This page lists all my publications, organized by category. A brief description is provided for several recent papers, especially those involving model credibility evaluation.

If you use any of these publications in a regulatory submission in any way, please cite clearly, with all author names to support searchability.

Contents

Overviews and Perspectives

  1. B. Craven et al., Computational modeling and simulation for medical devices: a summary of the 2024 FDA/MDIC Symposium, Progress in Biomedical Engineering, 8(1), 013001, 2026. DOI

  2. K. Ahmed, P. Pathmanathan, S. Kabadi, T. Drgon, T. Morrison, Editorial on the FDA Report on “Successes and Opportunities in Modeling & Simulation for FDA”, Annals of Biomedical Engineering, 51(1):6–9, 2023. DOI

  3. 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 200+ citations

  4. P. Pathmanathan & R. Gray, Ensuring reliability of safety-critical clinical applications of computational cardiac models, Frontiers in Physiology, 4(358), 2013. DOI50+ citations
    This paper considers how techniques developed by the traditional engineering community for evaluating models can be applied to cardiac models

In Silico Clinical Trials

  1. P. Pathmanathan et al., Credibility assessment of in silico clinical trials for medical devices, PLOS Computational Biology, 20(8), e1012289, 2024. DOI50+ citations
    Addresses various questions including: what is an in silico clinical trial, what examples of ISCTs are there in the literature, how can ISCTs be evaluated?

  2. K. Aycock et al., Toward trustworthy medical device in silico clinical trials: a hierarchical framework for establishing credibility and strategies for overcoming key challenges, Frontiers in Medicine, 11:1433372, 2024. DOI
    Closely related to the above paper, discusses credibility assessment of ISCTs and considers a mitral valve clip device ISCT.

  3. S Niederer et al., Creation and Application of Virtual Patient Cohorts of Cardiac Models, Philosophical Transactions of the Royal Society A, 378 (2173), 2020. DOI100+ citations

Patient-Specific Models

  1. S. Kizilski et al., Preclinical Validation of a Patient-Specific Patch-Planning Workflow for Congenital Cardiovascular Reconstruction, Annals of Biomedical Engineering, 53(12), 3505–3522, 2025. DOI
    Presents a model-based surgical planning workflow. Of note: the supplement of this paper includes an end-to-end credibility assessment of the model-based workflow

  2. 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.

  3. J. Acero et al, The “digital twin” to enable the vision of precision cardiology, European Heart Journal, ehaa159, 2020. DOI500+ citations

  4. R. Gray and P. Pathmanathan, Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges, J. Cardiovascular Translational Research, 11(2):80-88, 2018. DOI200+ citations

General model evaluation frameworks

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

  2. P. Pathmanathan et al., Applicability Analysis of Validation Evidence for Biomedical Computational Models, J. Verification, Validation and Uncertainty Analysis, 2(2), 021005, 2017. DOI50+ citations
    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.

Model validation

  1. K. Dautel, E. Agyingi, P. Pathmanathan, Assessing the reliability of medical resource demand models in the context of COVID-19, BMC Medical Informatics and Decision Making, 24(1):322, 2024. 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 models) focusing on how well the models predict quantities such as date and magnitude of local peaks. The framework python code is available.

  3. 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

  4. 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.

  5. 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

  6. P. Pathmanathan and R. Gray, Validation and trustworthiness of multiscale models of cardiac electrophysiology, Frontiers in Physiology, 9:106, 2018. DOI50+ citations
    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.

Model verification

  1. P. Pathmanathan & R. Gray, Verification of computational models of cardiac electro-physiology, Int. Journal for Numerical Methods in Bioengineering, 30(5), 525-544, 2014. DOI50+ citations
    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.

  2. 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. DOI50+ citations

  3. 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. DOI50+ citations

  4. 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. DOI200+ citations

Uncertainty Quantification and Variability

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

  2. 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

  3. C. Lei et al., Considering discrepancy when calibrating a mechanistic electrophysiology model, Philosophical Transactions of the Royal Society A, 378 (2173), 2020. DOI50+ citations

  4. P. Pathmanathan et al. Comprehensive uncertainty quantification and sensitivity analysis for cardiac action potential models, Frontiers in Physiology, 2019. DOI50+ citations
    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

  5. G. Mirams et al., White paper: Uncertainty and variability in computational and mathematical models of cardiac physiology, J. Physiology, 594 (23), 6833-684, 2016. DOI100+ citations
    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

  6. 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. DOI100+ citations
    One of a series of papers on the impact of uncertainty in cardiac models. This paper presents a series of case studies on this subject.

  7. 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. DOI50+ citations
    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.

Cardiac Electrophysiology

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

  3. 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

  4. 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

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

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

  7. 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. DOI100+ citations

  8. 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

Physiological closed loop control

  1. 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

  2. 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. DOI50+ citations
    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.

Cardiac Mechanics

  1. 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. DOI50+ citations

  2. 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

  3. 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. DOI50+ citations

  4. P. Pathmanathan & J. Whiteley, A numerical method for cardiac mechano-electric simulations, Annals of Biomedical Engineering, 37(5), 860-873, 2009. DOI50+ citations

Non-cardiac mechanics

  1. 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

  2. P. Pathmanathan et al., Predicting tumor location by modeling the deformation of the breast, IEEE Transactions on Biomedical Engineering, 55(10), 2471-2480, 2008. DOI100+ citations

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

Computational Methods and Software

  1. F Cooper et al., Chaste: Cancer, Heart and Soft Tissue Environment, Journal of Open Source Software, 5(47), 2020. DOI50+ citations

  2. G. Mirams et al., Chaste: An open-source C++ library for computational physiology and biology, PLOS Computational Biology, 9(3), e1002970, 2013. DOI500+ citations

  3. 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

  4. 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. DOI100+ citations

  5. 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

  6. 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. DOI200+ citations

  7. 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. DOI50+ citations

  8. 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. DOI100+ citations

Cell-based modeling

  1. J. Osborne et al., A hybrid approach to multi-scale modelling of cancer, Philosophical Transactions of the Royal Society A, 368, 5013-5028, 2010. DOI100+ citations

  2. P. Pathmanathan et al., A computational study of discrete mechanical tissue models, Physical Biology, 6(3), 2009. DOI100+ citations

  3. I. van Leeuwen et al., An integrative computational model for intestinal tissue renewal, Cell Proliferation, 42(5), 617-636, 2009. DOI100+ citations