Main publications

If you want a single citation for Chaste, please use one of these, especially if you publish a paper using Chaste!

  • Cooper et al. 2020. Chaste: Cancer, Heart and Soft Tissue Environment. J Open Source Softw 5:1848. doi:10.21105/joss.01848
  • Mirams et al. 2013. Chaste: An open source C++ library for computational physiology and biology. PLoS Comput Biol 9:e1002970. doi:10.1371/journal.pcbi.1002970
  • Pitt-Francis et al. 2009. Chaste: a test-driven approach to software development for biological modelling. Comput Phys Commun 180:2452-2471. doi:10.1016/j.cpc.2009.07.019

Publications using Chaste

Here is a list of preprints and peer-reviewed publications that have used Chaste from its inception up to October 2022.


  1. Galappaththige et al. 2022. Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar. PLoS Computational Biology 18(10): e1010541. doi:10.1371/journal.pcbi.1010541
  2. Johnson et al. 2022. ChemChaste: Simulating spatially inhomogenous biochemical reaction-diffusion systems for modelling cell-environment feedbacks. GigaScience 11:giac051. doi:10.1093/gigascience/giac051
  3. Cook et al. 2022. Modelling cellular interactions and dynamics during kidney morphogenesis. Bull Math Biol 84:8. doi:10.1007/s11538-021-00968-3
  4. Donath et al. 2022. Investigation of colonic regeneration via precise damage application using femtosecond laser-based nanosurgery. Cells 11:1143. doi:10.3390/cells11071143
  5. Middleton et al. 2022. Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19. Comput Biol Med 145:105513. doi:10.1016/j.compbiomed.2022.105513


  1. Conrad et al. 2021. The biomechanical basis of biased epithelial tube elongation in lung and kidney development. Development 148:dev194209. doi:10.1242/dev.194209
  2. Germano & Osborne. 2021. A mathematical model of cell fate selection on a dynamic tissue. J Theor Biol 514:110535. doi:10.1016/j.jtbi.2020.110535
  3. Brunt et al. 2021. Vangl2 promotes the formation of long cytonemes to enable distant Wnt/beta-catenin signaling. Nat Commun 12:2058. doi:10.1038/s41467-021-22393-9
  4. Miller et al. 2021. Maintaining the proliferative cell niche in multicellular models of epithelia. J Theor Biol 527:110807. doi:10.1016/j.jtbi.2021.110807
  5. Hendrix et al. 2021. chaste codegen: automatic CellML to C++ code generation with fixes for singularities and automatically generated Jacobians. Wellcome Open Res 6:261. doi:10.12688/wellcomeopenres.17206.1


  1. Cooper et al. 2020. Chaste: Cancer, Heart and Soft Tissue Environment. J Open Source Softw 5:1848. doi:10.21105/joss.01848
  2. Sego et al. 2020. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness. PLOS Comput Biol 16:e1008451. doi:10.1371/journal.pcbi.1008451
  3. Bernabeu et al. 2020. Abnormal morphology biases haematocrit distribution in tumour vasculature and contributes to heterogeneity in tissue oxygenation. Proc Natl Acad Sci USA 117:27811-27819. doi:10.1073/pnas.2007770117
  4. Romijn et al. 2020. Modelling the effect of subcellular mutations on the migration of cells in the colorectal crypt. BMC Bioinformatics 21:95. doi:10.1186/s12859-020-3391-3
  5. Pak et al. 2020. Pakman: a modular, efficient and portable tool for approximate Bayesian inference. J Open Source Softw 5:1716. doi:10.21105/joss.01716
  6. Ward et al. 2020. Cross-talk between Hippo and Wnt signalling pathways in intestinal crypts: insights from an agent-based model. Comput Struct Biotechnol J 18:230-240. doi:10.1016/j.csbj.2019.12.015
  7. Grimes & Fletcher. 2020. Close encounters of the cell kind: the impact of contact inhibition on tumor growth and cancer models. Bull Math Biol 82:20. doi:10.1007/s11538-019-00677-y
  8. Shalaby et al. 2020. Simulating the effect of sodium channel blockage on cardiac electromechanics. Proc Inst Mech Eng H 234:16-27. doi:10.1177/0954411919882514
  9. Narciso et al. 2020. Semi-automatic tool to identify heterogeneity zones in LGE-CMR and incorporate the result into a 3D model of the left ventricle. ICIAR2020 238-46. doi:10.1007/978-3-030-50516-5_21
  10. Wang et al. 2020. A mono-bidomain electrophysiological simulation method for electrical defibrillation research. ICBBB2020 107-113. doi:10.1145/3386052.3386074
  11. Langham et al. 2018. Modeling shape selection of buckled dielectric elastomers. J Appl Phys 123:065102. doi:10.1063/1.5012848


