The pressing need for within-host models of the pulmonary immune response

Thursday, June 17 at 11:30am (PDT)
Thursday, June 17 at 07:30pm (BST)
Friday, June 18 03:30am (KST)

SMB2021 SMB2021 Follow Thursday (Friday) during the "MS20" time block.
Note: this minisymposia has multiple sessions. The second session is MS19-IMMU (click here).

Share this


Luis Sordo Vieira (Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, United States), Marissa Renardy (University of Michigan/Applied BioMath, United States), Tracy Stepien (Department of Mathematics, University of Florida, United States)


Respiratory pathogens and other pulmonary conditions will remain as leading threats to human health in the foreseeable future. The COVID-19 pandemic exemplified how mathematical models are integral to a quick and effective response to mitigating respiratory infections. Although many epidemiological models exist for the spread of disease, there is a need for within-host computational models of the immune response to respiratory pathogens that can be personalized to predict host-centric responses. This minisymposium will feature talks by clinicians discussing the leading problems in pulmonology, experimentalists discussing available tools for data acquisition including animal models, and computational modelers discussing models of the immune response to leading respiratory pathogens and related conditions.

Julie Leonard-Duke

(University of Virginia/Robert M. Berne Cardiovascular Research Center, United States)
"Computational Modeling of Fibroblast Subpopulations in Idiopathic Pulmonary Fibrosis"
Each year in this country, 40,000 patients are diagnosed with idiopathic pulmonary fibrosis (IPF), a progressive and terminal disease caused by excessive extracellular matrix production by fibroblasts in distributed lesions, or “fibrotic foci”, throughout the lung. Fibroblasts are the primary pathologic cell population in fibrosis and their presence has been shown to be essential for fibrotic foci formation. Their actions, such as proliferating, secreting collagen, or differentiation into myofibroblasts, is driven by a combination of mechanical and chemical cues that eventually lead to a pathologic phenotype in IPF. Recent literature suggests that there are sub-populations of fibroblasts in the lung that exhibit different phenotypes depending on chemical and mechanical signals present in their local environment. Understanding how fibroblast phenotypic heterogeneity contributes to fibrotic foci formation in the dynamic lung environment of progressive IPF is an overarching goal of our research team and has important implications in the design of new therapies for IPF. Our group has recently performed single-cell RNAseq analysis on human lung fibroblasts exposed to a combination of pro-inflammatory cytokines to recapitulate the IPF lung environment. This analysis has led to the identification of fibroblast sub-populations that may behave differently from one another in response to their local and changing environment. To better understand the consequences of these phenotypic differences on lung tissue remodeling, our team is combining data-driven analyses with multi-scale agent-based modeling that simulates intracellular signaling and multi-cell interactions to predict cell-specific behaviors that are crucial to the formation of fibrotic foci in IPF.

Amber M. Smith

(University of Tennessee Health Science Center, USA)
"Bacterial coinfections: from influenza to SARS-CoV-2"
Influenza virus infected individuals often become coinfected with a bacterial pathogen, which significantly enhances morbidity and mortality. These bacterial coinfections have contributed to 45-95% of mortality during influenza pandemics, and numerous host and pathogen mechanisms have been identified through various experimental and mathematical modeling approaches. Given the history of influenza-bacterial coinfections, this was an obvious fear for the ongoing SARS-CoV-2 pandemic. Thus far, there is some evidence that SARS-CoV-2 also increases susceptibility to bacterial infections but does so to a lesser extent compared to influenza. To better understand the potential for SARS-bacteria coinfection, we infected mice with SARS-CoV-2 followed by pneumococcus. Our data support clinical observations and highlight specific host responses that may play a role in the increased pathogenicity.

