Integrating data-driven and mechanistic models of the inflammatory response in sepsis and trauma

Yoram Vodovotz*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationComplex Systems and Computational Biology Approaches to Acute Inflammation
Subtitle of host publicationA Framework for Model-based Precision Medicine
PublisherSpringer International Publishing
Number of pages18
ISBN (Electronic)9783030565107
ISBN (Print)9783030565091
StatePublished - 4 Nov 2020
Externally publishedYes


  • Differential Equations
  • Dynamic Network Inference
  • Hierarchical Clustering
  • Machine Learning
  • Mathematical Model
  • Principal Component Analysis


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