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REVIEW 4 major objections 5 minor 51 references

A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper reports that a population of 1,180 cell-specific action-potential models, each fitted to a single fluorescence voltage trace from a rabbit ventricular myocyte, reproduces measured biomarker values cell by cell, matching the…

desk verdict A genuinely scalable cell-by-cell fitting pipeline, but the 'random sample of phenotype' claim is not yet supported—the paper deserves serious review with a required fix. read the letter →

arxiv 2501.08356 v1 pith:HXH6X7P7 submitted 2025-01-14 q-bio.QM physics.data-anq-bio.CB

Radostin D. Simitev , Rebecca J. Gilchrist , Zhechao Yang , Rachel Myles , Francis Burton , Godfrey L. Smith This is my paper · ORCID
classification q-bio.QMphysics.data-anq-bio.CB
keywords cellularexcitabilityrabbitventricularmyocytesfluorescencevoltagemeasurementsactionpotentialwaveformparameterestimationpopulationofmodelsinter-cellvariabilitymaximumlikelihood
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper seeks to show that whole action-potential waveforms can be fitted at scale: for each of 1,228 rabbit ventricular myocytes, a standard ionic model is calibrated by adjusting eight ion-current parameters plus the measurement noise, producing 1,180 accepted cell-specific model variants. Earlier population approaches reproduced only aggregate biomarker histograms, but here every fitted model is tied to one recorded cell, and the resulting models match measured action-potential durations at 30%, 50% and 90% repolarisation on a cell-by-cell basis ($R^2 = 0.854$, $0.975$, $0.990$). The authors interpret the population of parameter estimates as a random sample from the phenotype of healthy rabbit ventricular myocytes and report that the parameters are only weakly correlated, so action-potential duration does not depend strongly on any single ion current. If the claim holds, the practical payoff is a route to cell-specific cardiac models for studying inter-cell variability, drug responses, and unmeasured quantities such as intracellular calcium.

What carries the argument

The machinery is a Gaussian maximum-likelihood fit of the Shannon et al. (2004) model: for each trace of 5,000 voltage samples the algorithm searches over nine quantities $\theta = (G_{\mathrm{Kr}}, G_{\mathrm{Ks}}, G_{\mathrm{K1}}, G_{\mathrm{tos}}, G_{\mathrm{CaL}}, G_{\mathrm{Clb}}, I_{\mathrm{NaK}}, I_{\mathrm{NaCa}}, \sigma)$ using the covariance-matrix-adaptation evolution strategy, then reports each estimate with a standard error from the Jacobian of the fit and accepts it when the chi-squared goodness-of-fit probability exceeds 0.3. The union of accepted point estimates is the population $M$. Because every accepted model is paired with one biological cell, this machinery is what converts raw fluorescence recordings into cell-by-cell biomarkers and parameter distributions, and it is the basis for all downstream statements about phenotype variability.

What would settle it

Refit a random sample of the 1,180 accepted cells with 600-conditioning-beat prepacing and compare the two parameter populations: if the prepaced estimates differ from the reported ones by the order of the mean ratios in Eq. (16) ($\lambda_{\mathrm{tos}} \approx 32$, $\lambda_{\mathrm{Ks}} \approx 0.2$, $\lambda_{\mathrm{Kr}} \approx 2.87$, $\lambda_{\mathrm{Clb}} \approx 0.519$), then the fitted population is protocol-dependent and the claim that it samples the healthy myocyte phenotype fails.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that maximum-likelihood estimation can recover a cell-specific version of the Shannon et al. (2004) rabbit ventricular myocyte model from a single noisy fluorescence voltage trace, and that doing this for thousands of cells yields a population that matches experiments both in aggregate and per cell. The estimands are the maximal conductances of $I_{\mathrm{Kr}}$, $I_{\mathrm{Ks}}$, $I_{\mathrm{K1}}$, $I_{\mathrm{tos}}$, $I_{\mathrm{CaL}}$ and $I_{\mathrm{Clb}}$, the maximal densities of $I_{\mathrm{NaK}}$ and $I_{\mathrm{NaCa}}$, and the noise standard deviation $\sigma$; all other model parameters and initial conditions stay at baseline. Fitting 1,228 cells and accepting 1,180 fits with goodness-of-fit $p > 0.3$ gives action-potential durations at 30%, 50% and 90% repolarisation with coefficients of determination $R^2 = 0.854$, $0.975$ and $0.990$ against experimental values. The paper validates the pipeline on synthetic data with known parameters and, for nine cells, by Bayesian posterior sampling. It concludes that the accepted population is a random sample from the healthy rabbit ventricular myocyte phenotype and that fitting entire action-potential waveforms at scale is feasible.

