Weaver, Jordan
(2022)
Mathematical Modeling and Machine Learning Guided Optimization to Characterize Immunoregulation during Respiratory Infection.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Respiratory viruses present major public health challenges, as evidenced by seasonal influenza’s 290,000 – 650,000 worldwide annual deaths, while the Severe Acute Respiratory Coronavirus 2 (SARS-CoV-2) has caused 6.31 million deaths worldwide. These viruses invoke excessive immune responses; however, the kinetics that regulate inflammatory responses within infected cells remain unresolved. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Computational models of the innate immune response to respiratory infections are designed to provide greater insights into the regulation of the immune system, which will likely provide insights into clinical treatments and the pathological understandings of the disease. Efforts to develop these models have greatly increased as RNA and protein level data have become widely available.
Aim 1 incorporates viral replication, cell death, interferon stimulated genes’ effects on viral replication, and demonstrating that RIG-I is robust to viral antagonism. Aim 2’s model is a spatialized, multicellular representation of RNA virus infection and type-I interferon-mediated antiviral response that model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus leads to plaque growth arrest. Aim 3 compares low-pathogenic H1N1 and high-pathogenic H5N1 influenza virus infections, suggesting that the production rate of interferon is the major driver of strain-specific immune responses. This rate difference may arise from the degree of antagonism of RIG-I by the invading virus. Aim 4 details an unbiased method to determine the minimum number of parameters which must vary to explain differences observed between two or more datasets using an extension of Aim 3.
A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains. As kinetics are host cell-specific, the model presented provides an important step to modeling the intracellular immune dynamics of many RNA viruses, including the viruses responsible for influenza and COVID-19. A visual summary of the work is given in Figure 1.
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| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
4 July 2022 |
| Defense Date: |
5 July 2022 |
| Approval Date: |
6 September 2022 |
| Submission Date: |
25 July 2022 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
184 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Chemical Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
systems biology
innate immunology
machine learning
model parameterization
interferon
viral respiratory infection |
| Date Deposited: |
06 Sep 2022 16:36 |
| Last Modified: |
06 Sep 2022 16:36 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/43364 |
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