Chaste: An Open Source C++ Library for Computational Physiology and Biology

PLoS Computational Biology

Volumn 9, Issue 3, 2013, Pages

  Mirams, Gary R ^a   Arthurs, Christopher J ^a   Bernabeu, Miguel O ^b   Bordas, Rafel ^a   Cooper, Jonathan ^a   Corrias, Alberto ^c   Davit, Yohan ^a   Dunn, Sara Jane ^d   Fletcher, Alexander G ^a   Harvey, Daniel G ^a   Marsh, Megan E ^e   Osborne, James M ^a   Pathmanathan, Pras ^a,f   Pitt Francis, Joe ^a   Southern, James ^g   Zemzemi, Nejib ^h   Gavaghan, David J ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d MICROSOFT RESEARCH   (United Kingdom)

^e UNIVERSITY OF SASKATCHEWAN   (Canada)

^f CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

^g Fujitsu Laboratories of Europe Ltd   (United Kingdom)

^h INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

C++ (PROGRAMMING LANGUAGE); DISEASES; ELECTROPHYSIOLOGY; HEART; NEUROLOGY; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; ORDINARY DIFFERENTIAL EQUATIONS; STEM CELLS;

C++ LIBRARIES; CANCER TISSUES; CARDIAC ELECTROPHYSIOLOGY; CODE DEVELOPMENT; COMPUTATIONAL BIOLOGY; COMPUTATIONAL PHYSIOLOGY; COMPUTATIONAL SIMULATION; HEART TISSUES; OPEN-SOURCE; SOFT TISSUE;

CODES (SYMBOLS);

ARTICLE; BIOLOGY; CHASTE LIBRARY; COMPUTATIONAL PHYSIOLOGY; COMPUTER PROGRAM; HEART ELECTROPHYSIOLOGY; LIBRARY; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; ONLINE SYSTEM; PHYSIOLOGY; REFERENCE DATABASE; SIMULATION; STRUCTURE ANALYSIS; TUMOR SPHEROID;

EID: 84875998970     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002970     Document Type: Article

References (74)

