Dissertation
PARAMETER ESTIMATION FOR PARTICLE TRANSPORT ACROSS BLOOD BRAIN BARRIER USING MACHINE LEARNING
Washington State University
Doctor of Philosophy (PhD), Washington State University
01/2022
DOI:
https://doi.org/10.7273/000004005
Handle:
https://hdl.handle.net/2376/119074
Abstract
Blood-brain barrier (BBB) is a unique structure in the central nervous system (CNS) that protects the brain from blood borne pathogens. Despites the protection of BBB, millions of people throughout the world are suffering from neurodegenerative disorders such as Alzheimer’s, Parkinson’s etc. In most cases, accumulation of excessive iron in the brain has been identified as the initiator of those diseases. However, why, and how excessive irons accumulate in the brain is still unknown. Therefore, in this dissertation, first, we explored the iron transport mechanism across the BBB under healthy and diseased conditions with mathematical models. Since mass action-based mathematical models are governed by various kinetic rates, in all models, we need to solve an inverse problem to find the kinetic parameters. Therefore, in this dissertation, we also explored various inverse techniques, which include least square methods, Bayesian inference, and artificial neural network decomposition. With experiments guided mathematical model, we have shown that, in healthy condition, transferrin and its receptors control the iron transport; while, in diseased conditions, lactoferrin and its receptors regulate the iron transport. Our results also show that a surge in lactoferrin receptor density increases the lactoferrin as well as iron in the brain. Since there is no feedback loop for lactoferrin, iron can transport to the brain continuously, which might increase brain iron to pathological levels and can contribute to neurodegeneration. Once the cause of neurodegeneration is known, we shift our focus to the cure of neurodegeneration. The biggest challenge for the treatment of neurodegeneration is the effective transport of therapeutics across BBB. We, therefore, developed a new drug carrier and studied its transport efficacy in an in-vitro BBB environment. Our model results indicate that exocytosis (exiting from BBB endothelium) of drug carrier is seven-fold slower than endocytosis (entering to BBB endothelium). This result suggests that that future studies should focus on enhancing the exocytosis process.
Metrics
3 File views/ downloads
30 Record Views
Details
- Title
- PARAMETER ESTIMATION FOR PARTICLE TRANSPORT ACROSS BLOOD BRAIN BARRIER USING MACHINE LEARNING
- Creators
- Aminul Islam Khan
- Contributors
- Prashanta Dutta (Advisor)Jin Liu (Committee Member)Yuehe Lin (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Mechanical and Materials Engineering, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 180
- Identifiers
- 99900882927601842
- Language
- English
- Resource Type
- Dissertation