A robust multi-objective optimization framework to capture both cellular and intercellular properties in cardiac cellular model tuning: Analyzing different regions of membrane resistance profile in parameter fitting

Autor: Anders Nygren, Rodrigo Weber dos Santos, Elnaz Pouranbarani
Rok vydání: 2019
Předmět:
Optimization problem
Computer science
Physiology
Action Potentials
Multi-objective optimization
Mathematical and Statistical Techniques
Animal Cells
Medicine and Health Sciences
Myocytes
Cardiac
Peak Values
Numerical Analysis
Multidisciplinary
Mathematical model
Mathematical Models
Simulation and Modeling
Sorting
Curve Fitting
Electrophysiology
Membrane
Physical Sciences
Curve fitting
Medicine
Engineering and Technology
Cellular Types
Anatomy
Biological system
Algorithms
Interpolation
Research Article
Optimization
Science
Phase (waves)
Muscle Tissue
Neurophysiology
Research and Analysis Methods
Models
Biological
Membrane Potential
Genetic algorithm
Repolarization
Humans
Muscle Cells
Biology and Life Sciences
Cell Biology
Biological Tissue
Signal Processing
Mathematical Functions
Mathematics
Neuroscience
Zdroj: PLoS ONE
PLoS ONE, Vol 14, Iss 11, p e0225245 (2019)
ISSN: 1932-6203
Popis: Mathematical models of cardiac cells have been established to broaden understanding of cardiac function. In the process of developing electrophysiological models for cardiac myocytes, precise parameter tuning is a crucial step. The membrane resistance (Rm) is an essential feature obtained from cardiac myocytes. This feature reflects intercellular coupling and affects important phenomena, such as conduction velocity, and early after-depolarizations, but it is often overlooked during the phase of parameter fitting. Thus, the traditional parameter fitting that only includes action potential (AP) waveform may yield incorrect values for Rm. In this paper, a novel multi-objective parameter fitting formulation is proposed and tested that includes different regions of the Rm profile as additional objective functions for optimization. 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. Non-dominated sorting genetic algorithm II is utilized to solve the proposed multi-objective optimization problem. To verify the efficacy of the proposed problem formulation, case studies and comparisons are carried out using multiple models of human cardiac ventricular cells. Results demonstrate Rm is correctly reproduced by the tuned cell models after considering the curve of Rm obtained from the late phase of repolarization and Rm value calculated in the rest phase as additional objectives. However, relative deterioration of the AP fit is observed, demonstrating trade-off among the objectives. This framework can be useful for a wide range of applications, including the parameters fitting phase of the cardiac cell model development and investigation of normal and pathological scenarios in which reproducing both cellular and intercellular properties are of great importance.
Databáze: OpenAIRE
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