1. Wilson G, (2006) Where's the real bottleneck in scientific computing? American Scientist 94: 5-6.
2. Cooper J, Cervenansky F, De Fabritiis G, Fenner J, Friboulet D, et al. (2010) The virtual physiological human toolkit. Phil Trans R Soc A 368: 3925-3936.
3. Merali Z, (2010) Computational science: Error, why scientific programming does not compute. Nature 467: 775-777.
4. Baxter SM, Day SW, Fetrow JS, Reisinger SJ, (2006) Scientific software development is not an oxymoron. PLoS Comput Biol 2: e87.
5. Le Nov'ere N, Finney A, Hucka M, Bhalla US, Campagne F, et al. (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotech 23: 1509-1515.
6. Waltemath D, Adams R, Beard DA, Bergmann FT, Bhalla US, et al. (2011) Minimum Information About a Simulation Experiment (MIASE). PLoS Comput Biol 7: e1001122.
7. Hucka M, Finney A, Sauro H, Bolouri H, Doyle JC, et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19: 524-531.
8. Garny A, Nickerson DP, Cooper J, Santos RW, Miller AK, et al. (2008) CellML and associated tools and techniques. Phil Trans R Soc A 366: 3017-3043.
9. Christie GR, Nielsen PMF, Blackett SA, Bradley CP, Hunter PJ, (2009) FieldML: concepts and implementation. Phil Trans R Soc A 367: 1869-1884.
10. Waltemath D, Adams R, Bergmann F, Hucka M, Kolpakov F, et al. (2011) Reproducible computational biology experiments with SED-ML- The Simulation Experiment Description Markup Language. BMC Syst Biol 5: 198.
11. Prlić A, Procter JB, (2012) Ten simple rules for the open development of scientific software. PLoS Comput Biol 8: e1002802.
12. Bradley C, Bowery A, Britten R, Budelmann V, Camara O, et al. (2011) OpenCMISS: a multiphysics & multi-scale computational infrastructure for the VPH/Physiome project. Prog Biophys Mol Biol 107: 32-47.
13. Aguado-Sierra J, Kerckhoffs RCP, Lionetti F, Hunt D, Villongco C, et al. (2010) A Computational Framework for Patient-Specific Multi-Scale Cardiac Modeling. Springer New York. 203-223 pp.
14. Cickovski T, Aras K, Swat M, Merks RMH, Glimm T, et al. (2007) From genes to organisms via the cell: a problem-solving environment for multicellular development. Comput Sci Eng 9: 50-60.
15. Macklin P, Edgerton ME, Thompson A, Cristini V, (2012) Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS) II: From microscopic measurements to macroscopic predictions of clinical progression. J Theor Biol 301: 122-140.
16. Osborne JM, Walter A, Kershaw SK, Mirams GR, Fletcher AG, et al. (2010) A hybrid approach to multi-scale modelling of cancer. Phil Trans R Soc A 368: 5013-5028.
17. Bernabeu M, Cooper J, Gavaghan DJ, Osborne J, Pathmanathan P, et al. (2008) Chaste: using agile programming techniques to develop computational biology software. Phil Trans R Soc A 366: 3111-3136.
18. Pitt-Francis J, Pathmanathan P, Bernabeu MO, Bordas R, Cooper J, et al. (2009) Chaste: A test-driven approach to software development for biological modelling. Comput Phys Commun 180: 2452-2471.
19. Beck K, (1999) Embracing change with extreme programming. Computer 32: 70-77.
20. Karlsson B (2005) Beyond the C++ standard library: an introduction to boost. Addison-Wesley Professional.
21. Folk M, Cheng A, Yates K (1999) HDF5: A file format and I/O library for high performance computing applications. In: Proc. Supercomputing. volume 99.
22. Karypis G, Kumar V, (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20: 359-392.
23. Shewchuk Jr. (1996) Triangle: engineering a 2D quality mesh generator and Delaunay triangulator. In: Lin M, D M, editors. Applied Computational Geometry: Towards Geometric Engineering, volume 1148 of Lecture Notes in Computer Science. Springer-Verlag. pp. 203-222.
24. Si H, G̈artner K (2005) Meshing piecewise linear complexes by constrained Delaunay tetrahedralizations. In: Hanks BW, editor. Proceedings of the 14th International Meshing Roundtable. Berlin Heidelberg: Springer. pp. 147-163.
25. Cedilnik A, Geveci B, Moreland K, Ahrens J, Favre J, (2006) Remote large data visualization in the paraview framework. Eurographics Symposium Parallel Graphics and Visualization 2006: 163-170.
26. Balay S, Gropp WD, McInnes LC, Smith BF (1997) Efficient management of parallelism in object oriented numerical doftware libraries. In: Arge E, Bruaset A, Langtangen H, editors. Modern Software Tools in Scientific Computing. Birkhäuser Press. pp. 163-202.
27. Hindmarsh AC, Brown PN, Grant KE, Lee SL, Serban R, et al. (2005) SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers. ACM Trans Math Software 31: 363-396.