  1. Finegan et al. 2019. The tricellular vertex-specific adhesion molecule Sidekick facilitates polarised cell intercalation during Drosophila axis extension. PLOS Biol 7:e3000522. doi:10.1371/journal.pbio.3000522
  2. Germann et al. 2019. yalla: GPU-powered spheroid models for mesenchyme and epithelium. Cell Syst 8:261-6. doi:10.1016/j.cels.2019.02.007
  3. Murray et al. 2019. Cell cycle regulation of oscillations yields coupling of growth and form in a computational model of the presomitic mesoderm. J Theor Biol 481:75-83. doi:10.1016/j.jtbi.2019.05.006
  4. Panda & Buist. 2019. A finite element approach for gastrointestinal tissue mechanics. Int J Num Meth Biomed Eng 35:e3269. doi:10.1002/cnm.3269
  5. Mincholé et al. 2019. MRI-based computational torso/biventricular multiscale models to investigate the impact of anatomical variability on the ECG QRS complex. Front Physiol 10:1103. doi:10.3389/fphys.2019.01103
  6. Cervi & Spiteri. 2019. A comparison of fourth-order operator splitting methods for cardiac simulations. Appl Numer Math 145:227-35. doi:10.1016/j.apnum.2019.06.002
  7. Zhou et al. 2019. Investigating the complex arrhythmic phenotype caused by the gain-of-function mutation KCNQ1-G229D. Front Physiol 10:259. doi:10.3389/fphys.2019.00259
  8. Glimm et al. 2019. From automated MRI scan to finite elements. Reactive Systems to Cyber-Physical Systems Springer. doi:10.1007/978-3-030-31514-6_3
  9. Galappaththige et al. 2019. Effect of heart structure on ventricular fibrillation in the rabbit: a simulation study. Front Physiol 10:564. doi:10.3389/fphys.2019.00564
  10. Tomek et al. 2019. Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. eLife 8:e48890. doi:10.7554/eLife.48890
  11. Grace et al. 2019. High-resolution noncontact charge-density mapping of endocardial activation. JCI Insight 4:e126422. doi:10.1172/jci.insight.126422
  12. Kalinin et al. 2019. Solving the inverse problem of electrocardiography on the endocardium using a single layer source. Front Physiol 10:58. doi:10.3389/fphys.2019.00058


  1. Walter et al. 2018. Physical defects in basement membrane-mimicking collagen-IV matrices trigger cellular EMT and invasion. Integr Biol 10:342-55. doi:10.1039/c8ib00034d
  2. Mathur et al. 2018. Predicting collective migration of cell populations defined by varying repolarization dynamics. Biophys J 115:2474-85. doi:10.1016/j.bpj.2018.11.013
  3. Waites et al. 2018. An information-theoretic measure for patterning in epithelial tissues. IEEE Access 6:40302-12. doi:10.1109/ACCESS.2018.2853624
  4. Cervi & Spiteri. 2018. High-order operator splitting for the bidomain and monodomain models. SIAM J Sci Comput 40:A769-86. doi:10.1007/978-3-319-96649-6_2
  5. Daly et al. 2018. Inference-based assessment of parameter identifiability in nonlinear biological models. J R Soc Interface 1520180318. doi:10.1098/rsif.2018.0318
  6. Viceconti et al. 2018. Vph-hf: A software framework for the execution of complex subject-specific physiology modelling workflows. J Comput Sci 25:101-14. doi:10.1016/j.jocs.2018.02.009
  7. Cardone-Noott et al. 2018. Strategies of data layout and cache writing for input-output optimization in high performance scientific computing: applications to the forward electrocardiographic problem. PLOS ONE 13:e0202410. doi:10.1371/journal.pone.0202410
  8. Muszkiewicz et al. 2018. From ionic to cellular variability in human atrial myocytes: an integrative computational and experimental study. Am J Physiol - Heart C 314:H895-916. doi:10.1152/ajpheart.00477.2017
  9. Filos et al. 2018. Multiple P-wave morphologies in paroxysmal atrial fibrillation patients during sinus rhythm: a simulation study. Computing in Cardiology Conference 45:1-4. doi:10.22489/CinC.2018.320
  10. Lim et al. 2018. The role of conductivity discontinuities in design of cardiac defibrillation. Chaos 28:013106. doi:10.1063/1.5019367