Load-bearing premise

The paper assumes that one beat per cell, recorded without first conditioning the model into a steady rhythm, contains enough information to identify each cell's ionic phenotype, even though its own re-fits with 600 conditioning beats change some parameters by factors from 0.2 to 32.

Editorial extensions

If this is right

  • Whole-waveform fitting at scale becomes feasible: roughly 30 minutes per cell on 96 threads, so populations of thousands of cell-specific models can be built from multi-cell recordings.
  • Population-level biomarker ranges and distributions are reproduced even for biomarkers such as the duration at 30% repolarisation that earlier histogram-calibration approaches did not target.
  • Each fitted model can predict unmeasured cellular quantities, including ionic current densities and intracellular calcium biomarkers that fall within published experimental ranges.
  • Weak correlations among the estimated parameters imply that action-potential duration is spread across multiple currents, so a single conductance will not explain most of the observed variability.
  • The synthetic-data and Bayesian checks give confidence that reported fits are accurate even where classical standard errors are conservative.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The prepacing check the paper reports (mean ratios $\lambda_{\mathrm{tos}} = 32$, $\lambda_{\mathrm{Ks}} = 0.2$, $\lambda_{\mathrm{Kr}} = 2.87$, $\lambda_{\mathrm{Clb}} = 0.519$) implies the fitted population may encode the single-waveform protocol as much as the cell phenotype, so the 'random sample from a healthy phenotype' reading should be verified before drug-response use.
  • A direct test would be to refit the same cells with a train of paced beats or with paired pre-drug/post-drug traces; if estimates shift by the reported factors, additional conditioning protocols are needed to separate protocol from phenotype.
  • The same pipeline could be applied to alternative rabbit myocyte models, and a model-selection comparison would show which parameter variations survive across model structures and which are artefacts of the chosen baseline.
  • The paired dofetilide-response measurements announced but not analysed here make this population a natural substrate for inferring drug pharmacodynamics once the initial-condition ambiguity is resolved.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper develops a maximum-likelihood pipeline for fitting the Shannon et al. (2004) rabbit ventricular myocyte action potential model to fluorescence voltage recordings, estimating eight scaled ionic conductances/pump currents plus a noise variance for each cell. The method is applied to 1228 myocytes, of which 1180 fits are accepted using a goodness-of-fit threshold p > 0.3, producing a population M of cell-specific model variants. The authors report population-level parameter distributions, pairwise correlations, marginal dependencies of APD biomarkers on parameters, and cell-by-cell agreement of APD30/APD50/APD90 (R² = 0.854, 0.975, 0.990). They interpret M as a random sample from the phenotype of healthy rabbit ventricular myocytes. Validation includes a synthetic-data recovery test, a Bayesian MAP comparison on nine cells, and deposition of code and data at Zenodo.

Significance. If the population-level interpretation is accepted, the paper would be a substantial step toward combining sample-specific and population-based modelling, showing that full action potential waveforms can be fitted at scale and that the resulting population reproduces measured biomarker distributions and individual-cell values. The paper has notable strengths: machine-checkable open code and data, a synthetic-data test with parameter recovery errors of order 1%, a Bayesian cross-check of point estimates, and a large experimental dataset. The central analytical claim, however — that M is a random sample from the healthy myocyte phenotype — is not currently supported, because the fits use a single non-prepaced action potential while the measurements are taken from prepaced cells, and the authors' own prepacing check shows shifts up to a factor of 32 in one estimated conductance. With the claim appropriately reframed or the protocol-dependence corrected, the contribution remains valuable for computational electrophysiology.

