Data-driven Integration of Biological and In-silico Experiments for Precision Cardiology
Open Access
- Author:
- Kim, Haedong
- Graduate Program:
- Industrial Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- July 06, 2023
- Committee Members:
- Paul Griffin, Major Field Member
Keefe Manning, Outside Unit & Field Member
Ling Rothrock, Major Field Member
Hui Yang, Chair & Dissertation Advisor
Eric Bennett, Special Member
Steven Landry, Program Head/Chair - Keywords:
- Computational cardiology
Data assimilation
Ion channels
Virtual reality
Behavioral modeling - Abstract:
- Heart disease is a leading cause of death worldwide. It is a complex condition influenced by various risk factors and lifestyle choices. Animal models, such as transgenic mouse models, and experiments have been indispensable in studying cardiovascular diseases in lieu of the human heart. However, these physical models and experiments are not only expensive and ethically challenging but also are limited in their ability to rigorously determine underlying pathological features and mechanisms. Recent advancements in data analytics and digital technologies provide unprecedented opportunities to transform cardiology on many fronts, from basic research to education. The objective of this dissertation is to develop computer models and data assimilation methods to couple these in-silico models with data from in-vitro experiments on animal models. Specifically, molecular-level activities through ion channels in chronically glycosylation-deficient cardiac muscle cells are studied. Additionally, virtual reality (VR) learning environments and behavioral modeling methods for STEM education are studied. A series of this research will enable and assist in 1) integrative analyses of diseases-related perturbations in cardiac dysfunction, 2) guides to further physical experiments and pharmacological targets, and 3) human-centered learning experiences. The accomplishments are summarized as follows: 1)Simulation modeling of potassium channel Kv activities: A framework of simulation modeling of Kv kinetics in mouse ventricular myocytes and model calibration using the in-vitro data under normal and reduced N-glycosylation conditions through ablation of the MGAT1 gene. 2)Concurrent data assimilation method of in-vitro data: a novel concurrent data assimilation method that calibrates biophysics-based computer models to decompose and delineate kinetics of Kv isoforms with multiple voltage-clamp responses. 3) VR learning modules and modeling of learning behavior for human-centered learning: Immersive learning environments and sensor-based behavioral modeling for human-centered learning.