Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

Abstract

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

HMGB1 is a Central Driver of Dynamic Pro-inflammatory Networks in Pediatric Acute Liver Failure induced by Acetaminophen

“…We focused on HMGB1 as a proximal mediator of interest because of its known role as a driver of inflammation in models of APAP-induced liver injury 7 . As an extension of our previous work 8,9 , Translational Systems Biology modeling methodology developed for biologically complex and dynamic conditions 10,11 was applied to correlate biomarkers with clinical outcomes as well as identify potential therapeutic targets in PALF. Recapitulation of these findings using an in vitro model of isolated HC exposed to APAP ± NAC was used to create a bedside-to-bench translational platform for further investigation.…”

Toward a model-free feedback control synthesis for treating acute inflammation

“…The estimation of the fundamental quantity F in Equation (1) via the control and output variables u and y will be detailed in Section 2.2. It connects our approach to the data-driven viewpoint which has been adopted in control engineering (see, e.g., [39,42,67,68]) and in studies about inflammation (see, e.g., [9,20,69,84]).…”

Nested Active Learning for Efficient Model Contextualization and Parameterization: Pathway to generating simulated populations using multi-scale computational models

“…Sepsis in an inflammatory condition with a mortality rate of between 28%-50% (1). Numerous mechanistic computational simulations of acute inflammation and sepsis have been utilized over the past two decades (2)(3)(4)(5)(6)(7)(8)(9). These models have demonstrated that the sepsis population is much more heterogeneous than previously thought and this can be reflected by utilizing a range of multidimensional parameters that correlate to biologically plausible behaviors and phenotypes.…”