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Integrating Data-Driven and Mechanistic Models of the Inflammatory Response in Sepsis and Trauma

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Complex Systems and Computational Biology Approaches to Acute Inflammation

Abstract

Inflammation can drive both homeostasis and disease via dynamic, multidimensional, and multi-scale processes. The inflammatory response can be studied using multiplexed platforms for gathering biological data, 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 alone and in concert 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 direct or indirect positive feedback loops that control switching behavior between beneficial and detrimental inflammatory responses, with the former being self-resolving while the latter are self-sustaining, ultimately leading to multiple organ dysfunction and death. Self-resolving inflammation may occur when specific signals feedback in a positive fashion to drive anti-inflammatory responses, while pro-inflammatory signals remain below certain thresholds and within appropriate biological compartments. In contrast, self-amplifying, detrimental inflammation may occur when different signals feedback in a positive fashion to drive pro-inflammatory responses, setting in motion the positive feedback loop of inflammation → tissue damage/dysfunction → inflammation driven by damage-associated molecular pattern molecules. A third state that involves overly damped inflammatory responses and immunosuppression appears to drive organ dysfunction in a manner distinct from that observed in the context of overly amplified inflammation. These insights may drive a future generation of diagnostics and targeted, personalized therapies for acute inflammation in critical illness.

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Abbreviations

DAMP:

Damage-associated molecular pattern molecule

DBN:

Dynamic Bayesian network

GM-CSF:

Granulocyte-macrophage colony stimulating factor

IFN:

Interferon

IL:

Interleukin

IL-1ra:

Interleukin-1 receptor antagonist

IP-10:

IFN-γ-inducible protein of 10 kDa/CXCL10

MCP-1:

Monocyte chemotactic protein-1/CCL2

MIG:

Monokine induced by γ-interferon/CXCL9

MODS:

Multiple organ dysfunction syndrome

ODE:

Ordinary differential equation

PCA:

Principal component analysis

sIL-2rα:

Soluble IL-2 receptor α chain

SIRS:

Systemic inflammatory response syndrome

TBI:

Traumatic brain injury

TLR4:

Toll-like receptor 4

TNF-α:

Tumor necrosis factor-α

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Authors and Affiliations

  1. Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Yoram Vodovotz

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

    Yoram Vodovotz

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  1. Yoram Vodovotz
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Correspondence to Yoram Vodovotz .

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  1. Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Yoram Vodovotz

  2. Department of Surgery, University of Vermont, Burlington, VT, USA

    Gary An

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Vodovotz, Y. (2021). Integrating Data-Driven and Mechanistic Models of the Inflammatory Response in Sepsis and Trauma. In: Vodovotz, Y., An, G. (eds) Complex Systems and Computational Biology Approaches to Acute Inflammation. Springer, Cham. https://doi.org/10.1007/978-3-030-56510-7_4

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