28. van Leeuwen IMM, Mirams GR, Walter A, Fletcher AG, Murray PJ, et al. (2009) An integrative computational model for intestinal tissue renewal. Cell Prolif 42: 617-636.
29. Cooper J, Mirams GR, Niederer S, (2011) High throughput functional curation of cellular models. Prog Biophys Mol Biol 107: 11-20.
30. Mirams GR, Cui Y, Sher A, Fink M, Cooper J, et al. (2011) Simulation of multiple ion channel block provides improved early prediction of compounds' clinical torsadogenic risk. Cardiovasc Res 91: 53-61.
31. Mirams GR, Fletcher AG, Maini PK, Bryne HM, (2012) A theoretical investigation of the effect of proliferation and adhesion on monoclonal conversion in the colonic crypt. J Theor Biol 312: 143-156.
32. Moreira J, Deutsch A, (2002) Cellular automaton models of tumor development: a critical review. Adv Complex Syst 5: 247-268.
33. Perfahl H, Byrne HM, Chen T, Estrella V, Alarc'on T, et al. (2011) Multiscale modelling of vascular tumour growth in 3D: the roles of domain size and boundary conditions. PLoS ONE 6: e14790.
34. Graner F, Glazier JA, (1992) Simulation of biological cell sorting using a two-dimensional extended potts model. Phys Rev Lett 69: 2013-2016.
35. Honda H, Eguchi G, (1980) How much does the cell boundary contract in a monolayered cell sheet? J Theor Biol 84: 575-88.
36. Weliky M, Oster G, (1990) The mechanical basis of cell rearrangement. I. Epithelial morphogenesis during Fundulus epiboly. Development 109: 373-386.
37. Pathmanathan P, Cooper J, Fletcher AG, Mirams GR, Murray PJ, et al. (2009) A computational study of discrete mechanical tissue models. Phys Biol 6: 036001.
38. Lopez-Garcia C, Klein AM, Simons BD, Winton DJ, (2010) Intestinal stem cell replacement follows a pattern of neutral drift. Science 330: 822-825.
39. Snippert HJ, van der Flier LG, Sato T, van Es JH, van den Born M, et al. (2010) Intestinal Crypt Homeostasis Results from Neutral Competition between Symmetrically Dividing Lgr5 Stem Cells. Cell 143: 134-144.
40. Fletcher AG, Breward CJW, Chapman SJ, (2012) Mathematical modeling of monoclonal conversion in the colonic crypt. J Theor Biol 300: 118-133.
41. Dunn SJ, Fletcher AG, Chapman SJ, Gavaghan DJ, Osborne JM, (2012) Modelling the role of the basement membrane beneath a growing epithelial monolayer. J Theor Biol 298: 82-91.
42. Dunn SJ, Appleton PL, Nelson SA, N̈athke IS, Gavaghan DJ, 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 8: e1002515.
43. Eisenhoffer GT, Loftus PD, Yoshigi M, Otsuna H, Chien CB, et al. (2012) Crowding induces live cell extrusion to maintain homeostatic cell numbers in epithelia. Nature 484: 546-549.
44. Collier JR, Monk NAM, Maini PK, Lewis JH, (1996) Pattern formation by lateral inhibition with feedback: a mathematical model of Delta-Notch intercellular signalling. J Theor Biol 183: 429-446.
45. Crosnier C, Stamataki D, Lewis J, (2006) Organizing cell renewal in the intestine: stem cells, signals and combinatorial control. Nat Rev Genet 7: 349-359.
46. Bordas R, Carpentieri B, Fotia G, Maggio F, Nobes R, et al. (2009) Simulation of cardiac electrophysiology on next-generation high-performance computers. Phil Trans R Soc A 367: 1951-1969.
47. Corrias A, Pathmanathan P, Gavaghan DJ, Buist ML, (2012) Modelling tissue electrophysiology with multiple cell types: applications of the extended bidomain framework. Integr Biol 4: 192-201.
48. Gima K, Rudy Y, (2002) Ionic current basis of electrocardiographic waveforms. Circ Res 90: 889-896.
49. Qu Z, Xie F, Garfinkel A, Weiss JN, (2000) Origins of spiral wave meander and breakup in a two-dimensional cardiac tissue model. Ann Biomed Eng 28: 755-771.
50. Luo CH, Rudy Y, (1991) A model of the ventricular cardiac action potential. depolarization, repolarization, and their interaction. Circ Res 68: 1501-1526.
51. Zemzemi N, Benabeu M, Saiz J, Cooper J, Pathmanathan P, et al. (2012) Computational assessment of drug-induced effects on the electrocardiogram: from ion channel block to body surface potentials. Br J Pharmacol 168 (3) (): 718-33.
52. Mirams GR, Noble D, (2011) Is it time for in silico simulation of drug cardiac side effects? Ann NY Acad Sci 1245: 44-47.
53. Mirams GR, Davies MR, Cui Y, Kohl P, Noble D, (2012) Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing. Br J Pharmacol 167: 932-945.
