1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Biomedical Engineering
  4. Volume 18, 2016
  5. Article

Annual Review of Biomedical Engineering

Volume 18, 2016

The Virtual Physiological Human: Ten Years After

Abstract

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype–phenotype interaction and by a “systemic” nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible—the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done.

Keyword(s): computational physiology, in silico medicine, physiome, Virtual Physiological Human
Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-110915-114742
2016-07-11
2024-04-23
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/18/1/annurev-bioeng-110915-114742.html?itemId=/content/journals/10.1146/annurev-bioeng-110915-114742&mimeType=html&fmt=ahah

Literature Cited

  1. Bernard C.1.  1865. Introduction à l'étude de la médecine expérimentale Paris: Baillière
  2. Noble D.2.  2003. The future: putting Humpty-Dumpty together again. Biochem. Soc. Trans. 31:156–58 [Google Scholar]
  3. Brock DC. 3.  2006. Understanding Moore's Law: Four Decades of Innovation Philadelphia: Chem. Herit.
  4. Bassingthwaighte JB.4.  1995. Toward modeling the human physionome. Adv. Exp. Med. Biol. 382:331–39 [Google Scholar]
  5. Bassingthwaighte JB.5.  2000. Strategies for the physiome project. Ann. Biomed. Eng. 28:1043–58 [Google Scholar]
  6. Hunter PJ.6.  2004. The IUPS Physiome Project: a framework for computational physiology. Prog. Biophys. Mol. Biol. 85:551–69 [Google Scholar]
  7. Hunter P, Chapman T, Coveney PV, de Bono B, Diaz V. 7.  et al. 2013. A vision and strategy for the virtual physiological human: 2012 update. Interface Focus 3:20130004 [Google Scholar]
  8. Ayache N, Boissel JP, Brunak S, Clapworthy G, Fingberg J. 8.  et al. 2005. Towards Virtual Physiological Human: multilevel modelling and simulation of the human anatomy and physiology White pap., Eur. Comm., Barcelona. http://www.vph-institute.org/upload/file517569145f61b.pdf
  9. Noble D.9.  2006. The Music of Life: Biology Beyond Genes Oxford, UK: Oxford Univ. Press
  10. 10. STEP Consort 2007. Seeding the EuroPhysiome: A Roadmap to the Virtual Physiological Human Brussels: VPH Inst.
  11. Hunter P, Kurachi Y, Noble D, Viceconti M. 11.  2008. Meeting report on the 2nd MEI International Symposium—the worldwide challenge to physiome and systems biology and Osaka Accord. J. Physiol. Sci. 58:425–31 [Google Scholar]
  12. 12. Interag. Model. Anal. Group (IMAG) 2009. The impact of modeling on biomedical research Final rep., IMAG, Bethesda, MD. http://www.vph-institute.org/upload/imag-futures-report_519244d3a0aa0.pdf
  13. 13. PricewaterhouseCoopers 2008. Pharma 2020: virtual R&D. Which path will you take? Report, London/New York. http://www.vph-institute.org/upload/pwc-pharma2020-virtualrd-final_519245021a53a.pdf
  14. Shah I, Viceconti M. 14.  2009. Virtual tissues: a quantum leap in biomedical research Position pap., v-Tissues 2009 Experts Panel, Research Triangle Park, NC. http://www.vph-institute.org/upload/v-tissue-position-paper-2009_555460b051aaa.pdf
  15. Hunter P, Coveney PV, de Bono B, Diaz V, Fenner J. 15.  et al. 2010. A vision and strategy for the virtual physiological human in 2010 and beyond. Philos. Trans. A 368:2595–614 [Google Scholar]
  16. 16. VPH Inst 2011. From challenges to opportunities: towards a common strategic framework for EU research and innovation funding Position pap., VPH Inst., Brussels. http://www.vph-institute.org/upload/vphinst-position-on-fp8-greenpaper-v3_5192443874603.pdf
  17. 17. Discipulus Proj 2013. Roadmap for the digital patient Report, Eur. Comm., Brussels. http://www.vph-institute.org/upload/discipulus-digital-patient-research-roadmap_5270f44c03856.pdf
