Methods in cell biology.

[17]  K. Toellner,et al.  Toll-like receptor 4 signaling by follicular dendritic cells is pivotal for germinal center onset and affinity maturation. , 2010, Immunity.

[18]  Ivo F Sbalzarini,et al.  Modeling and simulation of biological systems from image data , 2013, BioEssays : news and reviews in molecular, cellular and developmental biology.

[19]  Huma Lodhi,et al.  Advances in Computational Systems Biology , 2010 .

[20]  Petros Koumoutsakos,et al.  A Stochastic Model for Microtubule Motors Describes the In Vivo Cytoplasmic Transport of Human Adenovirus , 2009, PLoS Comput. Biol..

[21]  C. Forst Host-pathogen systems biology. , 2006, Drug discovery today.

[22]  C. Skerka,et al.  Virulent strain of Lichtheimia corymbifera shows increased phagocytosis by macrophages as revealed by automated microscopy image analysis , 2014, Mycoses.

[23]  G. Gierz,et al.  Evidence that Spitzenkörper behavior determines the shape of a fungal hypha: a test of the hyphoid model. , 1995, Experimental mycology.

[24]  Teresa Lehnert,et al.  Epithelial invasion outcompetes hypha development during Candida albicans infection as revealed by an image‐based systems biology approach , 2014, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[25]  William J. Godinez,et al.  Objective comparison of particle tracking methods , 2014, Nature Methods.

[26]  Garry P Nolan,et al.  Chemical labeling strategies for cell biology , 2006, Nature Methods.

[27]  A Diaspro,et al.  Two-photon excitation of fluorescence for three-dimensional optical imaging of biological structures. , 2000, Journal of photochemistry and photobiology. B, Biology.

[28]  Rob J. De Boer,et al.  Chemotactic Migration of T Cells towards Dendritic Cells Promotes the Detection of Rare Antigens , 2012, PLoS Comput. Biol..

[29]  James Sharpe,et al.  A global “imaging’’ view on systems approaches in immunology , 2012, European journal of immunology.

[30]  J. Ellenberg,et al.  High-throughput fluorescence microscopy for systems biology , 2006, Nature Reviews Molecular Cell Biology.

[31]  Olaf Kniemeyer,et al.  Systems Biology of Fungal Infection , 2012, Front. Microbio..

[32]  Michael Meyer-Hermann,et al.  Germinal centres seen through the mathematical eye: B-cell models on the catwalk. , 2009, Trends in immunology.

[33]  Andrew G. Dempster,et al.  Analysis of infected blood cell images using morphological operators , 2002, Image Vis. Comput..

[34]  J. Rittscher Characterization of biological processes through automated image analysis. , 2010, Annual review of biomedical engineering.

[35]  Jeroen S. van Zon,et al.  A mechanical bottleneck explains the variation in cup growth during FcγR phagocytosis , 2009, Molecular systems biology.

[36]  J. Sharpe,et al.  Naive B-cell trafficking is shaped by local chemokine availability and LFA-1-independent stromal interactions. , 2013, Blood.

[37]  Scott E. Fraser,et al.  Imaging in Systems Biology , 2007, Cell.

[38]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

[39]  Steven L. Lytinen,et al.  Agent-based Simulation Platforms: Review and Development Recommendations , 2006, Simul..

[40]  Jose L. Segovia-Juarez,et al.  Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. , 2004, Journal of theoretical biology.

[41]  Stefan Schuster,et al.  Agent-Based Modeling Approach of Immune Defense Against Spores of Opportunistic Human Pathogenic Fungi , 2012, Front. Microbio..

[42]  Simeone Marino,et al.  Dendritic Cell Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis 1 , 2004, The Journal of Immunology.

[43]  J. Neumann The General and Logical Theory of Au-tomata , 1963 .

[44]  Abbas Shirinifard,et al.  Multi-scale modeling of tissues using CompuCell3D. , 2012, Methods in cell biology.

[45]  David M. Rubin,et al.  Automated image processing method for the diagnosis and classification of malaria on thin blood smears , 2006, Medical and Biological Engineering and Computing.

[46]  B. S. Manjunath,et al.  Biological imaging software tools , 2012, Nature Methods.