54. Bernabeu MO, Wallman M, Rodríguez B (2010) Shock-induced arrhythmogenesis in the human heart: A computational modelling study. In: Engineering inMedicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. pp. 760-763. doi:10.1109/IEMBS.2010.5626338.
55. Cooper J, Corrias A, Gavaghan D, Noble D, (2011) Considerations for the use of cellular electrophysiology models within cardiac tissue simulations. Prog Biophys Mol Biol 107: 74-80.
56. Pathmanathan P, Mirams GR, Southern J, Whiteley J, (2011) The significant effect of the choice of ionic current integration method in cardiac electro-physiological simulations. Int J Numer Meth Biomed Engng 27: 1751-1770.
57. Walmsley J, Burrage K, Pitt-Francis JM, Mirams GR, Rodriguez B, (2012) Stochasticity in action potential duration enhances dispersion of repolarisation at fast pacing rates. Biophys J 102: 592a-593a.
58. Dutta S, Bishop M, Pathmanathan P, Lee P, Kohl P, et al. (2011) Interpreting optical mapping recordings in the ischemic heart: a combined experimental and computational investigation. In: Metaxas D, Axel L, editors, Functional Imaging and Modeling of the Heart. volume 6666 of Lecture Notes in Computer Science. Springer Berlin/Heidelberg. pp. 20-27. doi: 10.1007/978-3-642-21028-0 3.
59. Pueyo E, Corrias A, Virag L, Jost N, Szel T, et al. (2011) A multiscale investigation of repolarization variability and its role in cardiac arrhythmogenesis. Biophys J 101: 2892-2902.
60. Cooper J, McKeever S, Garny A (2006) On the application of partial evaluation to the optimisation of cardiac electrophysiological simulations. In: PEPM'06: Proceedings of the 2006 ACMSIGPLAN Symposium on Partial Evaluation and Semantics-Based Program Manipulation. New York, NY, USA: ACM Press, pp. 12-20. doi:10.1145/1111542.1111546.
61. Marsh ME (2012) An assessment of numerical methods for cardiac simulation [Master's thesis]. University of Saskatchewan.
62. Bernabeu MO, Pathmanathan P, Pitt-Francis J, Kay D, (2010) Stimulus protocol determines the most computationally efficient preconditioner for the bidomain equations. IEEE Trans Biomed Eng 57: 2806-2815.
63. Bernabeu MO, Kay D, (2011) Scalable parallel preconditioners for an open source cardiac electrophysiology simulation package. Procedia Comp Sci 4: 821-830.
64. Southern J, Gorman GJ, Piggott MD, Farrell PE, (2012) Parallel anisotropic mesh adaptivity with dynamic load balancing for cardiac electrophysiology. J Comput Sci 3: 8-16.
65. Pathmanathan P, Bernabeu M, Bordas R, Cooper J, Garny A, et al. (2010) A numerical guide to the solution of the bidomain equations of cardiac electrophysiology. Prog Biophys Mol Biol 102: 136-155.
66. Arthurs CJ, Bishop MJ, Kay D, (2012) Efficient simulation of cardiac electrical propagation using high order finite elements. J Comput Phys 231: 2964-3962.
67. Niederer SA, Kerfoot E, Benson AP, Bernabeu MO, Bernus O, et al. (2011) Verification of cardiac tissue electrophysiology simulators using an n-version benchmark. Phil Trans R Soc A 369: 4331-4351.
68. Pathmanathan P, Bernabeu MO, Niederer SA, Gavaghan DJ, Kay D, (2012) Computational modelling of cardiac electrophysiology: explanation of the variability of results from different numerical solvers. Int J Numer Methods Biomed Eng 28: 890-903.
69. Strazdins P, Hegland M (2011) Performance analysis of a cardiac simulation code using IPM. In: Proceedings of the second workshop on Scalable algorithms for large-scale systems. pp. 29-32. doi:10.1145/2133173.2133186.
70. Bernabeu MO, Southern J, Wilson N, Strazdins P, Cooper J, et al. (2012) Chaste: a case study of parallelisation of an open source finite element solver with applications to computational cardiac electrophysiology simulation. International Journal of High Performance Computing In press. doi:10.1177/1094342012474997.
71. Pathmanathan P, Chapman SJ, Gavaghan DJ, Whiteley JP, (2010) Cardiac electromechanics: the effect of contraction model on the mathematical problem and accuracy of the numerical scheme. Q J Mech Appl Math 63: 375-399.
72. Sanders KM, Koh SD, Ward SM, (2006) Interstitial cells of Cajal as pacemakers in the gastrointestinal tract. Annu Rev Physiol 68: 307-343.
73. Buist ML, Corrias A, Poh YC, (2010) A model of slow wave propagation and entrainment along the stomach. Ann Biomed Eng 38: 3022-3030.
74. Corrias A, Du P, Buist ML (2013) New Advances in Gastrointestinal Motility Research, Springer, chapter Modelling Tissue Electrophysiology in the GI Tract: Past, Present and Future. Lecture Notes in Computational Vision and Biomechanics. In press.