Computational Modeling of Electrical Cell-to-cell Interactions in Cardiac Tissue: Applications to Model Parameter Selection and Pacemaker Function
Bibliographic record
Abstract
Cell-to-cell interactions are important in determining the electrophysiological behavior of cardiac tissue. In this research, computer modeling is used to investigate the importance of these interactions in two different contexts: 1) how to adjust parameters in single cell models to accurately reproduce tissue behavior, and 2) determining requirements for successful conduction at the interface between different tissue types, specifically from the sinoatrial node (SAN) to the atrium. Membrane resistance (Rm), the inverse of the slope of the current-voltage (I/V) relationship for a cardiac myocyte, is an important determinant of electrical cell-to-cell interactions. Experimentally, Rm can be measured by applying a small current and measuring the resulting change in membrane voltage. To investigate the importance of Rm, a multi-objective genetic algorithm approach was developed for enhancing the fitting of action potentials (APs) in single cell models. Rm was fit at several points during the AP along with AP morphology. The results demonstrate that including Rm as a fitting criterion yields improved convergence, reduced variability in parameter estimates, and improved robustness, while specifically improving the ability of the model to reproduce tissue behavior. Bioengineered pacemakers are cellular constructs intended to replace the SAN pacemaker function. The interface between the SAN and atrium appears to have features designed to facilitate conduction. Depending on the species, these features involve gradual transitions (gradients) in ion channel densities and coupling conductance, or insulating boundaries with conduction at discrete exit points only. We used simulations to determine the importance of each of these features, with the aim to provide guidance to future development of bioengineered pacemakers. We found that gradients in ionic conductance (specifically ICaL) are required in rabbit SAN. There is narrow range of coupling for which the SAN is able to propagate towards atrium without coupling gradients. In canine SAN, these gradients support conduction. However, gradients are not required, provided conduction from SAN to atrium is restricted to discrete exit points. This suggests two possible strategies for successful conduction at the interface between a bioengineered pacemaker and the atrium: 1) engineer the construct to have appropriate ionic current and intercellular coupling gradients, or 2) functionally insulate a homogeneous construct from the atrium with conduction only at discrete points.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".