Sepsis: From Pattern to Mechanism and Back

Department of Surgery, University of Chicago, Chicago, IL 60637
Critical Reviews in Biomedical Engineering 11/2012; 40(4):341-351. DOI: 10.1615/CritRevBiomedEng.v40.i4.80
Source: PubMed


Sepsis is a clinical entity in which complex inflammatory and physiological processes are mobilized, not only across a range of cellular and molecular interactions, but also in clinically relevant physiological signals accessible at the bedside. There is a need for a mechanistic understanding that links the clinical phenomenon of physiologic variability with the underlying patterns of the biology of inflammation, and we assert that this can be facilitated through the use of dynamic mathematical and computational modeling. An iterative approach of laboratory experimentation and mathematical/computational modeling has the potential to integrate cellular biology, physiology, control theory, and systems engineering across biological scales, yielding insights into the control structures that govern mechanisms by which phenomena, detected as biological patterns, are produced. This approach can represent hypotheses in the formal language of mathematics and computation, and link behaviors that cross scales and domains, thereby offering the opportunity to better explain, diagnose, and intervene in the care of the septic patient.

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Available from: Rami A. Namas
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    • "Such analyses may suggest principal drivers of inflammation and MODS [54] [55] and may define the interconnected networks of mediators and signaling responses that underlie the pathobiology of acute critical illness [56] [57]. However, to gain mechanistic insights necessary for the rational design and development of therapeutics and potentially also for the next generation of diagnostic applications, a precise dynamic characterization of the cellular and molecular mechanisms responsible for generating the acute critical illness phenotype is required [58] [59] [60] [61]. A second area of active research involves data-based or datadriven modeling approaches that do not rely on a priori knowledge of the internal state of the system but rather on input-output data measured directly on the system [62] [63] [64]. "
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    ABSTRACT: The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches in order to address this unmet need. Two main paths of development have characterized the Society’s approach: i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiological signals or multivariate analyses of molecular and genetic data, and ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control, and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience, and the impact that merging these modeling approaches can have on general anesthesia.
    Full-text · Article · Aug 2014 · Journal of critical care
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    • "Sepsis is a systemic host response to infection with a clinical spectrum ranging from hemodynamic changes to multiple organ dysfunction syndrome and even death, and it is the leading cause of death in surgical intensive care unit patients [1,2]. A harmful host response to bacterial infection is believed to be the key origin of sepsis, in which invading bacteria and their products such as lipopolysaccharide (LPS) are potent activators of the inflammatory reaction [3]. The pathogenesis of sepsis involves a complex process of cellular activation at multiple levels resulting in release of pro-inflammatory cytokines such as TNF-α, IL-1β, high-mobility group box 1(HMGB1) and anti-inflammatory cytokines, such as IL-10. "
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    ABSTRACT: Introduction Glycyrrhizin (GL) was recently found to suppress high-mobility group box 1 (HMGB1)-induced injury by binding directly to it. However, the effect of GL on HMGB1 expression in endotoxemia as well as its underlying molecular mechanism remained unclear. Methods Twenty-one pigs were divided into four groups: sham group (n = 3), control group (n = 6), ethyl pyruvate group (n = 6) and glycyrrhizin group (n = 6). Pigs were anesthetized, mechanically ventilated, monitored and given a continuous intravenous infusion of lipopolysaccharide (LPS). Twelve hours after the start of the LPS infusion, ethyl pyruvate (30 mg/kg/hr) or glycyrrhizin (1 mg/kg/hr) was administered for 12 hours. Systemic and pulmonary hemodynamics, oxygen exchange, and metabolic status were measured. The concentrations of cytokines in serum and the corresponding gene and protein expressions in tissue samples from liver, lungs, kidneys, small intestine and lymph nodes were measured. Results GL maintained the stability of systemic hemodynamics and improved pulmonary oxygen exchange and metabolic status. GL also attenuated organ injury and decreased the serum levels of HMGB1 and other pro-inflammatory cytokines by inhibiting their gene and protein expression. Conclusions GL improved systemic hemodynamics and protected vital organs against porcine endotoxemia through modulation of the systemic inflammatory response. By reducing the serum level and gene expression of HMGB1 and other pro-inflammatory cytokines, GL may become a potential agent for the treatment of sepsis.
    Full-text · Article · Mar 2013 · Critical care (London, England)
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    ABSTRACT: Sepsis accounts annually for nearly 10% of total U.S. deaths, costing nearly $17 billion/year. Sepsis is a manifestation of disordered systemic inflammation. Properly regulated inflammation allows for timely recognition and effective reaction to injury or infection, but inadequate or overly robust inflammation can lead to Multiple Organ Dysfunction Syndrome (MODS). There is an incongruity between the systemic nature of disordered inflammation (as the target of inflammation-modulating therapies), and the regional manifestation of organ-specific failure (as the subject of organ support), that presents a therapeutic dilemma: systemic interventions can interfere with an individual organ system's appropriate response, yet organ-specific interventions may not help the overall system reorient itself. Based on a decade of systems and computational approaches to deciphering acute inflammation, along with translationally-motivated experimental studies in both small and large animals, we propose that MODS evolves due to the feed-forward cycle of inflammation → damage → inflammation. We hypothesize that inflammation proceeds at a given, "nested" level or scale until positive feedback exceeds a "tipping point." Below this tipping point, inflammation is contained and manageable; when this threshold is crossed, inflammation becomes disordered, and dysfunction propagates to a higher biological scale (e.g., progressing from cellular, to tissue/organ, to multiple organs, to the organism). Finally, we suggest that a combination of computational biology approaches involving data-driven and mechanistic mathematical modeling, in close association with studies in clinically relevant paradigms of sepsis/MODS, are necessary in order to define scale-specific "tipping points" and to suggest novel therapies for sepsis.
    Full-text · Article · Apr 2012 · Annals of Biomedical Engineering
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