Ensemble Models of Neutrophil Trafficking in Severe Sepsis

Abstract

A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture)-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about of the treated population) that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental studies to advance understanding of the complex biological response to severe infection, a problem of growing magnitude in humans.

Early Diagnosis and Its Benefits in Sepsis Blood Purification Treatment

“…A significant advantage of having a virtual patient model for experiments is the possibility of testing different approaches for finding adequate therapies on the same virtual patient and comparing the outcomes. In order to follow a real-life scenario, virtual patient models are accompanied with well-defined constraints in therapy that are in accordance with clinical practice [2,8,9].…”

“…The mathematical model for inflammatory response to an infection is derived in [9]. A mathematical model defines the dynamics of concentration of 19 variables (states) among which 8 are observable (Lsel -Lselectin; HMGB1 -highmobility group protein B-1; CRT -creatinine; ALT -alanine aminotransferase; TNFα -tumor necrosis factor-α; IL-1 -interleukin-1β; IL-6 -interleukin-6; IL-10 -interleukin-10) and 11 are hidden (CLP -cecal ligation and puncture; B -bacteria; Nt -peritoneal neutrophil; Nr -resting blood neutrophil; Np -primed blood neutrophil; Na -activated blood neutrophil; PI -systemic proinflammatory response; AI -systemic antiinflammatory response; Ns -neutrophil sequestered in lung capillaries; Nl -lung neutrophil).…”

“…This mathematical model is based on the system of ordinary differential equations (ODE) whose details are presented in [9]. Since it models measurable concentrations of cytokines, the 19-states mathematical model are capable of simulating blood purification system by hemoadsoprtion device (a column packed with beads that adsorb cytokines).…”

“…The goal of purification is to remove harmful particles from patient blood, leading the patient to a healthy state. Preliminary studies on animal models indicate the success of blood purification techniques in sepsis treatment [9]. In conducted animal studies clinicians had full control of the exact time of sepsis induction.…”

“…Based on that information and theoretical assumptions about sepsis progress, clinicians determined start and duration of therapy. For example, in [9] the duration of therapy was fixed to 4 hours while the onset of therapy was set at 18 hours after sepsis induction (the clinicians' assumption was that after the 18 th hour sepsis would be fully manifested). The onset of therapy was the same for all animal models involved in the experiment, although more personalized approach to sepsis diagnosis would be far more effective.…”