A brief description is provided for some of the below papers, especially those involving model credibility evaluation.

  1. S. Galappaththige, P. Pathmanathan, R. Gray, A Computational Modeling Framework for Pre-clinical Evaluation of Cardiac Mapping Systems, Frontiers in Physiology, 2023. DOI

  2. K. Dautel, E. Agyingi, P. Pathmanathan, Validation framework for epidemiological models with application to COVID-19 model, PLOS Computational Biology, 2023. DOI
    This paper presents a framework for validating epidemiological models (e.g., COVID models0 focusing on how well the models predict quantities such as date and magnitude of local peaks. The framework python code is available.

  3. J. Blauer, R. Gray, D. Swenson, P Pathmanathan, Validation and Applicability Analysis of a Computational Model of External Defibrillation, J. Verification, Validation and Uncertainty Quantification 7(4), 2022. DOI
    This paper includes an example of how to use the Applicability Analysis framework developed in Pathmanathan et al., JVVUQ, 2017.

  4. V. Kanal, P. Pathmanathan, J. Hahn, G. Kramer, C. Scully, R. Bighamian, Development and validation of a mathematical model of heart rate response to fluid perturbation, Scientific Reports 12(1), 2022. DOI

  5. S. Galappaththige, R. Gray, C. Costa, S. Niederer, P. Pathmanathan, Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar, PLOS Computational Biology, 2022. DOI
    Patient-specific models can be used in medical device software as clinical tools, or to in virtual device testing. Here we considered how to assess credibility of patient-specific models.

  6. A. Santiago et al., Design and execution of a Verification, Validation, and Uncertainty Quantification plan for a numerical model of left ventricular flow after LVAD implantation, PLOS Computational Biology, 2022. DOI

  7. A. Sher et al., Quantitative systems pharmacology perspective on the importance of parameter identifiability, Bulletin of Mathematical Biology, 2022. DOI

  8. S. Braakman, P. Pathmanathan, H. Moore, Evaluation framework for systems models, CPT: Pharmacometrics & Systems Pharmacology, 2021. DOI

  9. B. Parvinian, R. Bighamian, C Scully, J. Hahn, P. Pathmanathan, Credibility Assessment of a Subject-Specific Mathematical Model of Blood Volume Kinetics for Prediction of Physiological Response to Hemorrhagic Shock and Fluid Resuscitation, Frontiers in Physiology, 2021. DOI

  10. P. Pathmanathan et al., Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics, Frontiers in Physiology, 11:1463, 2020, DOI
    This article is a sister paper to Pathmanathan et al., Frontiers, 2019 (see below), and extends the previous work by quantifying uncertainty in all cell model parameters using a range of data sources

  11. C. Lei et al., Considering discrepancy when calibrating a mechanistic electrophysiology model, Philosophical Transactions of the Royal Society A, 378 (2173), 2020. DOI

  12. S Niederer et al., Creation and Application of Virtual Patient Cohorts of Cardiac Models, Philosophical Transactions of the Royal Society A, 378 (2173), 2020. DOI

  13. J. Acero et al, The “digital twin” to enable the vision of precision cardiology, European Heart Journal, ehaa159, 2020. DOI

  14. F Cooper et al., Chaste: Cancer, Heart and Soft Tissue Environment, Journal of Open Source Software, 5(47), 2020. DOI

  15. P. Pathmanathan et al. Comprehensive uncertainty quantification and sensitivity analysis for cardiac action potential models, Frontiers in Physiology, 2019. DOI
    One of a series of papers on the impact of uncertainty in cardiac models. This paper represents a big step forward from the others in this series, by analysing the impact of simultaneous variability in all parameters of a cardiac action potential model

  16. S. Galappaththige et al., Effect of Heart Structure on Ventricular Fibrillation in the rabbit: a simulation study, Frontiers in Physiology, 2019. DOI

  17. B. Parvinian et al., Credibility Evidence for Computational Patient Models in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine,
    Frontiers in Physiology, 10:220, 2019. DOI
    PCLC devices are the medical device equivalent to autonomous (self-driving) cars. This paper reviews the evidence typically presented to support physiological models that are used to evaluate PCLC devices.

