Image‐based systems biology of infection

Abstract

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 imagebased 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. V C 2015 International Society for Advancement of Cytometry Key terms systems biology; image analysis; mathematical modeling; live-cell imaging; infection; host-pathogen interactions INFECTIOUS diseases still remain one of the main causes of death, especially in developing countries (1). To effectively treat infections, it is indispensable to understand how the pathogenic microorganisms interact with the host immune system. The mechanisms of these interactions can be extremely complex and also difficult to observe in experiment under physiological conditions. For example, if contacts between immune cells last longer than can be recorded in a typical microscopy study, this hinders a direct measurement of their duration and complicates interpretations (2). Computer simulations of immune processes can decrease the costs of systems biology studies by reducing the need for time-consuming experiments, expensive chemicals and animal testing. For instance, simulations by Beltman et al. (2,3) estimated the duration of contacts between T cells and dendritic cells, which are not accessible in laboratory experiments. Furthermore, the knowledge provided by dataderived computer models can direct further experimental studies. For example, the virtual infection model of a human whole blood assay by H€ unniger et al. (4) predicted the importance of extracellular killing of the pathogenic fungus Candida albicans by antimicrobial factors released from neutrophils during the first hour of infection, suggesting to study this mechanism in this particular time-frame. This iterative "experiment-modeling-experiment" cycle represents the central component of systems biology and aims to reveal the mechanisms of a biological process by quantifying the experimental data, building predictive com...

Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions

“…Other directions of future research include (i) focusing on conditions of immune dysregulation, (ii) comparing the impact of different pathogens, and (iii) including other types of innate immune cells. Furthermore, it is conceivable to combine modeling approaches with microscopy experiments of infection scenarios in vitro in an image-based systems biology approach (Mech et al, 2014; Figge and Murphy, 2015; Medyukhina et al, 2015). First steps into this direction have recently been made, e.g., by establishing algorithms for the automated image analysis of phagocytosis assays (Mech et al, 2011; Kraibooj et al, 2014) and for the automated tracking and classification of PMN from time-lapse microscopy (Mokhtari et al, 2013; Brandes et al, 2015) that was applied in the context of comparing C. albicans and C. glabrata infection (Duggan et al, 2015a).…”

“…However, SBM are in turn limited in that they do not represent spatial aspects. Individual-based models (IBM) such as cellular automata (CA) and agent-based models (ABM) do simulate discrete entities in space and time (Medyukhina et al, 2015). In a CA simulation, these entities can undergo state changes associated with their internal degrees of freedom as well as positional changes on a pre-defined spatial grid of computational cells (Von Neumann, 1951; Bittig and Uhrmacher, 2010).…”

“…For example, cells may be defined by parameters for size and shape as well as by parameters for interactions in the spatial environment that are associated with the typical frequency of interaction processes. Model parameters associated with dynamical, functional and morphological aspects of biological processes may be extracted from microscopic images by applying an image-based systems biology approach (Horn et al, 2012; Mech et al, 2014; Medyukhina et al, 2015). However, in many cases microscopy experiments cannot be performed for technical reasons, as is also the case for whole-blood infection assays where the majority of cells are erythrocytes blocking the view on leukocytes, let alone fungal pathogens that are present in even lower numbers.…”

Deciphering the Counterplay of Aspergillus fumigatus Infection and Host Inflammation by Evolutionary Games on Graphs

“…Furthermore, processes like phagocytosis of fungal cells by AM and PMN as well as recruitment of PMN could be quantified using fluorescence-activated cell sorting (FACS) at different time points and as a function of the infection-dose. Spatial-temporal insights from imaging experiments will support realistic mathematical modeling within the image-based systems biology approach11.…”

“…However, ODE assume spatially homogeneous environments, where the constituents occur in high concentrations, making them inappropriate for a realistic simulation of the infection-inflammation scenarios of A. fumigatus conidia in the lung. SBM and ABM allow for the stochastic simulation of biological systems at the level of single events1112. While SBM neglect spatial resolution, ABM simulate single individuals within a real spatial structure that may be represented on a grid or in continuous space.…”