Understanding lung function and disease through mathematical modeling and experiment

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 MS15-CBBS (click here).

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Uduak George (San Diego State University, United States), Mona Eskandari (University of California Riverside, United Staes)


The COVID-19 pandemic and its impact on respiratory system has highlighted the exigent needs to research pulmonary mechanics. This mini-symposium aims to showcase some recent studies using mathematical models to examine lung development, normal lung function, diseased lung conditions and functional deterioration. These research explorations provide insights to diseases such as asthma, tuberculosis, cystic fibrosis and treatment of lung infection. They have also advanced pulmonary tissue characterization and understanding of age related alterations in lung function. Lung diseases often leads to reduced lung function and poor quality of life and mathematical models can provide excellent avenues to unravel the complex dynamics that orchestrate lung decline in various health conditions. The models employed in these works cut across different areas of mathematical and computational mathematics, including reaction-diffusion models, partial differential equation models, agent-based models, image-based analyses, mechanics, morphoelasticity etc.

Hannah Pybus

(School of Mathematical Sciences, University of Nottingham, United Kingdom)
"Airway constriction in asthma - is it the chicken or the egg?"
Despite its prevalence in the population, the causes of asthma remain poorly understood. Airway hyperresponsiveness causes airway constriction at low doses of agonist which is thought to activate cytokines, such as Transforming Growth Factor β (TGF-β). TGF-β is thought to play a key role in promoting airway remodelling, which in turn could exaggerate hyperresponsiveness in a positive feedback loop; however, it is not clear what comes first. To begin to elucidate this, our research combines mathematical models of contracting airways with ex vivo precision-cut lung-slice (PCLS) stretching experiments to study stress-driven TGF-β activation in asthmatic airways. In this talk, we describe our mathematical model that couples subcellular mechanotransductive signalling pathways to nonlinear hyperelastic models of airway mechanics to predict the levels of TGF-β activation in different experimental conditions. We account for TGF-β-mediated contraction of the airway smooth muscle and the subsequent change in effective mechanical properties of the PCLS as TGF-β activation progresses. In agreement with the experimental results, we find that TGF-β activation increases as the airway deforms with imposed stretch. Our work shows that airway contraction, induced by active TGF-β signalling, in conjunction with airway wall stiffening generates stress differences across the airway wall and consequently initiates a positive feedback loop of TGF-β activation. Our work gives access to the highly complex stress distribution within the airway wall and surrounding parenchyma that can be used to investigate the effects of contractile heterogeneity and examine airway wall structure. This integrated study provides information that is of vital importance in interpreting PCLS experiments that seek to clarify the mechanochemical mechanisms underpinning TGF-β activation, a key aspect of the disease, that has only recently received attention.

Ashley Schwartz

(Computational Science Research Center, San Diego State University, United States)
"New metrics for quantifying the spatial inhomogeneity of abnormal fluid in MR images of cystic fibrosis lungs"
Cystic fibrosis (CF) is a genetic disease that can produce thick mucus accumulation in the lung, limiting a person’s ability to breathe. Treatment plans for CF are often determined from disease severity as determined by the spirometry metric percent predicted forced expiratory volume in 1 second (ppFEV1). Spirometry does not yield information about mucus accumulation behavior and location that imaging may provide. Magnetic resonance (MR) imaging is an imaging technique with no radiation effects that yields information about fluid density, or water content, within the lung from blood, lung tissues, and lung abnormalities such as excess mucus. In this talk, we will present an automated image processing algorithm that makes use of three-dimensional MR images to locate, segment, and describe the lung abnormalities in CF versus control lungs. The spatial location and behavior of lung abnormalities is categorized into three different spatial behaviors: (i) generalized, (ii) localized diffuse, and (iii) localized. Lungs with generalized behavior have little but sparse abnormal lung fluid. Localized lungs have a focality or concentration of abnormal lung fluid in a particular region of the lung and sparsity elsewhere, while localized diffuse lungs have a high concentration of abnormal lung fluid in multiple regions. Control patients mostly presented as generalized. CF patient’s abnormal fluid behavior did not directly correlate with severity level as determined by ppFEV1. This suggests CF disease is heterogeneous within severity levels and ppFEV1 may be missing additional information about disease behavior. The algorithm developed provides unique information about abnormal lung fluid behavior that may be used to distinguish differences in CF disease missed by traditional spirometry metrics.

Nourridine Siewe

(Rochester Institute of Technology, United States)
"A Mathematical Model of the Role of MIF in Severe Malarial Anemia: What Happens in TB"
Tuberculosis (TB) is the leading cause of death by infectious disease worldwide. The pathogen responsible for this infection is Mycobacterium tuberculosis (MtB). Due to the large number of people affected by TB daily, it remains a public health concern because of lack of treatment options, causing scarcity of resources, and the abundance of drug-resistant TB strains. To assist in the fight against this disease, we propose building a mathematical model of the interactions between the human immune system and MtB. This model will be described by a system of ordinary differential equations to capture the complex interactions between the variety of cells and proteins involved in this biological system. The model will include the effect of commonly used drugs to treat TB, namely isoniazid and rifampin, whose pathways contribute in decreasing the number of MtB in the host. This model will allow for the quick and easy analysis of experimental TB treatments, expediting the process of developing new treatment protocols.

Blessing Emerenini

(Rochester Institute of Technology, United States)
"Trends in the mathematical modeling of Bacteria-Phage combat in lung treatment"
Presence of pathogenic microorganisms in our environment entail enormous problems for humans and livestock. The problem of pathogenic microrganisms is even grievous when they reside in host vital organs such as the lung. Bacteria is one of such pathogenic microorganisms and they prefer to live in communities called Biofilms. Existence of Biofilm in any system is a huge problem because by its nature it is usually difficult to get rid of it by mere antibiotics. There are currently many ongoing studies that focus on how to do away with such pathogens from our systems. One of the medical approaches to treating inhost bacteria infection is by introducing bacteriophages (a.k.a phage therapy). In order to understand the different strategies of pathogenic infections and phage-bacteria interactions, pathogen-host infection dynamics helps us to derive better treatments to extenuate infectious diseases or develop vaccinations, thus preventing the occurrence of infections altogether. In this study we present a general review of methods and characterizations to facilitate right decision for understanding interdisciplinary modeling approaches.

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