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Toward Computational Identification of Multiscale “Tipping Points” in Acute Inflammation and Multiple Organ Failure

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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.

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Abbreviations

APRV:

Airway Pressure Release Ventilation

ARDS:

Acute Respiratory Distress Syndrome

AST:

Asparagine aminotransferase

DAMP:

Damage-associated molecular pattern molecule

DBN:

Dynamic Bayesian Network

DyNA:

Dynamic Network Analysis

FiO2 :

Fraction of inspired O2

IL:

Interleukin

MIST:

Minimally Invasive Suction and Treatment device

MODS:

Multiple Organ Dysfunction Syndrome

PaO2 :

Partial arterial O2 pressure

PEEP:

Positive end-expiratory pressure

SIRS:

Systemic Inflammatory Response Syndrome

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 UO1DK072146; National Institute on Disability and Rehabilitation Research grant H133E070024; National Science Foundation grant 0830-370-V601; a Shared University Research Award from IBM, Inc.; and grants from the Commonwealth of Pennsylvania, the Pittsburgh Lifesciences Greenhouse, and the Pittsburgh Tissue Engineering Initiative/Department of Defense.

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Authors and Affiliations

  1. Department of Surgery, University of Chicago, Chicago, IL, 60637, USA

    Gary An

  2. Department of Surgery, Upstate Medical University, Syracuse, NY, 13210, USA

    Gary Nieman

  3. Department of Surgery, University of Pittsburgh, W944 Starzl Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA, 15213, USA

    Yoram Vodovotz

  4. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA

    Yoram Vodovotz

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  1. Gary An
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  2. Gary Nieman
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  3. Yoram Vodovotz
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Correspondence to Yoram Vodovotz.

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Associate Editor Scott L. Diamond oversaw the review of this article.

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An, G., Nieman, G. & Vodovotz, Y. Toward Computational Identification of Multiscale “Tipping Points” in Acute Inflammation and Multiple Organ Failure. Ann Biomed Eng 40, 2414–2424 (2012). https://doi.org/10.1007/s10439-012-0565-9

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  • DOI: https://doi.org/10.1007/s10439-012-0565-9

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