  18. T. Morrison et al., Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA’s Office of Science and Engineering Laboratories, Frontiers in Medicine, 5:241, 2018. DOI

  19. R. Gray and P. Pathmanathan, Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges, J. Cardiovascular Translational Research, 11(2):80-88, 2018. DOI

  20. P. Pathmanathan and R. Gray, Validation and trustworthiness of multiscale models of cardiac electrophysiology, Frontiers in Physiology, 9:106, 2018. DOI
    This paper reviews the types of evidence the typically support (implicitly or explicitly) credibility of cardiac EP models. A novel categorization of credibility evidence was developed for this purpose.

  21. P. Pathmanathan et al., Applicability Analysis of Validation Evidence for Biomedical Computational Models, J. Verification, Validation and Uncertainty Analysis, 2(2), 021005, 2017. DOI
    There are often large differences between how computational models with applications in healthcare are validated, vs how they are used. For example, it may be difficult to validate a model of an implanted device against equivalent clinical data. This paper presents a new systematic approach to evaluate the relevance of validation results for supporting the use of a model.

  22. R. Gray & P. Pathmanathan, A Parsimonious Model of the Rabbit Action Potential Elucidates the Minimal Physiological Requirements for Alternans and Spiral Wave Breakup, PLOS Computational Biology, 12(10), e1005087, 2016. DOI

  23. G. Mirams et al., White paper: Uncertainty and variability in computational and mathematical models of cardiac physiology, J. Physiology, 594 (23), 6833-684, 2016. DOI
    One of a series of papers on the impact of uncertainty in cardiac models. This paper reviews different sources of uncertainty and methods for assessing their impact

  24. R. Johnstone et al., Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? J. Mol. Cell Cardiology, 96, 49-62, 2016. DOI
    One of a series of papers on the impact of uncertainty in cardiac models. This paper presents a series of case studies on the this subject.

  25. P. Pathmanathan & R. Gray, Filament dynamics during simulated ventricular fibrillation in a high resolution rabbit heart, BioMed Research International, Article ID 720575, 2015. DOI

  26. P. Pathmanathan et al., Uncertainty quantification of fast sodium current steady-state inactivation for multi-scale models of cardiac electrophysiology, Progress in Biophysics and Molecular Biology, 117(1), 4-18, 2015. DOI
    One of a series of papers on the impact of uncertainty in cardiac models. This paper quantifies population variability in gating parameters related to the fast sodium channel and determines the impact on the action potential.

  27. V. Gurev, P. Pathmanathan et al. A high resolution computational model of the deforming human heart, Biomechanics and Modeling in Mechanobiology, 14(4), 829-849, 2015. DOI

  28. V. Carapella et al., Quantitative study of the effect of tissue microstructure on contraction in a computational model of rat left ventricle, PLOS One, 9.4, e92792, 2014. DOI

  29. D. Bruce et al., Modelling the effect of gap junctions on tissue-level cardiac electrophysiology, Bulletin of Mathematical Biology, 76(2), 431-454, 2014. DOI

  30. P. Pathmanathan & R. Gray, Verification of computational models of cardiac electro-physiology, Int. Journal for Numerical Methods in Bioengineering, 30(5), 525-544, 2014. DOI
    This paper is focused on verification (“are the equations solved correctly”) of cardiac models. To be able to test cardiac solvers, we developed test problems for which the solution is known, for the monodomain and bidomain equations, in 1D, 2D and 3D.