  1. Godwin et al. 2017. An extended model for culture-dependent heterogenous gene expression and proliferation dynamics in mouse embryonic stem cells. npj Syst Biol Appl 3:19. doi:10.1038/s41540-017-0020-5
  2. Grogan et al. 2017. Microvessel Chaste: an open library for spatial modeling of vascularized tissues. Biophys J 112:1767-1772. doi:10.1016/j.bpj.2017.03.036
  3. Kursawe et al. 2017. Impact of implementation choices on quantitative predictions of cell-based computational models. J Comput Phys 345:752-767. doi:10.1016/
  4. Carroll et al. 2017. Interkinetic nuclear migration and basal tethering facilitates post-mitotic daughter separation in intestinal organoids. J Cell Sci 130:3862-3877. doi:10.1242/jcs.211656
  5. Grogan et al. 2017. Predicting the influence of microvascular structure on tumor response to radiotherapy. IEEE Trans Biomed Eng 64:504-511. doi:10.1109/TBME.2016.2606563
  6. Cooper et al. 2017. Numerical analysis of the immersed boundary method for cell-based simulation. SIAM J Sci Comput doi:10.1137/16M1092246
  7. Osborne et al. 2017. Comparing individual-based approaches to modelling the self-organization of multicellular tissues. PLOS Comput Biol 13:e1005387. doi:10.1371/journal.pcbi.1005387
  8. Daly et al. 2017. Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models. J R Soc Interface 14:20170340. doi:10.1098/rsif.2017.0340
  9. Zacur et al. 2017. MRI-based heart and torso personalization for computer modeling and simulation of cardiac electrophysiology. In: Cardoso M. et al. (eds) Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound BIVPCS 2017, POCUS 2017. Lecture Notes in Computer Science, vol 10549. Springer, Cham. doi:10.1007/978-3-319-67552-7_8
  10. Dutta et al. 2017. Electrophysiological properties of computational human ventricular cell action potential models under acute ischemic conditions. Prog Biophys Mol Biol 129:40-52. doi:10.1016/j.pbiomolbio.2017.02.007
  11. Corsi et al. 2017. Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients. Sci Rep 7:42492. doi:10.1038/srep42492
  12. Abdullah et al. 2017. Universal statistics of epithelial tissue topology. arXiv doi:1710.08527
  13. Sathar et al. 2015. A comparison of solver performance for complex gastric electrophysiology models. Engineering in Medicine and Biology Society, 2015 37th Annual International Conference of the IEEE doi:10.1109/EMBC.2015.7318643
  14. Sathar et al. 2015. A multiscale tridomain model for simulating bioelectric gastric pacing. IEEE Trans Biomed Eng 62. doi:10.1109/TBME.2015.2444384
  15. Moreno et al. 2015. A new KCNQ1 mutation at the S5 segment that impairs its association with KCNE1 is responsible for short QT syndrome. Cardiovasc Res 613-623 doi:10.1093/cvr/cvv196
  16. Sathar et al. 2015. Tissue specific simulations of interstitial cells of cajal networks using unstructured meshes. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 8062-8065. doi:10.1109/EMBC.2015
  17. Gao et al. 2015. A stochastic algorithm for generating realistic virtual interstitial cell of Cajal networks. IEEE Trans Biomed Eng 62:2070-2078. doi:10.1109/TBME.2015.2412533
  18. Corsi et al. 2017. Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients. Sci Rep 7:42492. doi:10.1109/TBME.2015.2412533