  18. 18. Avicenna Proj 2015. In silico clinical trials: how computer simulation will transform the biomedical induistry Report, Univ. Sheffield, UK. http://avicenna-isct.org/roadmap/ [Google Scholar]
  19. Viceconti M, Hunter P, Hose D. 19.  2015. Big data, big knowledge: big data for personalised healthcare. IEEE Biomed. Health Inform. 19:1209–15 [Google Scholar]
  20. Lloyd CM, Lawson JR, Hunter PJ, Nielsen PF. 20.  2008. The CellML Model Repository. Bioinformatics 24:2122–23 [Google Scholar]
  21. Cooling MT, Hunter P, Crampin EJ. 21.  2008. Modelling biological modularity with CellML. IET Syst. Biol. 2:73–79 [Google Scholar]
  22. Noble D.22.  1962. A modification of the Hodgkin–Huxley equations applicable to Purkinje fibre action and pace-maker potentials. J. Physiol. 160:317–52 [Google Scholar]
  23. Garny A, Hunter PJ. 23.  2015. OpenCOR: a modular and interoperable approach to computational biology. Front. Physiol. 6:26 [Google Scholar]
  24. Christie GR, Nielsen PM, Blackett SA, Bradley CP, Hunter PJ. 24.  2009. FieldML: concepts and implementation. Philos. Trans. A 367:1869–84 [Google Scholar]
  25. Britten RD, Christie GR, Little C, Miller AK, Bradley C. 25.  et al. 2013. FieldML, a proposed open standard for the Physiome project for mathematical model representation. Med. Biol. Eng. Comput. 51:1191–207 [Google Scholar]
  26. Brooks DJ, Hunter PJ, Smaill BH, Titchener MR. 26.  2011. BioSignalML—a meta-model for biosignals. IEEE Eng. Med. Biol. Soc. 2011:5670–73 [Google Scholar]
  27. Cooper J, Cervenansky F, De Fabritiis G, Fenner J, Friboulet D. 27.  et al. 2010. The Virtual Physiological Human ToolKit. Philos. Trans. A 368:3925–36 [Google Scholar]
  28. Kaniovskyi Y, Benkner S, Borckholder C, Wood S, Nowakowski P. 28.  et al. 2015. A semantic cloud infrastructure for data-intensive medical research. Int. J. Big Data Intell. 2:91–105 [Google Scholar]
  29. Nickerson DP, Atalag K, de Bono B, Hunter PJ. 29.  2016. The Physiome Project, openEHR archetypes and the digital patient. The Digital Patient: Advancing Healthcare, Research and Education Editors CD Combs, JA Sokolowski, CM Banks 101–26 New York: Wiley [Google Scholar]
  30. Hunter PJ, Smaill BH. 30.  1988. The analysis of cardiac function: a continuum approach. Prog. Biophys. Mol. Biol. 52:101–64 [Google Scholar]
  31. Bassingthwaighte JB.31.  1997. Design and strategy for the Cardiome Project. Adv. Exp. Med. Biol. 430:325–39 [Google Scholar]
  32. Bassingthwaighte JB, Qian H, Li Z. 32.  1999. The Cardiome Project. An integrated view of cardiac metabolism and regional mechanical function. Adv. Exp. Med. Biol. 471:541–53 [Google Scholar]
  33. Smith N, de Vecchi A, McCormick M, Nordsletten D, Camara O. 33.  et al. 2011. euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interface Focus 1:349–64 [Google Scholar]
  34. Krueger MW, Seemann G, Rhode K, Keller DU, Schilling C. 34.  et al. 2013. Personalization of atrial anatomy and electrophysiology as a basis for clinical modeling of radio-frequency ablation of atrial fibrillation. IEEE Trans. Med. Imaging 32:73–84 [Google Scholar]
  35. Zarins CK, Taylor CA, Min JK. 35.  2013. Computed fractional flow reserve (FFTCT) derived from coronary CT angiography. J. Cardiovasc. Transl. Res. 6:708–14 [Google Scholar]
  36. Morris PD, Ryan D, Morton AC, Lycett R, Lawford PV. 36.  et al. 2013. Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study. J. Am. Coll. Cardiol. Cardiovasc. Interv. 6:149–57 [Google Scholar]
  37. Swee JKY, Grbić S. 37.  2014. Advanced transcatheter aortic valve implantation (TAVI) planning from CT with ShapeForest. Med. Image Comput. Comput. Assist. Interv. 17:17–24 [Google Scholar]
