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Computational simulation methodologies for mechanobiological modelling: a cell-centred approach to neointima development in stents.

Boyle CJ, Lennon AB, Early M, Kelly DJ, Lally C, Prendergast PJ - Philos Trans A Math Phys Eng Sci (2010)

Bottom Line: Tissue growth and differentiation requires simulating many of these cells together.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.

View Article: PubMed Central - PubMed

Affiliation: Trinity Centre for Bioengineering, School of Engineering, Trinity College Dublin, Dublin, Republic of Ireland.

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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Related in: MedlinePlus

As endothelial proliferation rate decreases, maximum neointima at the stent ends at 320 days increases (pEC = 0 (filled circle), pEC = 1 (open circle), pEC = 1.5 (filled triangle), pEC = 2 (open triangle), pEC = 2.7 (filled square), pEC = 4 (open square)).
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RSTA20100071F6: As endothelial proliferation rate decreases, maximum neointima at the stent ends at 320 days increases (pEC = 0 (filled circle), pEC = 1 (open circle), pEC = 1.5 (filled triangle), pEC = 2 (open triangle), pEC = 2.7 (filled square), pEC = 4 (open square)).

Mentions: Injury was predicted under stent struts, with the highest injured volumes at the stent ends and at stent cell junctions. ECM degradation in these regions induced phenotype modulation to sSMCs in surrounding cSMCs. These sSMCs migrated into the injured regions and proliferated to fill up the injured space (figure 4, day 7) eventually leading to lesion growth (figure 4, days 90 and 160). Lumen narrowing was arrested in two ways. In regions where the injured volume was small, such as in the centre of the stent, sSMCs modulated back to the contractile phenotype. In regions with a large injured volume, such as at the ends of the stent, the amount of sSMCs was greater, and lumen narrowing did not stop until the healing endothelium covered it (figure 4, day 320). In this region, SMCs did not return to their contractile phenotype within the duration of the simulation. The lesion grew rapidly in the first 90 days for all simulations (figure 5). For high pEC rates, the lesion growth then slowed to a stop when the endothelium healed (approx. 180 days), whereas for low pEC rates the growth rate simply slowed (figure 5). The final distribution of the lesion was also highly dependent on endothelial proliferation rate, with low healing rates associated with focal thickening at the ends of the stent and high healing rates producing maximal lesion size at the centre of the stent (figure 6). The proliferation rate over time of the artery was largely unaffected by changes in pEC; all simulations produced a peak in proliferation early on, followed by a gradual reduction in proliferative activity (figure 7).


Computational simulation methodologies for mechanobiological modelling: a cell-centred approach to neointima development in stents.

Boyle CJ, Lennon AB, Early M, Kelly DJ, Lally C, Prendergast PJ - Philos Trans A Math Phys Eng Sci (2010)

As endothelial proliferation rate decreases, maximum neointima at the stent ends at 320 days increases (pEC = 0 (filled circle), pEC = 1 (open circle), pEC = 1.5 (filled triangle), pEC = 2 (open triangle), pEC = 2.7 (filled square), pEC = 4 (open square)).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2944394&req=5

RSTA20100071F6: As endothelial proliferation rate decreases, maximum neointima at the stent ends at 320 days increases (pEC = 0 (filled circle), pEC = 1 (open circle), pEC = 1.5 (filled triangle), pEC = 2 (open triangle), pEC = 2.7 (filled square), pEC = 4 (open square)).
Mentions: Injury was predicted under stent struts, with the highest injured volumes at the stent ends and at stent cell junctions. ECM degradation in these regions induced phenotype modulation to sSMCs in surrounding cSMCs. These sSMCs migrated into the injured regions and proliferated to fill up the injured space (figure 4, day 7) eventually leading to lesion growth (figure 4, days 90 and 160). Lumen narrowing was arrested in two ways. In regions where the injured volume was small, such as in the centre of the stent, sSMCs modulated back to the contractile phenotype. In regions with a large injured volume, such as at the ends of the stent, the amount of sSMCs was greater, and lumen narrowing did not stop until the healing endothelium covered it (figure 4, day 320). In this region, SMCs did not return to their contractile phenotype within the duration of the simulation. The lesion grew rapidly in the first 90 days for all simulations (figure 5). For high pEC rates, the lesion growth then slowed to a stop when the endothelium healed (approx. 180 days), whereas for low pEC rates the growth rate simply slowed (figure 5). The final distribution of the lesion was also highly dependent on endothelial proliferation rate, with low healing rates associated with focal thickening at the ends of the stent and high healing rates producing maximal lesion size at the centre of the stent (figure 6). The proliferation rate over time of the artery was largely unaffected by changes in pEC; all simulations produced a peak in proliferation early on, followed by a gradual reduction in proliferative activity (figure 7).

Bottom Line: Tissue growth and differentiation requires simulating many of these cells together.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.

View Article: PubMed Central - PubMed

Affiliation: Trinity Centre for Bioengineering, School of Engineering, Trinity College Dublin, Dublin, Republic of Ireland.

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.

Show MeSH
Related in: MedlinePlus