Elsje Pienaar

(Purdue University, United States)
"Mycobacterium avium infection in the lungs: and agent-based model exploring early infection events"
INTRODUCTION: Mycobacterium avium complex (MAC), members of the nontuberculous mycobacteria family, are environmental microbes, capable of colonizing and infecting humans following inhalation of the bacteria. MAC-pulmonary disease is notoriously difficult to treat and prone to recurrence, and both incidence and prevalence have been increasing [1]. There are two types of MAC lung infection – fibrocavitary and nodular, with fibrocavitary much harder to treat, and with much lower cure rates, as low as 76% even with optimal treatment [2]. MAC are well known to form biofilms and diverse colonies in the environment. These biofilms have been shown to aid in epithelial cell invasion [3], cause premature apoptosis in macrophages [4], and inhibit antibiotic efficacy [5]. We hypothesize that both phenotypic diversity and biofilm formation are key to establishing and prolonging infections in the lung. To address these hypotheses, we developed a model that shows the interactions between bacteria, biofilm and immune cells as an agent-based model (ABM). This model allows us to explore both the intracellular scale (bacterial phenotypes and macrophage killing), and tissue scale (biofilm formation and epithelial invasion). METHODS: We used Repast Simphony to develop a three-dimensional ABM of in vivo MAC colonization to infection within the first 14 days post-deposition. The grid represents a length of lung airway with a layer of mucus/epithelial lining fluid (ELF). Bacteria agents are divided into either sessile (slow-growing, within biofilm and less susceptible to antibiotics), or planktonic (more quickly growing but not protected by biofilm) phenotypes. Biofilm is represented by continuous variables in each grid compartment, with values corresponding to the amount of extracellular matrix produced by bacteria in that grid compartment. To represent the protective properties of biofilm, the amount of biofilm is inversely related to the likelihood of a macrophage phagocytosing bacteria from that biofilm. All bacterial agents also release a chemoattractant that is represented by continuous variables in each grid compartment, and that diffuses throughout the grid. Macrophages probabilistically follow this chemoattractant gradient. Macrophages can phagocytose bacteria, prioritizing planktonic bacteria (not within biofilms), which infect the macrophage. Infected macrophages then have a probabilistic chance of killing internal bacteria. Macrophages also accumulate “apoptotic signal” through exposure to biofilm and internal bacteria. RESULTS: The model was parameterized through a literature search, test cases based on in vitro experiments and Latin Hypercube Sampling for unknown parameter values. We found that parameters affecting macrophage chemotaxis and recruitment have significant impact on the number of macrophages, but not on the number or distribution of bacteria. Initial parameters – the initial bacteria count, initial macrophage count, and ratio of planktonic to sessile bacteria - have lasting impacts throughout the simulation. Parameters that pertain to only one bacterial subpopulation (e.g. extracellular growth rates) are not significantly correlated with outcomes overall, because the composition of the bacterial populations varies so much between simulations. Finally, we have found that biofilm increases the number of bacterial cells that invade the epithelium, but in the absence of biofilm bacteria are able to persist in the airways. Higher biofilm levels also increase macrophage chemo-attractant production, death and recruitment. The most significant biofilm parameter is the amount that is deposited with bacteria in the lung upon initial exposure. Our simulations indicate that, based on in vitro data, once bacteria are deposited in the lung they cannot generate biofilm quickly enough to have a significant an impact. CONCLUSIONS: We have developed a multiscale agent-based model that allows us to study the initial colonization and infection in MAC-pulmonary disease on both the cellular- and tissue level. Early results show that initial parameters have lasting effects on the outcome of the deposition. Further, we have found that biofilms are not necessary to establish fibrocavitary type of MAC infection. Future directions of this work include organization of the infection into nodules, adding drug pharmacokinetics and pharmacodynamics to better understand the role of bi¬¬ofilm in treatment efficacy. REFERENCES: 1. Lee, et al. Antimicrob Agents Chemother, 59(6): 2972-2977, 2015. 2. Hwang, et al. Eur Respir J, 49(3): 2017. 3. Yamazaki, et al. Cell Microbiol, 8(5): 806-814, 2006. 4. Rose and Bermudez. Infect Immun, 82(1): 405-412, 2014. 5. Falkinham. J Med Microbiol, 56(Pt 2): 250-254, 2007.

Angela Reynolds

(Virginia Commonwealth University, United States)
" Mathematical modeling of lung inflammation from insult to recovery"
Lung inflammation can be triggered by many insults including viral and bacterial infections, structural damage, or inhalation of dangerous particles. The associated lung injury can resolve quickly, be treated effectively through various interventions, become a chronic problem, or lead to death. Given the variety of possible responses, often seen from the same insult, and the necessity for the lungs to function effectively mathematical modeling has become a necessary tool for improving lung health. Researchers have used mathematical modeling to understand immune system dynamics during a number of pulmonary infections and injuries, identify key mechanisms, and provide important insights into new treatments and to help identify who needs an intervention. In this talk we will review and explore recent research in mathematical modeling of inflammation in the lung and look into how mathematical modeling and computational methods can be used to guide interventions.

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