Aurore Lyon

Post doc

Dr Aurore Lyon graduated from Telecom ParisTech Engineering School in Paris (France) with a Master in Engineering, and from the University of Oxford (UK) with a Master in Computer Science. She obtained her PhD at the Department of Computer Science at the University of Oxford, working in the Computational Cardiovascular Science group. Her PhD focussed on using computational techniques for analysis, modelling and simulation of ECG signals for patient risk stratification in hypertrophic cardiomyopathy.

In April 2018, she started working at Maastricht University in the Department of Biomedical Engineering. Her research focusses on cardiac electromechanics and the coupling from cellular electrophysiology to whole-heart mechanics and hemodynamics. Specific projects of research include electromechanical modelling, effect of exercise on cardiac pathologies such as ARVC or deformation imaging analysis in atrial fibrillation patients. Her interests lie in combining computer simulations with clinical and experimental data to better understand cardiac physiology (e.g. exercise) and disease mechanisms (e.g. arrhythmia). In September 2020, she was awarded a CVON Young Talent grant and a UU-3Rs Stimulus grant to join the Department of Medical Physiology at the UMC Utrecht. There, she applied her computer modeling expertise to arrhythmogenic cardiomyopathy and established strong links between experimental and clinical groups. She is now back in Maastricht as of September 2021.

 

Department of Biomedical Engineering
Universiteitssingel 50, 6229 ER Maastricht
PO Box 616, 6200 MD Maastricht
Room number: H3.348