major comments (4)
  1. [§3.5, Eq. (16)] The prepacing check directly undermines the abstract's central claim that M is 'a random sample from the phenotype of healthy rabbit ventricular myocytes.' The experimental cells are field-stimulated for five minutes at 2 Hz before recording (§2.2), but the fits start from baseline Shannon initial conditions and use a single non-prepaced AP (§2.3–2.4). The authors' own re-fits of nine cells with 600-beat prepacing give mean ratios λ_tos = 32, λ_Ks = 0.2, λ_Kr = 2.87, λ_Clb = 0.52, λ_NaK = 0.25. A factor of 32 on G_tos means the non-prepaced estimates are largely absorbing the initial-condition transient rather than describing a steady-state cell phenotype. The proposed multiplicative correction assumes these ratios are cell-independent, but they are estimated from nine hand-picked cells chosen 'so as to have AP waveforms ranging from relatively short to relatively long,' which is not a random sample and cannot support a population-wide correction. All distributional statements (Table 1, Figs. 5–7) and forward predictions inherit this problem. I request either a substantially larger and randomized prepacing validation, or a clear reframing of M as conditional on the non-prepaced protocol, with the 'random sample from phenotype' claim removed or heavily qualified.
  2. [§2.1, Eq. (3); §3.1] The noise-model assumption of independence is contradicted by the paper's own residual analysis: Supplementary Figure 3(b) shows non-negligible autocorrelation of residuals over 10 to 15 lags. The likelihood (Eq. 4), the chi-square goodness-of-fit p-values (Eq. 8), the standard errors (Eq. 6), and the acceptance threshold all rely on this assumption. The authors acknowledge the violation and suggest an autoregressive noise model as a future refinement, but they do not quantify how the violation affects the reported p-values, standard errors, or membership in M. Because the acceptance of 1180 of 1228 fits and the uncertainty bars in Figures 1 and 5 depend on these quantities, the impact of the autocorrelation should be assessed, for example by fitting an AR(1) or ARMA noise model on a subset of cells and reporting changes in estimates, standard errors, and acceptance rates.
  3. [§3.4, Figure 8] The headline cell-by-cell match of APD30, APD50, and APD90 (R² = 0.854, 0.975, 0.990) is partly by construction: these biomarkers are deterministic summaries of the full voltage waveform used in the fitting objective. The high R² therefore demonstrates internal consistency of the fits rather than an independent prediction. The contrast with earlier population-calibration studies, which used only APD90 to calibrate, is valid as a statement about fitting the full waveform, but not as evidence of predictive superiority. To substantiate the 'also match experimental biomarker values on a cell-by-cell basis' claim as a predictive advance, the authors should validate the fitted models against data not used in fitting, such as a held-out portion of the waveform, a second AP from the same cell, or independently measured biomarkers like calcium transients or APD restitution.
  4. [§3.4] The acceptance threshold γ = 0.3 is described as 'selected by comparison with the goodness-of-fit values of the fits shown in Figure 1,' i.e., chosen post hoc from nine examples. Since the chi-square p-values themselves are affected by the noise-model misspecification noted above, the composition of M (1180 cells) depends on an ad hoc threshold. The paper should report the sensitivity of the population statistics (Table 1, Figs. 5–7) to the choice of γ over a plausible range, and ideally prespecify an acceptance rule or justify the threshold on statistical grounds rather than by visual inspection of nine cells.
minor comments (5)
  1. [§3.5, procedural uncertainty] The fluorescence-to-voltage mapping is anchored to the baseline model's plateau (0 mV) and rest (−86 mV) values. The perturbation test was performed on only one cell with ±1 mV standard deviations; the population-level effects of this mapping choice remain unquantified. Reporting the same sensitivity on a random subset of cells would strengthen the uncertainty analysis.
  2. [§3.2] The synthetic-data test uses a single randomly drawn parameter vector and a single noise realization. The reported relative errors of order 10⁻² would be more convincing if repeated over multiple synthetic cells with different parameter draws and noise realizations, so that the distribution of recovery errors could be described rather than a single example.
  3. [§3.1, Figure 1] The text states that 'on average, six out of the eight estimates are obtained with small uncertainty,' but Figure 1 shows that different parameters have large standard errors in different cells. It would be useful to report, for the full population, the fraction of cells for which each parameter is estimated with relative standard error below some threshold, since this bears on identifiability claims.
  4. [§3.4, Figure 7] The uni-variate regressions have R² ≈ 0.1 and the multivariate regressions R² ≈ 0.6, yet the text and figures emphasize p-values 'indicating high statistical significance.' With N = 1180, statistical significance is expected for small effects; the discussion would be clearer if it focused on effect sizes and prediction error rather than p-values.
  5. [General] There are occasional typographical errors, e.g., 'wafevorms' in §2.2 and 'rererences' in the caption of Table 2. These do not affect the science but should be corrected in revision.

Circularity Check

1 steps flagged · score 6.0 of 10

Cell-by-cell APD agreement is an in-sample fit metric, not an independent prediction; the rest of the inference pipeline is self-consistent.

  1. fitted input called prediction [Abstract; Section 3.4, Figure 8; fitting objective in Eqs. (4)-(5) and model output in Eq. (12)]
    "Statistical inference yields a population of nearly 1200 cell-specific model variants that, on a population-level replicate experimentally measured biomarker ranges and distributions, and in contrast to earlier studies, also match experimental biomarker values on a cell-by-cell basis. ..."

