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Cardiac Active Tension Modeling via Genetic Algorithm-Optimized Fractional Order Systems
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 mo...
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| Format: | Thesis |
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AUC Knowledge Fountain
2024
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| Summary: | 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. |
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