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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, 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.

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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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Authors and Affiliations

  1. Department of Computational and Systems Biology, University of Pittsburgh, 3088 BST 3, 3501 Fifth Ave, Pittsburgh, PA, 15260, USA

    Nabil Azhar B.S.

  2. Sports Medicine and Nutrition, University of Pittsburgh, 6072 Forbes Tower, 3600 Forbes Ave., Pittsburgh, PA, 15218, USA

    Qi Mi Ph.D.

  3. Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Ave, BST3 Suite 3076, Pittsburgh, PA, 15260, USA

    Cordelia Ziraldo Ph.D.

  4. University of Pittsburgh at Bradford, 300 Campus Drive, 103 D Fisher Hall, Bradford, PA, 16701, USA

    Marius Buliga Ph.D.

  5. Department of Mathematics, University of Pittsburgh, Thackeray Hall, Pittsburgh, PA, 15260, USA

    Gregory M. Constantine Ph.D.

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

    Yoram Vodovotz Ph.D.

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  1. Nabil Azhar B.S.
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  2. Qi Mi Ph.D.
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  3. Cordelia Ziraldo Ph.D.
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  4. Marius Buliga Ph.D.
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  5. Gregory M. Constantine Ph.D.
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  6. Yoram Vodovotz Ph.D.
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Correspondence to Yoram Vodovotz Ph.D. .

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Editors and Affiliations

  1. University of Pittsburgh Inflammation and Regenerative Modeling, Pittsburgh, Pennsylvania, USA

    Yoram Vodovotz

  2. University of Chicago Medical Center MC 5094, Chicago, Illinois, USA

    Gary An

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Azhar, N., Mi, Q., Ziraldo, C., Buliga, M., Constantine, G.M., Vodovotz, Y. (2013). 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, New York, NY. https://doi.org/10.1007/978-1-4614-8008-2_8

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