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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor: Levi Wood
Committee Members:
Dr. Alicia Lyle (CDC)
Dr. Melissa Kemp (Georgia Institute of Technology)
Dr. Manu Platt (Georgia Institute of Technology)
Dr. Srikant Rangaraju (Emory University)
Dr. Krishnendu Roy (Georgia Institute of Technology)
Temporal Regulation of Pro-Inflammatory Macrophage and Microglia Activation Dynamics Using Model Predictive Control
Today, chronic inflammatory diseases, including neurodegenerative conditions, cancer, and heart disease, account for more than 50% of deaths globally. There is thus an urgent need for novel strategies to treat such diseases. Alzheimer’s disease (AD) is an example of one such disease that presents both a significant challenge and urgent unmet need because there are no clinically approved treatments to stop or slow disease progression. Years of dysfunctional immune activity during the early stages of AD can create damaging cycles of inflammatory dysregulation and pathology accumulation. However, suppression of neural immune activity has not proven to be an effective strategy for AD treatment, perhaps in part because healthy immune function is increasingly recognized to involve a temporally dynamic process. Thus, a new method is needed to exert temporal control over dysregulated immune activity in AD and other currently intractable diseases. A computational model that captures dynamic immune activity could be used to predict and control the immune response under pathological conditions to recover the immune functionality of healthy tissues. In this thesis, I hypothesized that the application of engineering control theory tools could both quantitatively model immune system responses in disease environments and predict the temporal input trajectory, e.g. treatment regimen, needed to maintain a desired reference level of activity. Indeed, system models were identified that predicted stimuli sequences needed to sustain inflammatory activity and increase pathology clearance, which are lost in chronic disease. I demonstrated the controllability of macrophage pro-inflammatory activity and microglial uptake of the hallmark protein of AD, Aβ, in vitro. The results of these in vitro studies informed a temporal immunomodulation treatment strategy that I administered in vivo in a mouse model of AD. Preliminary in vivo results showed a recovery of homeostatic immune activity and reduced AD pathology. Future work will apply the model frameworks in vivo, further establishing dynamic, model-predicted immune modulation as a novel treatment strategy for AD and beyond.