    The parameters are estimated by maximizing the likelihood in Eq. (4) with observable y = V(t), i.e. by least-squares fitting the full voltage waveform for each cell. APD30, APD50 and APD90 are threshold-crossing summaries read off that same fitted voltage trace. Therefore the cell-by-cell biomarker agreement shown in Figure 8 (R2 = 0.854, 0.975, 0.990) is an in-sample measure of how well the voltage fit reproduces the data, not an independent model prediction. Presenting it as the key advantage over population-calibration studies recasts the fitted input as an output. The genuinely unobserved predictions (intracellular calcium biomarkers) are instead checked against literature ranges, which is independent, but the headline cell-by-cell biomarker claim reduces to the fit by construction.

full rationale

The paper's core numerical procedure—fitting 1180 Shannon-model variants to individual voltage traces at scale—is internally validated and not circular: the synthetic-data test (Section 3.2) recovers known parameters with small relative errors, the Bayesian check (Section 3.3) corroborates the maximum-likelihood estimates, and the unmeasured [Ca2+]i biomarkers are compared with published experimental ranges rather than fitted values. The main circularity is in framing: the headline claim of matching experimental biomarker values on a cell-by-cell basis (abstract; Section 3.4; Figure 8) reports APD agreement between the fitted model and the very voltage recordings used to fit it. Because the likelihood (Eq. 4) is maximized against the full V(t) trace, APD values are summaries of the fitted trace, so their R2 values quantify in-sample fit quality. This is a genuine fitted-input-called-prediction pattern, but it is partial: the synthetic recovery, Bayesian agreement, and calcium forward predictions give the paper independent content. The self-citation of Lachaud et al. (2022) for the choice of eight estimands is not load-bearing circularity, because it relies on an external sensitivity analysis rather than on the present results. The Section 3.5 prepacing limitation (mean ratios lambda_tos = 32, lambda_Ks = 0.2, lambda_Kr = 2.87, lambda_Clb = 0.519) is an acknowledged validity concern about protocol dependence, not a circular step; it weakens the phenotype interpretation but does not make the derivation self-referential.

Assumptions & free parameters 12 free parameters · 6 assumptions · 0 invented entities

The central claim rests on 8 per-cell fitted conductances plus noise, whose values constitute the population M. Additional hand-chosen settings (gamma, ENa,SL, fluorescence mapping) affect every fit. The axioms are the stated statistical and modeling assumptions, several of which are known to be approximate, especially the single-waveform, fixed-initial-condition protocol.

free parameters (12)
  • alpha_GKr (relative IKr conductance) = per-cell proportions; population mean 0.368, max 2.54
    Estimated from each cell's waveform via MLE; central estimand.
  • alpha_GKs (relative IKs conductance) = per-cell proportions; population mean 8.45, max 1.00e+2
    Estimated from each cell's waveform via MLE; central estimand.
  • alpha_GK1 (relative IK1 conductance) = per-cell proportions; population mean 0.417, range 0.053 to 1.01
    Estimated from each cell's waveform via MLE; central estimand.
  • alpha_Gtos (relative Itos conductance) = per-cell proportions; population mean 0.553, max 2.30e+1
    Estimated from each cell's waveform via MLE; central estimand.
  • alpha_GCaL (relative ICaL conductance) = per-cell proportions; population mean 0.498, max 2.21e+1
    Estimated from each cell's waveform via MLE; central estimand.
  • alpha_GClb (relative Clb conductance) = per-cell proportions; population mean 0.224, max 2.83
    Estimated from each cell's waveform via MLE; central estimand.
  • alpha_INaK (relative NaK pump current) = per-cell proportions; population mean 0.536, max 5.22
    Estimated from each cell's waveform via MLE; central estimand.
  • alpha_INaCa (relative NaCa exchanger current) = per-cell proportions; population mean 0.704, max 4.79
    Estimated from each cell's waveform via MLE; central estimand.
  • sigma (noise standard deviation) = 2.37 to 3.96 mV in nine example cells; estimated per cell
    Estimated as a component of the likelihood maximization; enters all standard errors.
  • acceptance threshold gamma = 0.3
    Chosen by hand by comparison with nine example fits; determines which 1180 of 1228 cells are accepted.
  • ENa,SL reversal potential modification = -15 mV
    Hand-chosen to suppress spikes in the baseline model; affects all fits and the focus on repolarization.
  • fluorescence-to-voltage mapping anchors = V_plateau = 0 mV, V_rest = -86 mV
    Chosen from baseline Shannon model values; converts fluorescence intensity to voltage and affects every fitted trace.
assumptions (6)
  • domain assumption Measurement errors are independent, identically distributed Gaussian noise with constant variance.
    Stated in Section 2.1, Eq. (3); used for likelihood, standard errors and chi-square test. Supplementary Figure 3b shows residual autocorrelation over 10 to 15 lags, so independence is violated.
  • domain assumption Shannon et al. (2004) model with CellML implementation is an adequate baseline for rabbit ventricular myocytes.
    Baseline model is fixed except for 8 parameters and the ENa,SL change; Section 2.3.
  • domain assumption Eight parameters chosen from prior sensitivity analysis are sufficient to capture inter-cell variability.
    Section 2.4, based on Lachaud et al. (2022); no formal identifiability analysis across the full parameter space.
  • domain assumption A single non-paced action potential with baseline initial conditions represents the cell phenotype.
    Section 2.4 and Section 3.5; prepacing test shows ratios lambda_tos = 32, lambda_Ks = 0.2, lambda_Kr = 2.87 (Eq. 16), so this assumption is fragile.
  • standard math Chi-square goodness-of-fit with nu = K - l degrees of freedom remains valid for nonlinear models.
    Section 2.1, citing Press et al. (2007); an approximation for nonlinear y(t; theta).
  • ad hoc to paper Search bounds [1e-4, 1e2] and CMA-ES settings do not bias the estimates.
    Section 2.4; bounds span 6 orders of magnitude and priors are centered at baseline; no sensitivity analysis to bounds is reported.

