(184b) Machine Learning and Mechanism-Based Mathematical Modeling to Identify Biomarkers and Mechanisms behind Severe Pediatric Influenza Infection
AIChE Annual Meeting
2022
2022 Annual Meeting
Computing and Systems Technology Division
Area Plenary: Future Directions in Applied Mathematics and Numerical Analysis (Invited Talks)
Monday, November 14, 2022 - 3:55pm to 4:20pm
The overall goal of this project is to identify the underlying mechanisms and biomarkers associated with influenza severity in genetically diverse juvenile mice. In order to accomplish this objective, we apply machine learning algorithms such as Support Vector Machines (SVM) to blood and lung cytokine data, as well as flow cytometry inflammatory cell data, from juvenile mice to assess how well these biomarkers can distinguish between mild and severe disease outcomes. We hypothesize that pro-inflammatory markers, such as interleukin (IL) â 6, will be identified as key biomarkers since previous experimental studies have revealed a significant correlation between elevated levels of pro-inflammatory cytokines and severe influenza infection in both children and adults[7]. Biomarker predictions will be assessed using the receiver operating characteristic curve which assesses SVMâs ability to correctly classify juvenile mice into mild or severe outcome groups for different levels of prediction confidence.
While the ML will reveal new biomarkers to predict disease outcome, mechanisms-based mathematical modeling approaches are necessary to identifying the underlying mechanisms that promote severe infection. Non-linear ordinary differential equations (ODE) models are highly effective at characterizing complex biological systems and have been used to describe the dynamics of innate immune responses to influenza infection. Unfortunately, to our knowledge, there are no current ODE models that describe the differences of the immune response of juvenile mice that experience different levels of disease severity. We will use an ODE model trained to mouse data to determine the extent to which differences in initial cell counts and/or interaction rates contribute to immune response differences of mild and severe disease groups. After training the model to various cohorts (male/female, mild/severe outcome), we will conduct in silico experiments to determine how to best intervene to reduce viral titers and inflammation, thus improving future therapeutics.
References
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[2] Influenza (seasonal). (2018). Retrieved April 08, 2022, from https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal)
[3] Wong KK, et al. (2013) Influenza-associated pediatric deaths in the
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[4] Levy, O., Innate immunity of the newborn: basic mechanisms and clinical correlates. Nature Reviews Immunology, 2007. 7(5): p. 379-90.
[5] You, D., et al., Inchoate CD8+ T cell responses in neonatal mice permit influenza-induced persistent pulmonary dysfunction. Journal of Immunology, 2008. 181(5): p. 3486-94.
[6] Yu, J.S. and N. Bagheri, Multi-class and multi-scale models of complex biological phenomena. Current Opinion in Biotechnology, 2016. 39: p. 167-173.
[7] Hall, M.W., et al., Innate immune function and mortality in critically ill children with influenza: a multicenter study. Critical Care Medicine, 2013. 41(1): p. 224-36.