Year-end Sale % OFF 
Abstract
Computer simulations of mathematical models open up the possibility of assessing hypotheses generated by experiments on pathogen immune evasion in human whole-blood infection assays. We apply an interdisciplinary systems biology approach in which virtual infection models implemented for the dissection of specific immune mechanisms are combined with experimental studies to validate or falsify the respective hypotheses. Focusing on the assessment of mechanisms that enable pathogens to evade the immune response in the early time course of a whole-blood infection, the least-square error (LSE) as a measure for the quantitative agreement between the theoretical and experimental kinetics is combined with the Akaike information criterion (AIC) as a measure for the model quality depending on its complexity. In particular, we compare mathematical models with three different types of pathogen immune evasion as well as all their combinations: (i) spontaneous immune evasion, (ii) evasion mediated by immune cells, and (iii) pre-existence of an immune-evasive pathogen subpopulation. For example, by testing theoretical predictions in subsequent imaging experiments, we demonstrate that the simple hypothesis of having a subpopulation of pre-existing immune-evasive pathogens can be ruled out. Furthermore, in this study we extend our previous whole-blood infection assays for the two fungal pathogens Candida albicans and C. glabrata by the bacterial pathogen Staphylococcus aureus and calibrated the model predictions to the time-resolved experimental data for each pathogen. Our quantitative assessment generally reveals that models with a lower number of parameters are not only scored with better AIC values, but also exhibit lower values for the LSE. Furthermore, we describe in detail model-specific and pathogen-specific patterns in the kinetics of cell populations that may be measured in future experiments to distinguish and pinpoint the underlying immune mechanisms.
Highlights
As integral part of the systems biology cycle, mathematical models based on experimental measurements allow unraveling the complex network of immune reactions during infections
Virtual infection modeling was realized in terms of a state-based model (SBM), where the states are occupied by the populations of essential immune cells, i.e. polymorphonuclear neutrophils (PMNs) and monocytes, as well as pathogens that can be either alive or killed and that are located either in extracellular space or within the immune cells
We performed whole-blood infection assays with either C. albicans or C. glabrata and observed that infection outcomes strongly depend on the pathogen [1, 2]
Summary
As integral part of the systems biology cycle, mathematical models based on experimental measurements allow unraveling the complex network of immune reactions during infections. We applied the iterative cycle between biological experiments and virtual infection modeling to unravel the immune response to fungal pathogens in human wholeblood infection assays [1,2,3]. Virtual infection modeling was realized in terms of a state-based model (SBM), where the states are occupied by the populations of essential immune cells, i.e. polymorphonuclear neutrophils (PMNs) and monocytes, as well as pathogens that can be either alive or killed and that are located either in extracellular space or within the immune cells. The biological processes that take place during infection are implemented by state transitions, which are characterized by transition rates In these SBMs, we defined rates for transitions representing phagocytosis of pathogens by PMNs and monocytes, intracellular and extracellular killing of pathogens as well as the acquisition of the immune-evasive state. We found that the SBM does mimic the infection dynamics in human whole-blood assays and enables quantitative predictions of treatment strategies against fungal pathogens for neutropenic patients [4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
