Approaches to the Calibration of Single-Cell Cardiac Models Based on Determinants of Multi-Cellular Electrical Interactions

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dc.contributor.advisorNygren, Anders
dc.contributor.authorPouranbarani, Elnaz
dc.contributor.committeememberRose, Robert
dc.contributor.committeememberBehjat, Laleh
dc.contributor.committeememberDi Martino, Elena
dc.contributor.committeememberDubljevic, Stevan
dc.date2021-06
dc.date.accessioned2021-03-19T18:14:03Z
dc.date.available2021-03-19T18:14:03Z
dc.date.issued2021-03-17
dc.description.abstractCardiac single-cell models are often used as building blocks for tissue simulation. Cellular models can successfully reproduce the expected behaviour at the tissue scale, providing that a single cell is accurately modeled. One of the imprecisions of conventional cellular modeling, evident mainly when the models are used at the tissue level, stems from only considering some cellular properties (e.g., action potential (AP) shape) and ignoring properties that reflect interconnections of the cells in the calibration/optimization process. This can result in inaccurate modeling of intercellular electrical communications. Computational models are used in a well-known Safety Pharmacology paradigm (i.e., the Comprehensive in Vitro Proarrhythmia Assay), the goal of which is to evaluate drug effects on the occurrence of proarrhythmia. So, accurate characterization of cellular models’ properties is of great importance. In this thesis, a cellular multi-objective optimization framework is proposed to consider the fitness of membrane resistance (Rm) (i.e., an indicator of cellular interconnection) in addition to AP as an additional optimization objective. As Rm depends on the transmembrane voltage (Vm) and exhibits singularities for some specific values of Vm, analyses are conducted to carefully select the regions of interest for the proper characterization of Rm. To verify the efficacy of the proposed problem formulation, case studies and comparisons are carried out using human cardiac ventricular models. Afterward, the performance of the framework proposed in the previous step is analyzed at the tissue-level using various tissue configurations: source-sink configuration, Purkinje-myocardium configuration, and transmural APD heterogeneity configuration. The comprehensive statistical analyses suggest that considering Rm in the calibration procedure results in a significant reduction of errors in cardiac tissue simulations. In the subsequent step, due to the variations among tissue simulations, it is proposed to include more essential properties to constrain the calibration problem. To achieve this, a machine learning-based approach is presented. The numerical results show that the proposed method efficiently estimates the base model’s parameters. Therefore, using a calibrated model as building blocks of tissue simulation yields accurate replication of reference behaviour at the tissue scale.en_US
dc.identifier.citationPouranbarani, E. (2021). Approaches to the Calibration of Single-Cell Cardiac Models Based on Determinants of Multi-Cellular Electrical Interactions (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/38680
dc.identifier.urihttp://hdl.handle.net/1880/113157
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectCardiacen_US
dc.subjectComputational Modelingen_US
dc.subjectCellular Modelingen_US
dc.subjectTissue Modelingen_US
dc.subjectMembrane Resistanceen_US
dc.subjectOptimizationen_US
dc.subjectMachine Learningen_US
dc.subject.classificationComputer Scienceen_US
dc.subject.classificationEngineering--Biomedicalen_US
dc.subject.classificationEngineering--Electronics and Electricalen_US
dc.titleApproaches to the Calibration of Single-Cell Cardiac Models Based on Determinants of Multi-Cellular Electrical Interactionsen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineEngineering – Electrical & Computeren_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
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