Abstract
The acute inflammatory response is a complex defense mechanism that has evolved to respond rapidly to injury, infection, and other disruptions in homeostasis. This robust responsiveness to biological stress likely endows the host with increased fitness, but over-robust or inadequate inflammation predisposes the host to various diseases. Importantly, well-compartmentalized inflammation is generally beneficial, but spillover of inflammation into the blood is a hallmark—and likely also a driver—of self-maintaining inflammation. The blood is also a key entry point and immunological interface for vectors of parasitic diseases, diseases that themselves incite systemic inflammation. The complex role of inflammation in health and disease has made this biological system difficult to understand comprehensively and modulate rationally for therapeutic purposes. Consequently, systems approaches have been applied in order to characterize dynamical properties and identify key control points in inflammation. This process begins with the collection of high-dimensional, experimental, and clinical data, followed by data reduction and data-driven modeling that finally informs mechanistic computational models for analysis, prediction, and rational modulation. These studies have suggested that the overall architecture of the inflammatory response includes a multiscale positive feedback from inflammation → tissue damage → inflammation, which is often inadequately controlled by negative feedback from anti-inflammatory mediators. Given the importance of the blood interface for the inflammatory response, and the accessibility of this compartment both as an immunological sampling reservoir for vectors as well as for diagnosis and therapy, we suggest that any rational efforts at modulating inflammation via the blood compartment must involve computational modeling.
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- ABM:
-
Agent-based model
- AsNOS:
-
Anopheles stephensi nitric oxide synthase
- DAMP:
-
Damage-associated molecular pattern molecule
- DBN:
-
Dynamic Bayesian Networks
- GMM:
-
Genetically modified mosquito
- MODS:
-
Multiple organ dysfunction syndrome
- ODE:
-
Ordinary differential equations
- PCA:
-
Principal component analysis
- RBM:
-
Rule-based model
- sTNFR:
-
Soluble tumor necrosis factor-α receptor
- TNF-α:
-
Tumor necrosis factor-α
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Acknowledgments
This work was supported in part by the National Institutes of Health grants R01GM67240, P50GM53789, R33HL089082, R01HL080926, R01AI080799, R01HL76157, R01DC008290, and UO1 DK072146; National Institute on Disability and Rehabilitation Research grant H133E070024; a Shared University Research Award from IBM, Inc.; and grants from the Commonwealth of Pennsylvania, the Pittsburgh Life Sciences Greenhouse, and the Pittsburgh Tissue Engineering Initiative/Department of Defense.
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Azhar, N., Vodovotz, Y. (2014). Innate Immunity in Disease: Insights from Mathematical Modeling and Analysis. In: Corey, S., Kimmel, M., Leonard, J. (eds) A Systems Biology Approach to Blood. Advances in Experimental Medicine and Biology, vol 844. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2095-2_11
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