  • 2024
    • Arts, T., Lyon, A., Delhaas, T., Kuster, D. W. D., van der Velden, J., & Lumens, J. (2024). Translating myosin-binding protein C and titin abnormalities to whole-heart function using a novel calcium-contraction coupling model. Journal of Molecular and Cellular Cardiology. Advance online publication. https://doi.org/10.1016/j.yjmcc.2024.03.001
  • 2023
    • Trayanova, N. A., Lyon, A., Shade, J., & Heijman, J. (2023). Computational modeling of cardiac electrophysiology and arrhythmogenesis. Physiological Reviews. Advance online publication. https://doi.org/10.1152/physrev.00017.2023
    • Tamargo, M., Martínez-Legazpi, P., Espinosa, M. Á., Lyon, A., Méndez, I., Gutiérrez-Ibañes, E., Fernández, A. I., Prieto-Arévalo, R., González-Mansilla, A., Arts, T., Delhaas, T., Mombiela, T., Sanz-Ruiz, R., Elízaga, J., Yotti, R., Tschöpe, C., Fernández-Avilés, F., Lumens, J., & Bermejo, J. (2023). Increased Chamber Resting Tone Is a Key Determinant of Left Ventricular Diastolic Dysfunction. Circulation-Heart Failure, 16(12), E010673. Article 010673. https://doi.org/10.1161/CIRCHEARTFAILURE.123.010673
    • Buonocunto, M., Lyon, A., Delhaas, T., Heijman, J., & Lumens, J. (2023). Electrophysiological effects of stretch-activated ion channels: a systematic computational characterization. The Journal of Physiology. Advance online publication. https://doi.org/10.1113/JP284439
    • Beela, A. S., Manetti, C. A., Lyon, A., Prinzen, F. W., Delhaas, T., Herbots, L., & Lumens, J. (2023). Impact of Estimated Left Atrial Pressure on Cardiac Resynchronization Therapy Outcome. Journal of Clinical Medicine, 12(15), Article 4908. https://doi.org/10.3390/jcm12154908
  • 2022
    • Van Mourik, M. J. W., Arita, V. A., Lyon, A., Lumens, J., De With, R. R., van Melle, J. P., Schotten, U., Bekkers, S. C. A. M., Crijns, H. J. G. M., Van Gelder, I. C., Rienstra, M., & Linz, D. K. (2022). Association between comorbidities and left and right atrial dysfunction in patients with paroxysmal atrial fibrillation: Analysis of AF-RISK. International Journal of Cardiology, 360, 29-35. https://doi.org/10.1016/j.ijcard.2022.05.044
    • Weerts, J., Barandiarán Aizpurua, A., Henkens, M. T. H. M., Lyon, A., van Mourik, M. J. W., van Gemert, M. R. A. A., Raafs, A., Sanders-van Wijk, S., Bayés-Genís, A., Heymans, S. R. B., Crijns, H. J. G. M., Brunner-La Rocca, H.-P., Lumens, J., van Empel, V. P. M., & Knackstedt, C. (2022). The prognostic impact of mechanical atrial dysfunction and atrial fibrillation in heart failure with preserved ejection fraction. European Heart Journal Cardiovascular Imaging, 23(1), 74-84. https://doi.org/10.1093/ehjci/jeab222
  • 2021
    • Lyon, A., van Mourik, M., Cruts, L., Heijman, J., Bekkers, S. C. A. M., Schotten, U., Crijns, H. J. G. M., Linz, D., & Lumens, J. (2021). Understanding the effects of heart beat irregularity on ventricular function in human atrial fibrillation: simulation models may help to untie the knot-Authors' reply. EP Europace, 23(11), 1869-1869. https://doi.org/10.1093/europace/euab144
    • van Osta, N., Kirkels, F. P., van Loon, T., Koopsen, T., Lyon, A., Meiburg, R., Huberts, W., Cramer, M. J., Delhaas, T., Haugaa, K. H., Teske, A. J., & Lumens, J. (2021). Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling. Frontiers in physiology, 12, Article 738926. https://doi.org/10.3389/fphys.2021.738926
    • Lyon, A., van Opbergen, C. J. M., Delmar, M., Heijman, J., & van Veen, T. A. B. (2021). In silico Identification of Disrupted Myocardial Calcium Homeostasis as Proarrhythmic Trigger in Arrhythmogenic Cardiomyopathy. Frontiers in physiology, 12, Article 732573. https://doi.org/10.3389/fphys.2021.732573
    • Sutanto, H., & Lyon, A. (2021). Predicting the neuro-cardio-haemodynamic outcomes of sepsis and its pharmacological interventions: get to the future through numerical equations. The Journal of Physiology, 599(11), 2797-2799. https://doi.org/10.1113/JP281661
    • Lyon, A., van Mourik, M., Cruts, L., Heijman, J., Bekkers, S. C. A. M., Schotten, U., Crijns, H. J. G. M., Linz, D., & Lumens, J. (2021). Both beat-to-beat changes in RR-interval and left ventricular filling time determine ventricular function during atrial fibrillation. EP Europace, 23, I21-I28. https://doi.org/10.1093/europace/euaa387
    • van Osta, N., Kirkels, F., Lyon, A., Koopsen, T., van Loon, T., Cramer, M.-J., Teske, A. J., Delhaas, T., & Lumens, J. (2021). Electromechanical substrate characterization in arrhythmogenic cardiomyopathy using imaging-based patient-specific computer simulations. EP Europace, 23, I153-I160. https://doi.org/10.1093/europace/euaa407
  • 2020
    • Sutanto, H., Lyon, A., Lumens, J., Schotten, U., Dobrev, D., & Heijman, J. (2020). Cardiomyocyte calcium handling in health and disease: Insights from in vitro and in silico studies. Progress in Biophysics & Molecular Biology, 157, 54-75. https://doi.org/10.1016/j.pbiomolbio.2020.02.008
    • Lyon, A., Dupuis, L. J., Arts, T., Crijns, H. J. G. M., Prinzen, F. W., Delhaas, T., Heijman, J., & Lumens, J. (2020). Differentiating the effects of β-adrenergic stimulation and stretch on calcium and force dynamics using a novel electromechanical cardiomyocyte model. American Journal of Physiology-heart and Circulatory Physiology, 319(3), H519-H530. https://doi.org/10.1152/ajpheart.00275.2020
    • van Osta, N., Lyon, A., Kirkels, F., Koopsen, T., van Loon, T., Cramer, M. J., Teske, A. J., Delhaas, T., Huberts, W., & Lumens, J. (2020). Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model. Philosophical Transactions of the Royal Society A: mathematical Physical and Engineering Sciences, 378(2173), Article 20190347. https://doi.org/10.1098/rsta.2019.0347
    • Hermans, B. J. M., Bennis, F. C., Vink, A. S., Koopsen, T., Lyon, A., Wilde, A. A. M., Nuyens, D., Robyns, T., Pison, L., Postema, P. G., & Delhaas, T. (2020). Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization. Heart Rhythm, 17(5), 752-758. https://doi.org/10.1016/j.hrthm.2019.12.020
  • 2019
    • Lyon, A., Mincholé, A., Bueno-Orovio, A., & Rodriguez, B. (2019). Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study. Morphologie, 103(343), 169-179. https://doi.org/10.1016/j.morpho.2019.09.001
    • Minchole, A., Camps, J., Lyon, A., & Rodriguez, B. (2019). Machine learning in the electrocardiogram. Journal of Electrocardiology, 57, S61-S64. https://doi.org/10.1016/j.jelectrocard.2019.08.008