Immunobiology and Infection Subgroup mini-symposium

Wednesday, June 16 at 11:30am (PDT)
Wednesday, June 16 at 07:30pm (BST)
Thursday, June 17 03:30am (KST)

SMB2021 SMB2021 Follow Wednesday (Thursday) during the "MS14" time block.
Note: this minisymposia has multiple sessions. The second session is MS13-IMMU (click here).

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Stanca Ciupe (Virginia Tech, United States), Jessica Conway (Penn State University, USA), Amber Smith (University of Tennessee Health Science Center, USA), Jonathan Forde (Hobart and William Smith Colleges, USA)


The Immunobiology and Infection Subgroup was created to bring together researchers in the SMB community who are interested in the modeling and analysis of immune processes in human disease and of host-pathogen interactions. Our broad objective is to discuss various topics including - within-host infectious diseases - host immune responses - causes and effects of inflammation - disease progression and outcome - integration of experimental and clinical data into models - model-driven experimental design In our 2021 mini-symposium, we will focus on infection and host immune responses to both infectious and non-infectious insults. We will have speakers with expertise in these areas. The talks will also showcase diverse modeling styles and integration with data.

Ivan Ramirez-Zuniga

(University of Tennessee Health Science Center, USA)
"A data-driven mathematical model of the role of energy in sepsis"
Mounting an adequate acute immune response against a pathogenic infection is energetically expensive. In an ideal scenario, this response may eradicate the infection but, in some cases, an imbalanced response may lead to sepsis. In this talk I will present a mathematical model that captures the dynamics of an immune response and its energy requirements to fight an infection. We calibrate our model with available animal data and identified key parameters for distinguishing between surviving and non-surviving subjects. On our analysis, we found that energy-related processes play a fundamental role in determining these outcomes. Moreover, we explore factors that modulate the inflammatory response across baseline and altered glucose conditions.

Sarah Minucci

(Virginia Commonwealth University, USA)
"Mathematical modeling of ventilator-induced lung inflammation"
Despite the benefits of mechanical ventilators, prolonged or misuse of ventilation may lead to ventilation-associated/ventilation-induced lung injury (VILI). Lung insults, such as respiratory infections and lung injuries, can damage the pulmonary epithelium, with the most severe cases needing mechanical ventilation for effective breathing and survival. Damaged epithelial cells within the alveoli trigger a local immune response. A key immune cell is the macrophage, which can differentiate into a spectrum of phenotypes ranging from pro- to anti-inflammatory. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes in the absence of evolving comorbidities, we mathematically modeled interactions between the immune system and site of damage while accounting for macrophage phenotype. We generated a collection of parameter sets with biologically feasible dynamics and used statistical methods and sensitivity analysis to hypothesize predictors of outcome and interventions for poor response to ventilation. Additionally, we analyzed macrophage phenotype using a system of ordinary differential equations and an agent-based model, both of which focused on the spectrum of macrophage activation on an individual cell level. Using both platforms, we tested different scenarios to examine macrophage response to damage.

Julia Arciero

(Indiana University-Purdue University Indianapolis, USA)
"Modeling novel immunoregulatory treatments for transplant patients"
Solid organ transplantation is a life-saving procedure that requires lifelong immunosuppression to prevent transplant rejection. Developing immunoregulatory treatments that minimize the need for chronic immunosuppression would be life-changing for transplant patients. Adoptive cell therapy with regulatory T cells (Treg) has emerged as a very promising approach, but there is limited understanding of the conditions that maximize Treg therapeutic effect. Mathematical modeling offers a unique and useful method for identifying cell therapy manipulations that would be most significant. This study introduces a mathematical model of transplant rejection that has been adapted to include adoptive transfer of Tregs with varied immunosuppression regimens. The model exhibits expected transplant behavior in the presence of immunosuppression, including graft acceptance with therapeutic levels of immunosuppression and graft rejection with subtherapeutic levels of immunosuppression. Preliminary results also indicate that combinatorial treatment strategies that incorporate adoptive transfer with subtherapeutic immunosuppression prolongs graft lifetime longer than either treatment in isolation. Ultimately, the model will be used to investigate optimal combinatorial dosing strategies that prevent graft rejection while minimizing immunosuppression. Modeling novel immunoregulatory treatments for transplant patients

Josua Aponte-Serrano

(Indiana University, USA)
"Integrating Validated Models of Viral Replication and Interferon Signaling into a Multi-Scale Spatial Framework to Identify Key Factors of Viral Infection Dynamics"
Multi-scale models are commonly used tools to address complex problems that span over multiple biological scales: from intracellular signaling and regulatory pathways to host-level systemic responses. We present a multi-scale spatial model of RNA viral replication and type-I interferon response in epithelial cells. The parameters of the models were identified using using both in vivo and in vitro data from Influenza A Virus (IAV). We show that, by following our cellularization workflow, we can integrate independently validated models into a multi-scale framework that reproduces the dynamics of each model subcomponent. By exploring the parameter space of this integrated model we identified factors that lead to viral plaque growth arrest such as modulation of the JAK-STAT pathway and differential propagation of the interferon signal and viral particles in the extracellular environment. Sensitivity analysis of the integrated model suggest that parameters associated with the interferon signaling pathways are identifiable under experimental conditions that inhibit virus growth. Finally, we should how this multi-scale model can be extended to incorporate additional aspects of the host-immune response to viral infection.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.