  38. Bosi GM, Capelli C, Khambadkone S, Taylor AM, Schievano S. 38.  2015. Patient-specific finite element models to support clinical decisions: a lesson learnt from a case study of percutaneous pulmonary valve implantation. Catheter. Cardiovasc. Interv. 86:1120–30 [Google Scholar]
  39. Pijls NH, De Bruyne B, Peels K, Van Der Voort PH, Bonnier HJ. 39.  et al. 1996. Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N. Engl. J. Med. 334:1703–8 [Google Scholar]
  40. Nørgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS. 40.  et al. 2014. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J. Am. Coll. Cardiol. 63:1145–55 [Google Scholar]
  41. Lungu A, Wild JM, Capener D, Kiely DG, Swift AJ, Hose DR. 41.  2014. MRI model-based non-invasive differential diagnosis in pulmonary hypertension. J. Biomech. 47:2941–47 [Google Scholar]
  42. Gasser TC, Nchimi A, Swedenborg J, Roy J, Sakalihasan N. 42.  et al. 2014. A novel strategy to translate the biomechanical rupture risk of abdominal aortic aneurysms to their equivalent diameter risk: method and retrospective validation. Eur. J. Vasc. Endovasc. Surg. 47:288–95 [Google Scholar]
  43. Sforza DM, Putman CM, Cebral JR. 43.  2012. Computational fluid dynamics in brain aneurysms. Int. J. Numer. Methods Biomed. Eng. 28:801–8 [Google Scholar]
  44. Cristofolini L, Taddei F, Baleani M, Baruffaldi F, Stea S, Viceconti M. 44.  2008. Multiscale investigation of the functional properties of the human femur. Philos. Trans. A 366:3319–41 [Google Scholar]
  45. Viceconti M, Taddei F, van Sint JS, Leardini A, Cristofolini L. 45.  et al. 2008. Multiscale modelling of the skeleton for the prediction of the risk of fracture. Clin. Biomech. 23:845–52 [Google Scholar]
  46. Viceconti M, Taddei F, Cristofolini L, Martelli S, Falcinelli C, Schileo E. 46.  2012. Are spontaneous fractures possible? An example of clinical application for personalised, multiscale neuro-musculo-skeletal modelling. J. Biomech. 45:421–26 [Google Scholar]
  47. Falcinelli C, Schileo E, Balistreri L, Baruffaldi F, Bordini B. 47.  et al. 2014. Multiple loading conditions analysis can improve the association between finite element bone strength estimates and proximal femur fractures: a preliminary study in elderly women. Bone 67:71–80 [Google Scholar]
  48. Martelli S, Taddei F, Cappello A, van Sint JS, Leardini A, Viceconti M. 48.  2011. Effect of sub-optimal neuromotor control on the hip joint load during level walking. J. Biomech. 44:1716–21 [Google Scholar]
  49. Martelli S, Calvetti D, Somersalo E, Viceconti M, Taddei F. 49.  2013. Computational tools for calculating alternative muscle force patterns during motion: a comparison of possible solutions. J. Biomech. 46:2097–100 [Google Scholar]
  50. Martelli S, Calvetti D, Somersalo E, Viceconti M. 50.  2015. Stochastic modelling of muscle recruitment during activity. Interface Focus 5:20140094 [Google Scholar]
  51. Fernandez JW, Ho A, Walt S, Anderson IA, Hunter PJ. 51.  2005. A cerebral palsy assessment tool using anatomically based geometries and free-form deformation. Biomech. Model. Mechanobiol. 4:39–56 [Google Scholar]
  52. Fritsch A, Hellmich C. 52.  2007. ‘Universal’ microstructural patterns in cortical and trabecular, extracellular and extravascular bone materials: micromechanics-based prediction of anisotropic elasticity. J. Theor. Biol. 244:597–620 [Google Scholar]
  53. Predoi-Racila M, Crolet JM. 53.  2008. Human cortical bone: the SiNuPrOs model. Comput. Methods Biomech. Biomed. Eng. 11:169–87 [Google Scholar]
  54. Ghanbari J, Naghdabadi R. 54.  2009. Nonlinear hierarchical multiscale modeling of cortical bone considering its nanoscale microstructure. J. Biomech. 42:1560–65 [Google Scholar]