  31. P. Pathmanathan & R. Gray, Ensuring reliability of safety-critical clinical applications of computational cardiac models, Frontiers in Physiology, 4(358), 2013. DOI
    This paper considers how techniques developed by the traditional engineering community for evaluating models can be applied to cardiac models

  32. R. Gray et al. Transmembrane current imaging in the heart during pacing and fibrillation, Biophysical Journal, 105(7), 1710-1719, 2013. DOI

  33. G. Mirams et al., Chaste: An open-source C++ library for computational physiology and biology, PLOS Computational Biology, 9(3), e1002970, 2013. DOI

  34. N. Zemzemi et al., Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials, British Journal of Pharmacology, 168(3), 718-733, 2013. DOI

  35. A. Corrias et al. Modelling tissue electrophysiology with multiple cell types: applications of the extended bidomain framework, Integrative Biology, 4(2), 192-201, 2012. DOI

  36. P. Pathmanathan et al., Computational modelling of cardiac electro-physiology: explanation of the variability of results from different numerical solvers, International Journal for Numerical Methods in Biomedical Engineering, 28(2), 890-903, 2012. DOI

  37. P. Pathmanathan et al., The significant effect of the choice of ionic current integration method in cardiac electrophysiological simulations, International Journal for Numerical Methods in Biomedical Engineering, 27(11), 1751-1770, 2011. DOI

  38. S. Niederer et al., Verification of cardiac tissue electrophysiology simulators using an N-version benchmark, Royal Society Philosophical Transactions A, 369(1954), 4331-4351, 2011. DOI

  39. P. Pathmanathan et al., A numerical guide to the solution of the bidomain equations of cardiac electrophysiology, Progress in Biophysics and Molecular Biology, 102(2-3), 136-155, 2010. DOI

  40. P. Pathmanathan et al., Cardiac electromechanics: the effect of contraction model on the mathematical problem and accuracy of the numerical scheme, Quarterly Journal of Mechanics and Applied Mathematics, 3, 375-399, 2010. DOI

  41. P. Pathmanathan et al., A note on the effect of the choice of weak form on GMRES convergence for incompressible nonlinear elasticity problems, Journal of Applied Mechanics, 77(3), 2010. DOI

  42. M. Bernabeu et al., Stimulus protocol determines the most computationally-efficient preconditioner for the bidomain equations, Transactions of Biomedical Engineering, 57(12), 2806-2815, 2010. DOI

  43. J. Osborne et al., A hybrid approach to multi-scale modelling of cancer, Philosophical Transactions of the Royal Society A, 368, 5013-5028, 2010. DOI

  44. P. Pathmanathan & J. Whiteley, A numerical method for cardiac mechano-electric simulations, Annals of Biomedical Engineering, 37(5), 860-873, 2009. DOI

  45. P. Pathmanathan et al., Inverse membrane problems in elasticity, Quarterly Journal of Mechanics and Applied Mathematics, 62(1), 67-88, 2009. DOI

  46. P. Pathmanathan et al., A computational study of discrete mechanical tissue models, Physical Biology, 6(3), 2009. DOI

  47. P. Pathmanathan et al., A comparison of numerical methods used for finite element modelling of soft tissue deformation, The Journal of Strain Analysis for Engineering Design, 44(5), 391-406, 2009. DOI

  48. J. Pitt-Francis, P. Pathmanathan et al., Chaste: A test-driven approach to software development for biological modelling, Computer Physics Communications, 180(12), 2452-2471. 2009. DOI

  49. M. Bernabeu et al., Chaste: Incorporating a novel multiscale spatial and temporal algorithm into a large scale open source library, Philosophical Transactions of the Royal Society (A), 367(1895), 1907-1930, 2009. DOI

  50. I. van Leeuwen et al., An integrative computational model for intestinal tissue renewal, Cell Proliferation, 42(5), 617-636, 2009. DOI

  51. P. Pathmanathan et al., Predicting tumor location by modeling the deformation of the breast, IEEE Transactions on Biomedical Engineering, 55(10), 2471-2480, 2008. DOI

  52. J. Pitt-Francis et al., Chaste: Using agile programming techniques to develop computational biology software, Philosophical Transactions of the Royal Society A, 366(1878), 3111-3136, 2008. DOI