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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.

Half of the global population is at risk for malaria, which results in nearly one million deaths annually, 86 % of which are in children [1]. Plasmodium falciparum, the most important human malaria parasite, is transmitted by female Anopheles mosquitoes. Parasite development in the mosquito begins with the ingestion of blood containing sexualstage gametocytes. Mobile ookinetes penetrate the midgut epithelium 24–36 h later and transform into midgut-bound oocysts within the open circulatory system of the mosquito. Oocysts grow and develop for 10–12 days and then release thousands of sporozoites, which invade the salivary glands and are released during later blood feeding by the mosquito.

Inflammation can drive both homeostasis and disease via dynamic, multiscale processes. The inflammatory response can be studied using multiplexed platforms, but there is no straightforward means by which to deal with the consequent “data deluge” in order to glean basic insights and clinically useful applications. Systems approaches, including data-driven and mechanistic computational modeling, have been employed in order to study the acute inflammatory response in the settings of trauma/hemorrhage and sepsis. Through combined data-driven and mechanistic modeling based on such “meso-dimensional” datasets, computational models of acute inflammation applicable to multiple preclinical species as well as humans were generated. A key hypothesis derived from these studies is that inflammation may be regulated via positive feedback loops that control switching between beneficial and detrimental inflammatory responses. Self-resolving inflammation may occur when specific signals feedback in a positive fashion to drive anti-inflammatory responses, while proinflammatory signals remain below certain thresholds. In contrast, self-amplifying, detrimental inflammation may occur when different signals feedback in a positive fashion to drive proinflammatory responses, setting in motion the positive feedback loop of inflammation → tissue damage/dysfunction → inflammation driven by damage-associated molecular pattern molecules. These insights may drive a future generation of targeted, personalized therapies for acute inflammation.