Multi-scale modeling can predict factors driving the immune response and treatment in Tuberculosis

Abstract: Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the world’s deadliest infectious diseases and remains a significant global health burden even in the face of COVID. TB disease and pathology presents clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the immunobiology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. We have utilized a myriad of multi-scale approaches and analyses to study the host immune response to Mtb across multiple physiologic and time scales. The models we developed range from molecular scale to whole-host scale and we used a hybrid approach combining differential equations and agent-based modeling at multiple scales. We use extensive analyses to predict mechanisms in the system driving different outcomes via parameters. Additionally, a key goal is to improve antibiotic treatment for TB. Currently multiple drugs are used simultaneously over a 6-9 month period which is a burden on the patient and medical system. We use our models to predict regimens that can improve clinical treatment of TB.

Mathematics

Audience: general audience

Comments: See the event webpage at: math.vcu.edu/events/event-items/math-colloquium-multi-scale-modeling-can-predict-factors-driving-the-immune-response-and-treatment-in-tuberculosis.html

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VCU Mathematics and Applied Mathematics Colloquium Series

Series comments: A series of public lectures in Mathematics and Applied Mathematics from Virginia Commonwealth University.

To attend virtually, please connect via Zoom using the following coordinates:

Zoom Meeting ID: 822 7853 4531 Password: VCUMATH101

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