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Pith. "Pith review of A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes." pith.science (2026). https://pith.science/paper/HXH6X7P7

@misc{pith2026250108356,
  author       = {Pith},
  title        = {Pith review of: A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HXH6X7P7}},
  note         = {Machine review of arXiv:2501.08356}
}
read the original abstract

Recent high-throughput experiments unveil substantial electrophysiological diversity among uncoupled healthy myocytes under identical conditions. To quantify inter-cell variability, the values of a subset of the parameters in a well-regarded mathematical model of the action potential of rabbit ventricular myocytes are estimated from fluorescence voltage measurements of a large number of cells. Statistical inference yields a population of nearly 1200 cell-specific model variants that, on a population-level replicate experimentally measured biomarker ranges and distributions, and in contrast to earlier studies, also match experimental biomarker values on a cell-by-cell basis. This model population may be regarded as a random sample from the phenotype of healthy rabbit ventricular myocytes. Uni-variate and bi-variate joint marginal distributions of the estimated parameters are presented, and the parameter dependencies of several commonly utilised electrophysiological biomarkers are revealed. Parameter values are weakly correlated, while summary metrics such as the action potential duration are not strongly dependent on any single electrophysiological characteristic of the myocyte. Our results demonstrate the feasibility of accurately and efficiently fitting entire action potential waveforms at scale. Keywords: cellular excitability, rabbit ventricular myocytes, fluorescence voltage measurements, action potential waveform, parameter estimation in differential equations, noisy time series

Figures

Figures reproduced from arXiv: 2501.08356 by the authors.

Figure 1
Figure 1. Examples of parameter estimations for nine typical biological myocytes. Cells are identified [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Normalised positive and negative cumulative currents [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Fitting to synthetic data. Given randomly selected, but known, parameter values (grey [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Uni-variate (on the main diagonal) and bi-variate pairwise (lower diagonal part) marginal [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Marginal probability distributions of parameter estimands ˆα [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Pairwise sample correlation coefficients [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Action potential durations APD90 and APD30 as “marginal” functions of each Shannon parameter estimand as indicated on the abscissas are shown in the peripheral panels as scatter￾plots. Simultaneously, all other estimands vary randomly. Uni-variate linear regression fit…
Figure 8
Figure 8. Figure 8: Cell-by-cell comparison of selected biomarkers (as specified in axis labels) computed from [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

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Works this paper leans on

51 extracted references · 38 canonical work pages

  1. [1]

    Aghasafari, P., et al. (2021). A deep learning algorithm to translate and classify cardiac electrophysi- ology. eLife, 10, doi:10.7554/elife.68335

    work page doi:10.7554/elife.68335 2021

  2. [2]

    B., Mann, S

    Amuzescu, B., Airini, R., Epureanu, F. B., Mann, S. A., Knott, T., & Radu, B. M. (2021). Evo- lution of mathematical models of cardiomyocyte electrophysiology. Math. Biosci. , 334, 108567, doi:10.1016/j.mbs.2021.108567. 20

    work page arXiv 2021

  3. [3]

    Antzelevitch, C., et al. (1991). Heterogeneity within the ventricular wall. Electrophysiology and pharmacology of epicardial, endocardial, and M cells. Circ. Res. , 69(6), 1427–1449, doi:10.1161/01.res.69.6.1427

    work page doi:10.1161/01.res.69.6.1427 1991

  4. [4]

    A., Lopez, M

    Aragon, F., Goberna, M. A., Lopez, M. A., & Rodriguez, M. M. (2019). Nonlinear Optimization . Springer International Publishing

    work page 2019

  5. [5]

    Arridge, S., Maass, P., ¨Oktem, O., & Sch¨ onlieb, C.-B. (2019). Solving inverse problems using data- driven models. Acta Numerica, 28, 1–174, doi:10.1017/s0962492919000059

    doi:10.1017/s0962492919000059 2019

  6. [6]

    & Simitev, R

    Aziz, M. & Simitev, R. D. (2022). Estimation of Parameters for an Archetypal Model of Cardiomyocyte Membrane Potentials. Int. J. Bioautom. , 26(3), 255–272, doi:10.7546/ijba.2022.26.3.000832

    work page doi:10.7546/ijba.2022.26.3.000832 2022

  7. [7]

