Electrocardiography (ECG) records a complex curve composed of several waves (P, QRS and T) representing the electrical activity in the heart. Long QT syndrome (LQT) refers to a group of heart conditions and derives its name from the ECG interval ranging from the beginning of the QRS to the end of the T wave. Several LQT subtypes have been identified; together, the first three subtypes account for approximately 80% of the reported disease cases. Genetic mutations in cardiac ion channels have been identified as a cause of LQT. The details of how genetic and other elements interact to modulate the translation of genetic defects to lethal arrhythmias is not fully understood. The aim of this thesis is to develop computational biology techniques that facilitate the investigation of the (I) molecular properties of cardiac channel proteins and their role in the electrical behavior of cardiac cells and tissue, and (II) interplay of genetic and other factors in LQT arrhythmogenesis. Adopting an in silica approach, the particular objectives of this thesis are (1) to investigate the structure-function relationship of cardiac channel proteins; (2) to develop computational models correlating the molecular processes of cardiac channel proteins with the electrical dynamics of cardiac cells and tissue; (3) to investigate the role of genetic mutations in LQT arrhythmogenesis; (4) to investigate the interplay of genetic predisposition and other modifiers in the modulation of LQT arrhythmogenesis; and (5) to develop and evaluate mathematical and computational techniques facilitating multiscale modeling and simulation in biology. In addressing the listed aims and objectives, we have developed and evaluated a number of computational biology techniques in the stated areas. Studying the LQT with our tools on several levels of biological organization (cardiac ion channels, cells and tissue), we have generated a number of plausible hypotheses on the mechanisms underlying LQT.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:674919 |
| Date | January 2012 |
| Creators | Wang, Chong |
| Publisher | Ulster University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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