[47]  Mark J. Miller,et al.  Systems biology approaches for understanding cellular mechanisms of immunity in lymph nodes during infection. , 2011, Journal of theoretical biology.

[48]  Martin Meier-Schellersheim,et al.  Systems biology in immunology: a computational modeling perspective. , 2011, Annual review of immunology.

[49]  Peter M. A. Sloot,et al.  A simulation framework to investigate in vitro viral infection dynamics , 2013, Journal of Computational Science.

[50]  Kerstin Hünniger,et al.  A Virtual Infection Model Quantifies Innate Effector Mechanisms and Candida albicans Immune Escape in Human Blood , 2014, PLoS Comput. Biol..

[51]  Alexander G. Fletcher,et al.  Chaste: An Open Source C++ Library for Computational Physiology and Biology , 2013, PLoS Comput. Biol..

[52]  Mark J. Miller,et al.  Two‐photon microscopy of host–pathogen interactions: acquiring a dynamic picture of infection in vivo , 2009, Cellular microbiology.

[53]  F. Pineda,et al.  Kinetic modeling of Toxoplasma gondii invasion. , 2007, Journal of Theoretical Biology.

[54]  C. G. Reynaga-Peña,et al.  Analysis of the role of the Spitzenkörper in fungal morphogenesis by computer simulation of apical branching in Aspergillus niger. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[55]  Ting Zhao,et al.  Automated learning of generative models for subcellular location: Building blocks for systems biology , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[56]  Yasuhiro Suzuki,et al.  Evidence for Finely-Regulated Asynchronous Growth of Toxoplasma gondii Cysts Based on Data-Driven Model Selection , 2013, PLoS Comput. Biol..

[57]  Antonio Bru,et al.  Mathematical Modeling of Tuberculosis Bacillary Counts and Cellular Populations in the Organs of Infected Mice , 2010, PloS one.

[58]  Ellen A. Robey,et al.  Dynamic imaging of host–pathogen interactions in vivo , 2010, Nature Reviews Immunology.

[59]  Aabid Shariff,et al.  Automated Image Analysis for High-Content Screening and Analysis , 2010, Journal of biomolecular screening.

[60]  J. Goguen,et al.  No better time to FRET: shedding light on host pathogen interactions , 2010, Journal of biology.

[61]  Stefan Hoehme,et al.  A cell-based simulation software for multi-cellular systems , 2010, Bioinform..

[62]  J. Swanson,et al.  Live cell fluorescence microscopy to study microbial pathogenesis , 2009, Cellular microbiology.

[63]  Michael Meyer-Hermann,et al.  University of Birmingham Deriving a germinal center lymphocyte migration model from two-photon data , 2008 .

[64]  S. Shorte,et al.  Three-dimensional FRET reconstruction microscopy for analysis of dynamic molecular interactions in live cells. , 2008, Biophysical journal.

[65]  Gary An,et al.  Agent-based model of epithelial host-pathogen interactions in anastomotic leak. , 2013, The Journal of surgical research.

[66]  M. Meyer-Hermann,et al.  Modelling Intravital Two-Photon Data of Lymphocyte Migration and Interaction , 2011 .

[67]  Ting Song,et al.  A review of imaging techniques for systems biology , 2008, BMC Systems Biology.

[68]  A. F. Marée,et al.  Spatial modelling of brief and long interactions between T cells and dendritic cells , 2007, Immunology and cell biology.

[69]  Kerstin Hünniger,et al.  Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils , 2015, Medical Image Anal..

[70]  P. Liberali,et al.  Population context determines cell-to-cell variability in endocytosis and virus infection , 2009, Nature.

[71]  Nathalie Harder,et al.  A benchmark for comparison of cell tracking algorithms , 2014, Bioinform..

[72]  J. Petravic,et al.  Cell-autonomous and environmental contributions to the interstitial migration of T cells , 2010, Seminars in Immunopathology.

[73]  Michael C. Fu,et al.  Optimization via simulation: A review , 1994, Ann. Oper. Res..

[74]  F. Wouters,et al.  Imaging biochemistry inside cells. , 2001, Trends in cell biology.