    Baartscheer, A. (2003). Increased Na+/H+-exchange activity is the cause of increased [Na+]i and underlies disturbed calcium handling in the rabbit pressure and volume overload heart failure model. Cardiovascular Research, 57(4), 1015–1024, doi:10.1016/s0008-6363(02)00809-x http://dx.doi. org/10.1016/S0008-6363(02)00809-X

    work page doi:10.1016/s0008-6363(02)00809-x 2003

  8. [8]

    Bardenet, R., Capp´ e, O., Fort, G., & K´ egl, B. (2015). Adaptive MCMC with online relabeling. Bernoulli, 21, doi:10.3150/13-bej578

    work page doi:10.3150/13-bej578 2015

Show all 51 references
  1. [9]

    E., & Simitev, R

    Biktashev, V., Suckley, R., Elkin, Y. E., & Simitev, R. D. (2008). Asymptotic Analysis and Analytical Solutions of a Model of Cardiac Excitation. Bull. Math. Biol. , 70(2), 517–554, doi:10.1007/s11538- 007-9267-0

    2008 doi

  2. [10]

    R., Ortega, F

    Bot, C., Kherlopian, A. R., Ortega, F. A., Christini, D. J., & Krogh-Madsen, T. (2012). Rapid Genetic Algorithm Optimization of a Mouse Computational Model: Benefits for Anthropomorphization of Neonatal Mouse Cardiomyocytes. Front. Physiol., 3, doi:10.3389/fphys.2012.00421

    2012 arXiv

  3. [11]

    Rodriguez, B. (2013). Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. PNAS, 110(23), E2098–E2105, doi:10.1073/pnas.1304382110

    2013 doi

  4. [12]

    A., Aon, M

    Brown, D. A., Aon, M. A., Akar, F. G., Liu, T., Sorarrain, N., & O’Rourke, B. (2008). Effects of 4’-chlorodiazepam on cellular excitation-contraction coupling and ischaemia-reperfusion injury in rabbit heart. Cardiovascular Research, 79(1), 141–149, doi:10.1093/cvr/cvn053 http...

    2008 doi

  5. [13]

    Clerx, M., Collins, P., de Lange, E., & Volders, P. G. (2016). Myokit: A simple inter- face to cardiac cellular electrophysiology. Prog. Biophys. Molec. Biol. , 120(1-3), 100–114, doi:10.1016/j.pbiomolbio.2015.12.008

    2016 doi

  6. [14]

    L., Ghosh, S., Mirams, G

    Clerx, M., Robinson, M., Lambert, B., Lei, C. L., Ghosh, S., Mirams, G. R., & Gavaghan, D. J. (2019). Probabilistic Inference on Noisy Time Series (PINTS). J. Open Res. Software , 7(1), 23, doi:10.5334/jors.252

    2019 doi

  7. [15]

    & Lovell, N

    Dokos, S. & Lovell, N. H. (2004). Parameter estimation in cardiac ionic models. Prog. Biophys. Molec. Biol., 85(2-3), 407–431, doi:10.1016/j.pbiomolbio.2004.02.002

    2004 doi

  8. [16]

    Feng, J., Yue, L., Wang, Z., & Nattel, S. (1998). Ionic Mechanisms of Regional Action Potential Heterogeneity in the Canine Right Atrium. Circ. Res., 83(5), 541–551, doi:10.1161/01.res.83.5.541

    1998 doi

  9. [17]

    A., Kherlopian, A

    Groenendaal, W., Ortega, F. A., Kherlopian, A. R., Zygmunt, A. C., Krogh-Madsen, T., & Christini, D. J. (2015). Cell-Specific Cardiac Electrophysiology Models. PLOS Comp. Biol. , 11(4), e1004242, doi:10.1371/journal.pcbi.1004242

    2015 doi

  10. [18]

    Hansen, N. (2006). The CMA Evolution Strategy: A Comparing Review. In Towards a New Evolutionary Computation (pp. 75–102). Springer Berlin Heidelberg https://doi.org/10.1007/ 3-540-32494-1_4

    2006

  11. [19]

    D., & Koumoutsakos, P

    Hansen, N., M¨ uller, S. D., & Koumoutsakos, P. (2003). Reducing the Time Complexity of the Deran- domized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). Evolut. Comp., 11(1), 1–18, doi:10.1162/106365603321828970

    2003 doi

  12. [20]

    L., & Entcheva, E

    Heinson, Y., Han, J. L., & Entcheva, E. (2023). Portable low-cost macroscopic mapping system for all- optical cardiac electrophysiology. J. Biomed. Optics, 28(01), 016001, doi:10.1117/1.jbo.28.1.016001

    2023 doi

  13. [21]