  55. Hamed E, Novitskaya E, Li J, Jasiuk I, McKittrick J. 55.  2015. Experimentally-based multiscale model of the elastic moduli of bovine trabecular bone and its constituents. Mater. Sci. Eng. C 54:207–16 [Google Scholar]
  56. Lavagnino M, Arnoczky SP, Kepich E, Caballero O, Haut RC. 56.  2008. A finite element model predicts the mechanotransduction response of tendon cells to cyclic tensile loading. Biomech. Model. Mechanobiol. 7:405–16 [Google Scholar]
  57. Maceri F, Marino M, Vairo G. 57.  2012. An insight on multiscale tendon modeling in muscle–tendon integrated behavior. Biomech. Model. Mechanobiol. 11:505–17 [Google Scholar]
  58. Shim VB, Fernandez JW, Gamage PB, Regnery C, Smith DW. 58.  et al. 2014. Subject-specific finite element analysis to characterize the influence of geometry and material properties in Achilles tendon rupture. J. Biomech. 47:3598–604 [Google Scholar]
  59. Smith DW, Rubenson J, Lloyd D, Zheng M, Fernandez J. 59.  et al. 2013. A conceptual framework for computational models of Achilles tendon homeostasis. Wiley Interdiscip. Rev. Syst. Biol. Med. 5:523–38 [Google Scholar]
  60. Szczesny SE, Elliott DM. 60.  2014. Incorporating plasticity of the interfibrillar matrix in shear lag models is necessary to replicate the multiscale mechanics of tendon fascicles. J. Mech. Behav. Biomed. Mater. 40:325–38 [Google Scholar]
  61. Fernandez JW, Buist ML, Nickerson DP, Hunter PJ. 61.  2005. Modelling the passive and nerve activated response of the rectus femoris muscle to a flexion loading: a finite element framework. Med. Eng. Phys. 27:862–70 [Google Scholar]
  62. El Makssoud H, Guiraud D, Poignet P, Hayashibe M, Wieber PB. 62.  et al. 2011. Multiscale modeling of skeletal muscle properties and experimental validations in isometric conditions. Biol. Cybern. 105:121–38 [Google Scholar]
  63. Kohout J, Clapworthy GJ, Zhao Y, Tao Y, Gonzalez-Garcia G. 63.  et al. 2013. Patient-specific fibre-based models of muscle wrapping. Interface Focus 3:20120062 [Google Scholar]
  64. Heidlauf T, Rohrle O. 64.  2014. A multiscale chemo-electro-mechanical skeletal muscle model to analyze muscle contraction and force generation for different muscle fiber arrangements. Front. Physiol. 5:498 [Google Scholar]
  65. Shim VB, Hunter PJ, Pivonka P, Fernandez JW. 65.  2011. A multiscale framework based on the physiome markup languages for exploring the initiation of osteoarthritis at the bone–cartilage interface. IEEE Trans. Biomed. Eng. 58:3532–36 [Google Scholar]
  66. Fernandez JW, Shim VB, Hunter PJ. 66.  2012. Integrating degenerative mechanisms in bone and cartilage: a multiscale approach. IEEE Eng. Med. Biol. Soc. 2012:6616–19 [Google Scholar]
  67. Halloran JP, Sibole S, van Donkelaar CC, van Turnhout MC, Oomens CW. 67.  et al. 2012. Multiscale mechanics of articular cartilage: potentials and challenges of coupling musculoskeletal, joint, and microscale computational models. Ann. Biomed. Eng. 40:2456–74 [Google Scholar]
  68. Sánchez Egea AJ, Valera M, Parraga Quiroga JM, Proubasta I, Noailly J, Lacroix D. 68.  2014. Impact of hip anatomical variations on the cartilage stress: a finite element analysis towards the biomechanical exploration of the factors that may explain primary hip arthritis in morphologically normal subjects. Clin. Biomech. 29:444–50 [Google Scholar]
  69. Lemaire T, Capiez-Lernout E, Kaiser J, Naili S, Sansalone V. 69.  2011. What is the importance of multiphysical phenomena in bone remodelling signals expression? A multiscale perspective. J. Mech. Behav. Biomed. Mater. 4:909–20 [Google Scholar]
  70. Colloca M, Blanchard R, Hellmich C, Ito K, van Rietbergen B. 70.  2014. A multiscale analytical approach for bone remodeling simulations: linking scales from collagen to trabeculae. Bone 64:303–13 [Google Scholar]