  1. Langlands et al. 2016. Paneth cell-rich regions separated by a cluster of Lgr5+ cells initiate crypt fission in the intestinal stem cell niche. PLoS Biol 14:e1002491. doi:10.1371/journal.pbio.1002491
  2. Tetley et al. 2016. Unipolar distributions of junctional Myosin II identify cell stripe boundaries that drive cell intercalation throughout Drosophila axis extension. eLife 5:e12094. doi:10.7554/eLife.12094
  3. Dunn et al. 2016. Combined changes in Wnt signalling response and contact inhibition induce altered proliferation in radiation treated intestinal crypts. Mol Biol Cell doi:h10.1091/mbc.E15-12-0854
  4. Campos et al. 2016. Lattice Boltzmann method for parallel simulations of cardiac electrophysiology using GPUs. J Comput Appl Math 295:70-82. doi:10.1016/
  5. Johnstone et al. 2016. Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? J Mol Cell Cardiol 96:49-62. doi:10.1016/j.yjmcc.2015.11.018
  6. Mahoney et al. 2016. Connexin 43 contributes to electrotonic conduction across scar tissue in the intact heart. Sci Rep 6:26744. doi:10.1038/srep26744
  7. Davies et al. 2016. Recent developments in using mechanistic cardiac modelling for drug safety evaluation. Drug Discov Today 21:924-38. doi:10.1016/j.drudis.2016.02.003
  8. Zhou et al. 2016. In vivo and in silico investigation into mechanisms of frequency dependence of repolarization alternans in human ventricular cardiomyocytes. Circ Res 118:266-78. doi:10.1161/CIRCRESAHA.115.307836
  9. Passini et al. 2016. Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti-arrhythmic therapies in human hypertrophic cardiomyopathy. J Mol Cell Cardiol 96:72-81. doi:10.1016/j.yjmcc.2015.09.003
  10. Cooper et al. 2016. The Cardiac Electrophysiology Web Lab. Biophys J 11:292−300. doi:10.1016/j.bpj.2015.12.012
  11. Lekadir et al. 2016. Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model. Med Image Anal 27:105-116. doi:10.1016/
  12. Corrado et al. 2016. Stability analysis of the POD reduced order method for solving the bidomain model in cardiac electrophysiology. Math Biosci 272:81-91. doi:10.1016/j.mbs.2015.12.005


  1. Kursawe et al. 2015. Capabilities and limitations of tissue size control through passive mechanical forces. PLoS Comput Biol 11:e1004679. doi:10.1371/journal.pcbi.1004679
  2. Atwell et al. 2015. Mechano-logical model of C. elegans germ line suggests feedback on the cell cycle. Development 142:3902-3911. doi:10.1242/dev.126359
  3. Rubinacci et al. 2015. Cognac: a chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer. Cancer Inform 14:53. doi:10.4137/CIN.S19965
  4. Osborne. 2015. Multiscale model of colorectal cancer using the cellular Potts framework. Cancer Inform 14:83. doi:10.4137/CIN.S19332
  5. Harvey et al. 2015. A parallel implementation of an off-lattice individual-based model of multicellular populations. Comput Phys Commun 192:130-137. doi:10.1016/j.cpc.2015.03.005
  6. Franzetti et al. 2015. Combined approach for the biomechanical characterization of skin lesions. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society doi:10.1109/EMBC.2015.7318511
  7. Cooper et al. 2015. Cellular cardiac electrophysiology modeling with Chaste and CellML. Front Physiol doi:[10.3389/fphys.2014.00511](
  8. Samanta et al. 2015. Ca2+ channel re−localization to plasma−membrane microdomains strengthens activation of Ca2+−dependent nuclear gene expression. Cell Rep 12:203−216 doi:10.1016/j.celrep.2015.06.018
  9. Daly et al. 2015. Hodgkin-Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods. R Soc Open Sci 2:150499. doi:10.1098/rsos.150499
  10. Williams & Mirams. 2015. A web portal for in−silico action potential predictions. J Pharmacol Toxicol Methods 75:10−16. doi:10.1016/j.vascn.2015.05.002
  11. Walmsley et al. 2015. Application of stochastic phenomenological modelling to cell-to-cell and beat-to-beat electrophysiological variability in cardiac tissue. J Theor Biol 365:325-36. doi:10.1016/j.jtbi.2014.10.029
  12. Zemzemi & Rodriguez. 2015. Effects of L-type calcium channel and human ether-a-go-go related gene blockers on the electrical activity of the human heart: a simulation study. Europace 326-333. doi:10.1093/europace/euu122