    Herron, T., Lee, P., & Jalife, J. (2012). Optical Imaging of Voltage and Calcium in Cardiac Cells & 21 Tissues. Circ. Res., 110(4), 609–623, doi:10.1161/circresaha.111.247494

    2012 doi

  14. [22]

    N., Grant, K

    Hindmarsh, A., Brown, P. N., Grant, K. E., Lee, S. L., Serban, R., Shumaker, D. E., & Woodward, C. S. (2005). SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers.ACM Trans. Math. Software (TOMS) , 31(3), 363–396, doi:10.1145/1089014.1089020

    2005

  15. [23]

    Huethorst, E., et al. (2021). Conventional rigid 2D substrates cause complex contractile signals in monolayers of human induced pluripotent stem cell-derived cardiomyocytes. J. Physiol. , 600(3), 483–507, doi:10.1113/jp282228

    2021 doi

  16. [24]

    Johnstone, R., et al. (2016). Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? Journal of Molecular and Cellular Cardiology , 96, 49–62, doi:10.1016/j.yjmcc.2015.11.018 http://dx.doi.org/10.1016/j.yjmcc.2015.11.018

    2016 doi

  17. [25]

    A., & Christini, D

    Krogh-Madsen, T., Sobie, E. A., & Christini, D. J. (2016). Improving cardiomyocyte model fidelity and utility via dynamic electrophysiology protocols and optimization algorithms.J. Physiol., 594(9), 2525–2536, doi:10.1113/jp270618

    2016 doi

  18. [26]

    Lachaud, Q., Aziz, M. H. N., Burton, F. L., Macquaide, N., Myles, R. C., Simitev, R. D., & Smith, G. L. (2022). Electrophysiological heterogeneity in large populations of rabbit ventricular cardiomy- ocytes. Cardiovasc. Res., 118(15), 3112–3125, doi:10.1093/cvr/cvab375

    2022 doi

  19. [27]

    C., Cusimano, N., Burrage, P., Rodriguez, B., & Burrage, K

    Lawson, B., Drovandi, C. C., Cusimano, N., Burrage, P., Rodriguez, B., & Burrage, K. (2018). Unlocking data sets by calibrating populations of models to data density: A study in atrial electro- physiology. Sci. Adv., 4(1), e1701676, doi:10.1126/sciadv.1701676

    2018 doi

  20. [28]

    Lee, P., et al. (2023). A fully-automated low-cost cardiac monolayer optical mapping robot. Front. Cardiovasc. Med., 10, 1096884, doi:10.3389/fcvm.2023.1096884

    2023

  21. [29]

    Lei, C., et al. (2020). Considering discrepancy when calibrating a mechanistic electrophysiology model. Phil. Trans. Roy. Soc. A , 378(2173), 20190349, doi:10.1098/rsta.2019.0349

    2020

  22. [30]

    R., Hunter, P

    Lloyd, C., Lawson, J. R., Hunter, P. J., & Nielsen, P. F. (2008). The CellML Model Repository. Bioinformatics, 24(18), 2122–2123, doi:10.1093/bioinformatics/btn390

    2008 doi

  23. [31]

    Mahajan, A., et al. (2008). A Rabbit Ventricular Action Potential Model Replicating Cardiac Dynamics at Rapid Heart Rates. Biophys. J. , 94(2), 392–410, doi:10.1529/biophysj.106.98160 http://dx.doi.org/10.1529/biophysj.106.98160

    2008 doi

  24. [32]

    McIntosh, M. (2000). Heterogeneous changes in action potential and intracellular Ca2+ in left ventric- ular myocyte sub-types from rabbits with heart failure. Cardiovascular Research, 45(2), 397–409, doi:10.1016/s0008-6363(99)00360-0 http://dx.doi.org/10.1016/s0008-6363(99)00360-0

    2000 doi

  25. [33]

    & Grandi, E

    Morotti, S. & Grandi, E. (2017). Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk. MethodsX, 4, 25–34, doi:10.1016/j.mex.2016.12.002. M¨ ullenbroich, M., et al. (2021). Novel Optics-Based Approaches for Cardiac Electrophysi...

    2017

  26. [34]

    Muszkiewicz, A., et al. (2016). Variability in cardiac electrophysiology: Using experimentally- calibrated populations of models to move beyond the single virtual physiological human paradigm. Prog. Biophys. Molec. Biol. , 120(1-3), 115–127, doi:10.1016/j.pbiomolbio.2015.12.002

    2016 doi

  27. [35]

    Ni, H., Morotti, S., & Grandi, E. (2018). A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research. Front. Physiol., 9, 00958, doi:10.3389/fphys.2018.00958

    2018

  28. [36]

    L., & Sorger, P

    Niepel, M., Spencer, S. L., & Sorger, P. K. (2009). Non-genetic cell-to-cell variability and the conse- quences for pharmacology. Curr. Op. Chem. Biol., 13(5-6), 556–561, doi:10.1016/j.cbpa.2009.09.015

    2009 doi

  29. [37]

    Passini, E., Minchole, A., Coppini, R., Cerbai, E., Rodriguez, B., Severi, S., & Bueno-Orovio, A. (2016). Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti- arrhythmic therapies in human hypertrophic cardiomyopathy. J. Molec. Cell. Cardiol. , 96...

