Identifying important parameters in the inflammatory process with a mathematical model of immune cell influx and macrophage polarization

Abstract

In an inflammatory setting, macrophages can be polarized to an inflammatory M1 phenotype or to an anti-inflammatory M2 phenotype, as well as existing on a spectrum between these two extremes. Dysfunction of this phenotypic switch can result in a population imbalance that leads to chronic wounds or disease due to unresolved inflammation. Therapeutic interventions that target macrophages have therefore been proposed and implemented in diseases that feature chronic inflammation such as diabetes mellitus and atherosclerosis. We have developed a model for the sequential influx of immune cells in the peritoneal cavity in response to a bacterial stimulus that includes macrophage polarization, with the simplifying assumption that macrophages can be classified as M1 or M2. With this model, we were able to reproduce the expected timing of sequential influx of immune cells and mediators in a general inflammatory setting. We then fit this model to in vivo experimental data obtained from a mouse peritonitis model of inflammation, which is widely used to evaluate endogenous processes in response to an inflammatory stimulus. Model robustness is explored with local structural and practical identifiability of the proposed model a posteriori . Additionally, we perform sensitivity analysis that identifies the population of apoptotic neutrophils as a key driver of the inflammatory process. Finally, we simulate a selection of proposed therapies including points of intervention in the case of delayed neutrophil apoptosis, which our model predicts will result in a sustained inflammatory response. Our model can therefore provide hypothesis testing for therapeutic interventions that target macrophage phenotype and predict outcomes to be validated by subsequent experimentation.

Mathematical modeling of ventilator-induced lung inflammation

“…The spectrum of macrophage activation has been a recently growing field of research (Aggarwal et al, 2014;Mosser and Edwards, 2008;Torres et al, 2019). Mathematical models have studied a host of causes of lung inflammation including bacterial and viral infections and allergic reactions (Brown et al, 2011;Day et al, 2009;Manchanda et al, 2014;Smith et al, 2011).…”

“…Through mathematical modeling, we can understand more about the pulmonary immune response and how treatments can be most effective in combating damage to alveoli and immune cells. Towards this goal, we adapted a model developed by Torres et al for the innate immune response to bacteria, which accounts for macrophage polarization, to include epithelial dynamics and stretch-induced recruitment of immune cells (Torres et al, 2019). We use this model to understand the mechanisms by which the immune system responds to damaged epithelial cells and the sensitivity of post-ventilation outcome to components of this complex process.…”

A case for the reuse and adaptation of mechanistic computational models to study transplant immunology

“…The use of mechanistic computational models in transplant is lagging behind other immunology fields such as infection and cancer immunology (see for recent representative examples and for relevant reviews). Multiple processes are involved in the alloimmune response to a transplanted organ.…”

Investigating the impact of combination phage and antibiotic therapy: a modeling study

“…However, several of our parameter values are not experimentally measurable. To determine the relative effect of fluctuations in parameter values on the model output, we use Matlab and Simbiology to implement the model and run a sensitivity analysis (similar to the process described in [54]). Sensitivity analysis of parameters for our model will inform us about changes to which parameters would have the most affect on the model transients.…”