In partial fulfillment of the requirements for the degree of 

Doctor of Philosophy in Quantitative Biosciences

in the School of Physics

Guanlin Li

Defends his thesis:
OPTIMIZATION AND CONTROL OF VIRUS-HOST SYSTEMS

Thursday, May 20, 2021
12:00pm Eastern Time
Via BlueJeans: https://bluejeans.com/3040782917
Open to the Community

Advisor:

Dr. Joshua S. Weitz

School of Biological Sciences & School of Physics

Georgia Institute of Technology

Co-advisor:

Dr. Yao Yao

School of Mathematics

Georgia Institute of Technology

Committee Members:
Dr. Sam Brown; School of Biological Sciences, Georgia Tech
Dr. Yorai Wardi; School of Electrical and Computer Engineering, Georgia Tech
Dr. Kurt Wiesenfeld; School of Physics, Georgia Tech

Abstract:

Optimization and control are powerful tools to design a system that works as effectively as possible. In this thesis, we focus on applications of model-based optimization and control in complex virus-host systems at multiple scales. Viruses that infect bacteria, i.e., bacteriophage or ‘phage’, are increasingly considered as treatment options for the control and clearance of bacterial infections, particularly as compassionate use therapy for multi-drug resistant infections. Here, we evaluate principles underlying why careful application of multiple phage (i.e., a ‘cocktail’) might lead to therapeutic success in contrast to the failure of single-strain phage therapy to control an infection. We combine dynamical modeling of phage, bacteria, and host immune cell populations with control-theoretic principles (via optimal control theory) to devise phage cocktails and delivery schedules to control the bacterial populations. However, a risk in using cocktails of different phage is that bacteria could simultaneously develop resistance to all injected phage (i.e., selecting for multi-phage resistant). The next step is to understand how to pre-select phage that have adapted via co-evolution with bacterial strains and then to efficiently use these ‘future’ phage to clear the infection early on. In doing so, we develop the evolutionarily robust phage therapy in immunodeficient hosts given the infection networks that was identified in co-evolutionary training. Optimization and control not only can be applied to bacteria-phage-immune systems (i.e., at the microbial level) to help design phage therapy, but also can be applied to epidemiological systems (i.e., at the large-scale population level) to guide the development and deployment of efficient interventions. Lockdowns and stay-at-home orders have reduced the transmission of SARS-CoV-2 but have come with significant social and economic costs. Here, we describe a control theory framework combining population-scale viral and serological testing as part of an individualized approach to control COVID-19 spread. The aim is to develop policies for modulating individualize contact rates depending on both personalized disease status and the status of the epidemic at the population scale. Altogether in this thesis, we apply control strategies to alleviate the burden or spread of disease at multiple scales.