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Computational Simulation Methodologies for Mechanobiological Modelling: a Cell-centred Approach to Neointima Development in Stents

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Abstract

The design of medical devices could be very much improved if robust tools were available for computational simulation of tissue response to the presence of the implant. Such tools require algorithms to simulate the response of tissues to mechanical and chemical stimuli. Available methodologies include those based on the principle of mechanical homeostasis, those which use continuum models to simulate biological constituents, and the cell-centred approach, which models cells as autonomous agents. In the latter approach, cell behaviour is governed by rules based on the state of the local environment around the cell; and informed by experiment. Tissue growth and differentiation requires simulating many of these cells together. In this paper, the methodology and applications of cell-centred techniques--with particular application to mechanobiology--are reviewed, and a cell-centred model of tissue formation in the lumen of an artery in response to the deployment of a stent is presented. The method is capable of capturing some of the most important aspects of restenosis, including nonlinear lesion growth with time. The approach taken in this paper provides a framework for simulating restenosis; the next step will be to couple it with more patient-specific geometries and quantitative parameter data.

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References
1.
Bennett M, OSullivan M . Mechanisms of angioplasty and stent restenosis: implications for design of rational therapy. Pharmacol Ther. 2001; 91(2):149-66. DOI: 10.1016/s0163-7258(01)00153-x. View

2.
Checa S, Prendergast P . A mechanobiological model for tissue differentiation that includes angiogenesis: a lattice-based modeling approach. Ann Biomed Eng. 2008; 37(1):129-45. DOI: 10.1007/s10439-008-9594-9. View

3.
Gijsen F, Migliavacca F, Schievano S, Socci L, Petrini L, Thury A . Simulation of stent deployment in a realistic human coronary artery. Biomed Eng Online. 2008; 7:23. PMC: 2525649. DOI: 10.1186/1475-925X-7-23. View

4.
Orford J, Selwyn A, Ganz P, Popma J, Rogers C . The comparative pathobiology of atherosclerosis and restenosis. Am J Cardiol. 2000; 86(4B):6H-11H. DOI: 10.1016/s0002-9149(00)01094-8. View

5.
Harnek J, Zoucas E, Carlemalm E, Cwikiel W . Differences in endothelial injury after balloon angioplasty, insertion of balloon-expanded stents or release of self-expanding stents: An electron microscopic experimental study. Cardiovasc Intervent Radiol. 1999; 22(1):56-61. DOI: 10.1007/s002709900329. View