  1. Baker et al. 2014. Quantification of crypt and stem cell evolution in the normal and neoplastic human colon. Cell Rep 8:940-947. doi:10.1016/j.celrep.2014.07.019
  2. Hu & Cucinotta. 2014. Epidermal homeostasis and radiation responses in a multiscale tissue modeling framework. Integr Biol 6:76-89. doi:10.1039/C3IB40141C
  3. Koke et al. 2014. A computational model of nuclear self-organisation in syncytial embryos. J Theor Biol 359:92-100. doi:10.1016/j.jtbi.2014.06.001
  4. Nelson et al. 2014. STI-GMaS: an open-source environment for simulation of sexually-transmitted infections. BMC Syst Biol 20148:66 doi:10.1186/1752-0509-8-66
  5. Pathmanathan & Gray. 2014. Verification of computational models of cardiac electro-physiology. Int J Numer Methods Biomed Eng 30:525-544. doi:10.1002/cnm.2615
  6. Mirams et al. 2014. Prediction of Thorough QT study results using action potential simulations based on ion channel screens. J Pharmacol Toxicol Methods 70:246−254. doi:10.1016/j.vascn.2014.07.002
  7. Passini et al. 2014. Late sodium current inhibition counteracts pro-arrhythmic mechanisms in human hypertrophic cardiomyopathy. Computing in Cardiology 2014, Cambridge, MA, 2014, pp. 861-864. URL:
  8. Carapella et al. 2014. Quantitative study of the effect of tissue microstructure on contraction in a computational model of rat left ventricle. PLOS ONE 9:e92792. doi:10.1371/journal.pone.0092792
  9. Sadrieh et al. 2014. Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2. Nat Commun 5:5069. doi:10.1038/ncomms6069
  10. Cardone-Noott. 2014. A computational investigation into the effect of infarction on clinical human electrophysiology biomarkers. Computing in Cardiology 2014, Cambridge, MA, 2014, pp. 673-676. URL:
  11. Lekadir et al. 2014. Effect of statistically derived fiber models on the estimation of cardiac electrical activation. IEEE Trans Biomed Eng 61:2740-2748. doi:10.1109/TBME.2014.2327025
  12. Sathar et al. 2014. A biophysically based finite-state machine model for analyzing gastric experimental entrainment and pacing recordings. Ann Biomed Eng 42:858-870. doi:10.1007/s10439-013-0949-5
  13. Gao et al. 2014. Developmental changes in postnatal murine intestinal interstitial cell of Cajal network structure and function. Ann Biomed Eng 42:1729-1739. doi:10.1007/s10439-014-1021-9
  14. Bartolucci et al. 2014. Linking a novel mutation to its short QT phenotype through multiscale computational modelling. Computing in Cardiology 2014, Cambridge, MA, 2014, pp. 1017-1020. URL:


  1. Mirams et al. 2013. Chaste: an open source C++ library for computational physiology and biology. PLoS Comput Biol 9:e1002970. doi:10.1371/journal.pcbi.1002970
  2. Cooper & Osborne. 2013. Connecting models to data in multiscale multicellular tissue simulations. Proc Comput Sci 18:712-721. doi:10.1016/j.procs.2013.05.235
  3. Dunn et al. 2013. Computational models reveal a passive mechanism for cell migration in the crypt. PLOS ONE 8:e80516. doi:10.1371/journal.pone.0080516
  4. Fletcher et al. 2013. Implementing vertex dynamics models of cell populations in biology within a consistent computational framework. Prog Biophys Mol Biol 113:299-326. doi:10.1016/j.pbiomolbio.2013.09.003
  5. Davit et al. 2013. Validity of the Cauchy-Born rule applied to discrete cellular-scale models of biological tissues. Phys Rev E 87:042724. doi:10.1103/PhysRevE.87.042724
  6. Figueredo et al. 2013. On−lattice agent−based simulation of populations of cells within the open−source Chaste framework. Interface Focus 3. doi:10.1098/rsfs.2012.0081
  7. Beattie et al. 2013. Evaluation of an in silico cardiac safety assay: using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge. J Pharmacol Toxicol Methods 68:88−96. doi:10.1016/j.vascn.2013.04.004
  8. Elkins et al. 2013.Variability in high−throughput ion channel screening data and consequences for cardiac safety assessment. J Pharmacol Toxicol Methods 68:112−122. doi:10.1016/j.vascn.2013.04.007
  9. Britton et al. 2013. Experimentally−calibrated population of models predicts and explains inter−subject variability in cardiac cellular electrophysiology. Proc Natl Acad Sci USA doi:10.1073/pnas.1304382110
  10. Walmsley et al. 2013. mRNA expression levels in failing human hearts predict cellular electrophysiological remodelling: a population−based simulation study. PLOS ONE 8:e56359. doi:10.1371/journal.pone.0056359
  11. Zemzemi et al. 2013. Computational assessment of drug−induced effects on the electrocardiogram: from ion channel block to body surface potentials. Br J Pharmacol 168:718−733. doi:10.1111/j.1476-5381.2012.02200.x
  12. Passini et al. 2013. Computational analysis of Head-Down Bed Rest effects on cardiac action potential duration. Computing in Cardiology 2013, Zaragoza, 2013, pp. 357-360. URL:[] (
  13. Walmsley et al. 2013. Estimation of conductivity tensors from human ventricular optical mapping recordings. Functional Imaging and Modeling of the Heart 7495:224-231. doi:10.1007/978-3-642-38899-6_27
  14. Dutta et al. 2013. Ionic mechanisms of variability in electrophysiological properties in ischemia: a population-based study. Computing in Cardiology 2013, Zaragoza, 2013, pp. 691-694. URL:
  15. Agudelo−Toro & Neef. 2013. Computationally efficient simulation of electrical activity at cell membranes interacting with self−generated and externally imposed electric fields. J Neural Eng 10:026019. doi:10.1088/1741-2560/10/2/026019


  1. Dunn et al. 2012. A two−dimensional model of the colonic crypt accounting for the role of the basement membrane and pericryptal fibroblast sheath. PLoS Comput Biol e1002515. doi:10.1371/journal.pcbi.1002515
  2. Dunn et al. 2012. Modelling the role of the basement membrane in the colonic epithelium. J Theor Biol 298:82−91. doi:10.1016/j.jtbi.2011.12.013
  3. Mirams et al. 2012. A theoretical investigation of the effect of proliferation and adhesion on monoclonal conversion in the colonic crypt. J Theor Biol 312:143-156. doi:10.1016/j.jtbi.2012.08.002
  4. Fletcher et al. 2012. Mathematical modelling of monoclonal conversion in the colonic crypt. J Theor Biol 300:118-133. doi:10.1016/j.jtbi.2012.01.021
  5. Slaymaker et al. 2012. On an infrastructure to support sharing and aggregating pre− and post−publication systems biology research data. Syst Synth Biol 6:35-49. doi:10.1007/s11693-012-9095-x
  6. Bordas et al. 2012. A bidomain model of the cardiac specialized conduction system of the heart. SIAM J Appl Math. 2012. doi:10.1137/11082796X
  7. Sanchez et al. 2012. The Na+/K+ pump is an important modulator of refractoriness and rotor dynamics in human atrial tissue. Am J Physiol Heart Circ Physiol 302:H1146-H1159. doi:10.1152/ajpheart.00668.2011
  8. Wallman et al. 2012. A comparative study of graph−based‚ eikonal‚ and monodomain simulations for the estimation of cardiac activation times. IEEE Trans Biomed Eng 59:1739. doi:10.1109/tbme.2012.2193398
  9. Mirams et al. 2012. Application of cardiac electrophysiology simulations to pro−arrhythmic safety testing. Br J Pharmacol 167:932−945. doi:10.1111/j.1476-5381.2012.02020.x
  10. Pathmanathan et al. 2012. Computational modelling of cardiac electro−physiology: explanation of the variability of results from different numerical solvers. Int J Numer Method Biomed Eng 28:890−903. doi:10.1002/cnm.2467
  11. Southern et al. 2012. Parallel anisotropic mesh adaptivity with dynamic load balancing for cardiac electrophysiology. J Comput Sci 3:8-16. doi:10.1016/j.jocs.2011.11.002