  71. Vahdati A, Walscharts S, Jonkers I, Garcia-Aznar JM, Vander Sloten J, van Lenthe GH. 71.  2014. Role of subject-specific musculoskeletal loading on the prediction of bone density distribution in the proximal femur. J. Mech. Behav. Biomed. Mater. 30:244–52 [Google Scholar]
  72. Lerebours C, Buenzli PR, Scheiner S, Pivonka P. 72.  2015. A multiscale mechanobiological model of bone remodelling predicts site-specific bone loss in the femur during osteoporosis and mechanical disuse. Biomech. Model. Mechanobiol. doi: 10.1007/s10237-015-0705-x
  73. Webster D, Schulte FA, Lambers FM, Kuhn G, Müller R. 73.  2015. Strain energy density gradients in bone marrow predict osteoblast and osteoclast activity: a finite element study. J. Biomech. 48:866–74 [Google Scholar]
  74. Carlier A, Geris L, Gastel N, Carmeliet G, Van Oosterwyck H. 74.  2015. Oxygen as a critical determinant of bone fracture healing—a multiscale model. J. Theor. Biol. 365:247–64 [Google Scholar]
  75. Buganza Tepole A, Kuhl E. 75.  2016. Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing. Comput. Methods Biomech. Biomed. Eng. 19:13–30 [Google Scholar]
  76. Lemaire T, Naili S, Remond A. 76.  2006. Multiscale analysis of the coupled effects governing the movement of interstitial fluid in cortical bone. Biomech. Model. Mechanobiol. 5:39–52 [Google Scholar]
  77. Valente G, Taddei F, Jonkers I. 77.  2013. Influence of weak hip abductor muscles on joint contact forces during normal walking: probabilistic modeling analysis. J. Biomech. 46:2186–93 [Google Scholar]
  78. Carriero A, Zimmermann EA, Paluszny A, Tang SY, Bale H. 78.  et al. 2014. How tough is brittle bone? Investigating osteogenesis imperfecta in mouse bone. J. Bone Miner. Res. 29:1392–401 [Google Scholar]
  79. Miura J, Maeda Y, Nakai H, Zako M. 79.  2009. Multiscale analysis of stress distribution in teeth under applied forces. Dent. Mater. 25:67–73 [Google Scholar]
  80. Haiat G, Wang HL, Brunski J. 80.  2014. Effects of biomechanical properties of the bone–implant interface on dental implant stability: from in silico approaches to the patient's mouth. Annu. Rev. Biomed. Eng. 16:187–213 [Google Scholar]
  81. Li X, Kruger JA, Nash MP, Nielsen PM. 81.  2010. Effects of nonlinear muscle elasticity on pelvic floor mechanics during vaginal childbirth. J. Biomech. Eng. 132:111010 [Google Scholar]
  82. Li X, Kruger JA, Chung JH, Nash MP, Nielsen PM. 82.  2008. Modelling childbirth: comparing athlete and non-athlete pelvic floor mechanics. Med. Image Comput. Comput. Assist. Interv. 11:750–57 [Google Scholar]
  83. Weis JA, Miga MI, Arlinghaus LR, Li X, Abramson V. 83.  et al. 2015. Predicting the response of breast cancer to neoadjuvant therapy using a mechanically coupled reaction–diffusion model. Cancer Res. 75:4697–707 [Google Scholar]
  84. Hodgkin AL, Huxley AF. 84.  1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:500–44 [Google Scholar]
  85. Nielsen PM, Le Grice IJ, Smaill BH, Hunter PJ. 85.  1991. Mathematical model of geometry and fibrous structure of the heart. Am. J. Physiol. Heart Circ. Physiol. 260:H1365–78 [Google Scholar]
  86. LeGrice IJ, Smaill BH, Chai LZ, Edgar SG, Gavin JB, Hunter PJ. 86.  1995. Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. Am. J. Physiol. Heart Circ. Physiol. 269:H571–82 [Google Scholar]
  87. Hunter PJ, Pullan AJ, Smaill BH. 87.  2003. Modeling total heart function. Annu. Rev. Biomed. Eng. 5:147–77 [Google Scholar]
  88. Nordsletten D, McCormick M, Kilner PJ, Hunter P, Kay D, Smith NP. 88.  2011. Fluid–solid coupling for the investigation of diastolic and systolic human left ventricular function. Int. J. Numer. Methods Biomed. Eng. 27:1017–39 [Google Scholar]