    2016 doi

  30. [38]

    A., Vetterling, W

    Press, W., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical Recipes 3rd Edition: The Art of Scientific Computing . Cambridge University Press, 3 edition http://dx.doi. org/10.5555/1403886

    2007 doi

  31. [39]

    P., & Bence, S

    Riley, K., Hobson, M. P., & Bence, S. J. (2006). Mathematical Methods for Physics and Engineering . Cambridge University Press http://dx.doi.org/10.1017/CBO9780511810763. 22

    2006 doi

  32. [40]

    Romero, L., Pueyo, E., Fink, M., & Rodriguez, B. (2009). Impact of ionic current variabil- ity on human ventricular cellular electrophysiology. Am. J. Physiol. , 297(4), H1436–H1445, doi:10.1152/ajpheart.00263.2009. S´ anchez, C., et al. (2014). Inter-Subject Variability in Hu...

    2009

  33. [41]

    & Sobie, E

    Sarkar, A. & Sobie, E. (2010). Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells. PLoS Comp. Biol. , 6(9), e1000914, doi:10.1371/journal.pcbi.1000914

    2010 doi

  34. [42]

    Shannon, T., Wang, F., Puglisi, J., Weber, C., & Bers, D. M. (2004). A Mathematical Treat- ment of Integrated Ca Dynamics within the Ventricular Myocyte. Biophys. J. , 87(5), 3351–3371, doi:10.1529/biophysj.104.047449

    2004 doi

  35. [43]

    J., Burton, F

    Simitev, R., Gilchrist, R. J., Burton, F. L., Myles, R. C., & Smith, G. L. (2024). Code and data for estimation of parameters in mathematical models of myocyte action potentials. Zenodo, doi:10.5281/zenodo.11191649 https://zenodo.org/doi/10.5281/zenodo.11191649

    2024 doi

  36. [44]

    Sobie, E. (2009). Parameter Sensitivity Analysis in Electrophysiological Models Using Multivariable Regression. Biophys. J. , 96(4), 1264–1274, doi:10.1016/j.bpj.2008.10.056

    2009 doi

  37. [45]

    Syed, Z., Vigmond, E., Nattel, S., & Leon, L. J. (2005). Atrial cell action potential parameter fitting using genetic algorithms. Med. Biol. Eng. Comp. , 43(5), 561–571, doi:10.1007/bf02351029

    2005 doi

  38. [46]

    Warren, M., et al. (2010). High-precision recording of the action potential in isolated cardiomyocytes using the near-infrared fluorescent dye di-4-ANBDQBS. Am. J. Physiol. , 299(4), H1271–H1281, doi:10.1152/ajpheart.00248.2010

    2010

  39. [47]

    L., Christini, D

    Whittaker, D., Clerx, M., Lei, C. L., Christini, D. J., & Mirams, G. R. (2020). Calibration of ionic and cellular cardiac electrophysiology models. WIREs Sys. Biol. Med. , 12(4), e1482, doi:10.1002/wsbm.1482

    2020 doi

  40. [48]

    L., Rice, J

    Winslow, R., Cortassa, S., O 'Rourke, B., Hashambhoy, Y. L., Rice, J. J., & Greenstein, J. L. (2011). Integrative modeling of the cardiac ventricular myocyte. WIREs Sys. Biol. Med. , 3(4), 392–413, doi:10.1002/wsbm.122

    2011 doi

  41. [49]

    & Clancy, C

    Yang, P.-C. & Clancy, C. E. (2012). In silico Prediction of Sex-Based Differences in Hu- man Susceptibility to Cardiac Ventricular Tachyarrhythmias. Frontiers in Physiology , 3, doi:10.3389/fphys.2012.00360 http://dx.doi.org/10.3389/fphys.2012.00360

    2012 arXiv

  42. [50]

    Zhang, Y., Toyoda, F., Himeno, Y., Noma, A., & Amano, A. (2024). Cell-specific models of hiPSC- CMs developed by the gradient-based parameter optimization method fitting two different action potential waveforms. Scientific Reports, 14(1), doi:10.1038/s41598-024-63413-0 http://...

    2024 doi

  43. [51]

    Zhou, X., et al. (2016). In Vivo and In Silico Investigation Into Mechanisms of Frequency Dependence of Repolarization Alternans in Human Ventricular Cardiomyocytes. Circ. Res., 118(2), 266–278, doi:10.1161/circresaha.115.307836. 23

    2016 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.