  1. Murray et al. 2011. Comparing a discrete and continuum model of the intestinal crypt. Phys Biol 8:026011. doi:10.1088/1478-3975/8/2/026011
  2. Pueyo et al. 2011. A multi−scale investigation of repolarization variability and its role in cardiac arrhythmogenesis. Biophys J 101:2892-2902. doi:10.1016/j.bpj.2011.09.060
  3. Bordas et al. 2011. Rabbit−specific ventricular model of cardiac electrophysiological function including specialized conduction system. Prog Biophys Mol Biol 107:90-100. doi:10.1016/j.pbiomolbio.2011.05.002
  4. Corrias et al. 2011. Ionic mechanisms of electrophysiological properties and repolarization abnormalities in rabbit Purkinje fibers. Am J Physiol Heart Circ Physiol 300:H1806-H1813. doi:10.1152/ajpheart.01170.2010
  5. Wallman et al. 2011. Estimation of activation times in cardiac tissue using graph based methods. Functional Imaging and Modeling of the Heart 71-79. doi:10.1007/978-3-642-21028-0_9
  6. Zemzemi et al. 2011. Simulating drug−induced effects on the heart: from ion channel to body surface electrocardiogram. Functional Imaging and Modeling of the Heart 259-266. doi:10.1111/j.1476-5381.2012.02200.x
  7. Dutta et al. 2011. Interpreting optical mapping recordings in the ischemic heart: a combined experimental and computational investigation. Functional Imaging and Modeling of the Heart 6666:20-27. doi:10.1007/978-3-642-21028-0_3
  8. Mirams et al. 2011. Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk. Cardiovasc Res. 91:53-61. doi:10.1093/cvr/cvr044
  9. Niederer et al. 2011. High−throughput functional curation of cellular electrophysiology models. Prog Biophys Mol Biol 107:11−20. doi:10.1016/j.pbiomolbio.2011.06.003
  10. Niederer et al. 2011. Verification of cardiac tissue electrophysiology simulators using an N-version benchmark. Phil Trans R Soc A 369:4331-4351. doi:10.1098/rsta.2011.0139
  11. Bernabeu & Kay. 2011. Scalable parallel preconditioners for an open source cardiac electrophysiology simulation package. Proc Comput Sci 4:821-830. doi:10.1016/j.procs.2011.04.087
  12. Noble et al. 2011. Considerations for the use of cellular electrophysiology models within cardiac tissue simulations. Prog Biophys Mol Biol 107. No. 1. 74−80. 2011. doi:10.1016/j.pbiomolbio.2011.06.002
  13. Pathmanathan et al. 2011. The significant effect of the choice of ionic current integration method in cardiac electro-physiological simulations. Int. J. Numer. Methods Biomed. Eng 27:1751-1770. doi:10.1002/cnm.1438


  1. Murray et al. 2010. Modelling spatially regulated beta-catenin dynamics and invasion in intestinal crypts. Biophys J 99:716-725. doi:10.1016/j.bpj.2010.05.016
  2. Osborne et al. 2010. A hybrid approach to multi−scale modelling of cancer. Phil Trans R Soc A 368:5013-5028. doi:10.1098/rsta.2010.0173
  3. Bordas et al. 2010. Integrated approach for the study of anatomical variability in the cardiac Purkinje system: From high resolution MRI to electrophysiology simulation. Conf Proc IEEE Eng Med Biol Soc 1:6793-6. doi:10.1109/iembs.2010.5625979
  4. Rodriguez et al. 2010. Arrhythmic risk biomarkers for the assessment of drug cardiotoxicity: from experiments to computer simulations. Phil Trans R Soc A 368:3001-3025. doi:10.1098/rsta.2010.0083
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