  89. Sadrieh A, Domanski L, Pitt-Francis J, Mann SA, Hodkinson EC. 89.  et al. 2014. Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2. Nat. Commun. 5:5069 [Google Scholar]
  90. Sinclair MD, Lee J, Cookson AN, Rivolo S, Hyde ER, Smith NP. 90.  2015. Measurement and modeling of coronary blood flow. Wiley Interdiscip. Rev. Syst. Biol. Med. 7:335–56 [Google Scholar]
  91. Lee CH, Rabbah JP, Yoganathan AP, Gorman RC, Gorman JH 3rd, Sacks MS. 91.  2015. On the effects of leaflet microstructure and constitutive model on the closing behavior of the mitral valve. Biomech. Model. Mechanobiol. 14:1281–302 [Google Scholar]
  92. Wang VY, Wilson AJ, Sands GB, Young AA, Le Grice IJ, Nash MP. 92.  Microstructural remodelling and mechanics of hypertensive heart disease. Proceedings of Functional Imaging and Modeling of the Heart: 8th International Conference (FIMH 2015) H van Assen, P Bovendeerd, T Delhaas 382–89 Berlin: Springer [Google Scholar]
  93. Tawhai MH, Pullan AJ, Hunter PJ. 93.  2000. Generation of an anatomically based three-dimensional model of the conducting airways. Ann. Biomed. Eng. 28:793–802 [Google Scholar]
  94. Tawhai MH, Clark A, Donovan G, Burrowes K. 94.  2011. Computational modeling of airway and pulmonary vascular structure and function: development of a “lung physiome”. Crit. Rev. Biomed. Eng. 39:319–36 [Google Scholar]
  95. Kim M, Bordas R, Vos W, Hartley RA, Brightling CE. 95.  et al. 2015. Dynamic flow characteristics in normal and asthmatic lungs. Int. J. Numer. Methods Biomed. Eng. 31. doi: 10.1002/cnm.2730
  96. Burrowes KS, De Backer J, Smallwood R, Sterk PJ, Gut I. 96.  et al. 2013. Multi-scale computational models of the airways to unravel the pathophysiological mechanisms in asthma and chronic obstructive pulmonary disease (AirPROM). Interface Focus 3:20120057 [Google Scholar]
  97. Politi AZ, Donovan GM, Tawhai MH, Sanderson MJ, Lauzon AM. 97.  et al. 2010. A multiscale, spatially distributed model of asthmatic airway hyper-responsiveness. J. Theor. Biol. 266:614–24 [Google Scholar]
  98. Yin Y, Choi J, Hoffman EA, Tawhai MH, Lin CL. 98.  2010. Simulation of pulmonary air flow with a subject-specific boundary condition. J. Biomech. 43:2159–63 [Google Scholar]
  99. Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M. 99.  et al. 2014. Systems medicine: from molecular features and models to the clinic in COPD. J. Transl. Med. 12:Suppl. 2S4 [Google Scholar]
  100. Burrowes KS, Doel T, Brightling C. 100.  2014. Computational modeling of the obstructive lung diseases asthma and COPD. J. Transl. Med. 12:Suppl. 2S5 [Google Scholar]
  101. Migliavacca F, Rochette M, Petiot F, Boichon C, Dordoni E. 101.  et al. Real time prediction of the fatigue behavior of peripheral stents. Proceedings of the 13th International IEEE Conference on Bioinformatics and Bioengineering (BIBE)1–4 Piscataway, NJ: IEEE [Google Scholar]
  102. Taylor CA, Draney MT, Ku JP, Parker D, Steele BN. 102.  et al. 1999. Predictive medicine: computational techniques in therapeutic decision-making. Comput. Aided Surg. 4:231–47 [Google Scholar]
  103. Kovatchev BP, Breton M, Man CD, Cobelli C. 103.  2009. In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J. Diabetes Sci. Technol. 3:44–55 [Google Scholar]
  104. Bergman RN, Ider YZ, Bowden CR, Cobelli C. 104.  1979. Quantitative estimation of insulin sensitivity. Am J. Physiol. Endocrinol. Metab. 236:E667–77 [Google Scholar]
/content/journals/10.1146/annurev-bioeng-110915-114742
Loading
The Virtual Physiological Human: Ten Years After
Annual Review of Biomedical Engineering 18, 103 (2016); https://doi.org/10.1146/annurev-bioeng-110915-114742
/content/journals/10.1146/annurev-bioeng-110915-114742
/content/journals/10.1146/annurev-bioeng-110915-114742
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error