Developing a computational model to model cardiac activity has been increasingly important in recent decades. Accurate cell-level active tension modeling for cardiomyocytes is critical to understanding cardiac functionality on a patient-specific basis and developing an effective in-silico cardiac model. However, cell-level models in the literature fail to account for viscoelasticity and inter-patient variations in active tension. This research proposes a genetic algorithm-optimized, fractional order system to model cell-level active tension by extending Land’s state-of-the-art model of cardiac contraction. The model features the (left) Caputo derivative of six state variables that identify the mechanistic origins of viscoelasticity in a myocardial cell in terms of the thin filament, thick filament, and length-dependent interactions. This proposed Caputo Land System (CLS) model is the first of its kind for active tension modeling in cells and demonstrates notable patient-specificity, with smaller mean square errors than the reference model relative to cell-level experiments, promising greater clinical relevance than its counterparts in the literature.
School of Sciences and Engineering
Robotics, Control & Smart Systems Program
MS in Robotics, Control and Smart Systems
Committee Member 1
Anwar Abd Elnaser
Committee Member 2
Committee Member 3
Institutional Review Board (IRB) Approval
Not necessary for this item
(2024).Cardiac Active Tension Modeling via Genetic Algorithm-Optimized Fractional Order Systems [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Elhamshari, Afnan Khaled. Cardiac Active Tension Modeling via Genetic Algorithm-Optimized Fractional Order Systems. 2024. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
Available for download on Tuesday, July 23, 2024