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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

A system diagram of the components (in boxes) and processes (arrows) involved in the model of smooth muscle cell behaviour. (i) The uninjured artery wall is quiescent and contains contractile smooth muscle cells (cSMCs) and extracellular matrix (ECM). Injury induces cell death and tissue damage/rupture. (ii) In response, inflammatory cells infiltrate the injured region and produce matrix degrading factors and growth factors. (iii) Matrix degradation of ECM induces (iv) SMCs to modulate their phenotype from contractile to synthetic (sSMC). These sSMCs proliferate in response to growth factors (G) (v) and express ECM (vi). SMCs can resort back to the contractile phenotype if the ECM is fully restored.
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RSTA20100071F1: A system diagram of the components (in boxes) and processes (arrows) involved in the model of smooth muscle cell behaviour. (i) The uninjured artery wall is quiescent and contains contractile smooth muscle cells (cSMCs) and extracellular matrix (ECM). Injury induces cell death and tissue damage/rupture. (ii) In response, inflammatory cells infiltrate the injured region and produce matrix degrading factors and growth factors. (iii) Matrix degradation of ECM induces (iv) SMCs to modulate their phenotype from contractile to synthetic (sSMC). These sSMCs proliferate in response to growth factors (G) (v) and express ECM (vi). SMCs can resort back to the contractile phenotype if the ECM is fully restored.

Mentions: A cell-centred model of an idealized human coronary artery was implemented using a lattice-based approach. Three cell types (cSMCs, sSMCs and ECs), and three extracellular components (ECM, matrix degrading factors (MDF), and growth stimuli (G)) were modelled. The SMC phenotype was governed by the local ECM concentration: it was contractile if the ECM was present and not degraded (ECM = 1) and the local concentration of cells was below a critical value cSMC,crit, and synthetic otherwise. This ensured that a region would return to quiescence when the ratio between cells and ECM was appropriate. Differentiation was assumed to be reversible. sSMCs proliferated if the local growth stimulus was above a critical level (Gcrit), and occurred at a constant rate (pSMC). A successful proliferation reduced the local growth stimulus by Gcrit. Migration of sSMCs occurred in random directions at a constant rate (vSMC) through lattice points which either had or were adjacent to points with ECM present. sSMCs produced ECM at a constant rate (cECM) at their lattice position. In lattice points containing both ECM and MDF, both of these components were reduced at a constant rate of degradation, cdeg. ECs were only allowed to occupy lattice points which fulfilled the following conditions: they did not contain ECM, and they were adjacent to a lattice point with ECM, i.e. ECs could only proliferate along the lumen surface, and occurred at a constant proliferation rate, pEC. A system diagram of the simplified system implemented is given in figure 1. The lattice was updated according to these rules over time, as shown schematically in figure 2. At each time increment, all cells were operated upon based on their phenotype. Random walk migration and proliferation were performed on each active cell a number of times per increment based on the migration and proliferation rates, respectively. Differentiation of SMCs and ECM production were performed every increment, with the increment corresponding to one day. Depending on the particular initial conditions and parameters of the model, this simulation can progress until equilibrium is reached, i.e. when the endothelium is completely healed, or until the lumen is entirely occluded. SMCs cannot occupy a lattice point containing another cell, so a position occupied by an EC represents an immovable and impenetrable barrier to SMCs.


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)

A system diagram of the components (in boxes) and processes (arrows) involved in the model of smooth muscle cell behaviour. (i) The uninjured artery wall is quiescent and contains contractile smooth muscle cells (cSMCs) and extracellular matrix (ECM). Injury induces cell death and tissue damage/rupture. (ii) In response, inflammatory cells infiltrate the injured region and produce matrix degrading factors and growth factors. (iii) Matrix degradation of ECM induces (iv) SMCs to modulate their phenotype from contractile to synthetic (sSMC). These sSMCs proliferate in response to growth factors (G) (v) and express ECM (vi). SMCs can resort back to the contractile phenotype if the ECM is fully restored.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSTA20100071F1: A system diagram of the components (in boxes) and processes (arrows) involved in the model of smooth muscle cell behaviour. (i) The uninjured artery wall is quiescent and contains contractile smooth muscle cells (cSMCs) and extracellular matrix (ECM). Injury induces cell death and tissue damage/rupture. (ii) In response, inflammatory cells infiltrate the injured region and produce matrix degrading factors and growth factors. (iii) Matrix degradation of ECM induces (iv) SMCs to modulate their phenotype from contractile to synthetic (sSMC). These sSMCs proliferate in response to growth factors (G) (v) and express ECM (vi). SMCs can resort back to the contractile phenotype if the ECM is fully restored.
Mentions: A cell-centred model of an idealized human coronary artery was implemented using a lattice-based approach. Three cell types (cSMCs, sSMCs and ECs), and three extracellular components (ECM, matrix degrading factors (MDF), and growth stimuli (G)) were modelled. The SMC phenotype was governed by the local ECM concentration: it was contractile if the ECM was present and not degraded (ECM = 1) and the local concentration of cells was below a critical value cSMC,crit, and synthetic otherwise. This ensured that a region would return to quiescence when the ratio between cells and ECM was appropriate. Differentiation was assumed to be reversible. sSMCs proliferated if the local growth stimulus was above a critical level (Gcrit), and occurred at a constant rate (pSMC). A successful proliferation reduced the local growth stimulus by Gcrit. Migration of sSMCs occurred in random directions at a constant rate (vSMC) through lattice points which either had or were adjacent to points with ECM present. sSMCs produced ECM at a constant rate (cECM) at their lattice position. In lattice points containing both ECM and MDF, both of these components were reduced at a constant rate of degradation, cdeg. ECs were only allowed to occupy lattice points which fulfilled the following conditions: they did not contain ECM, and they were adjacent to a lattice point with ECM, i.e. ECs could only proliferate along the lumen surface, and occurred at a constant proliferation rate, pEC. A system diagram of the simplified system implemented is given in figure 1. The lattice was updated according to these rules over time, as shown schematically in figure 2. At each time increment, all cells were operated upon based on their phenotype. Random walk migration and proliferation were performed on each active cell a number of times per increment based on the migration and proliferation rates, respectively. Differentiation of SMCs and ECM production were performed every increment, with the increment corresponding to one day. Depending on the particular initial conditions and parameters of the model, this simulation can progress until equilibrium is reached, i.e. when the endothelium is completely healed, or until the lumen is entirely occluded. SMCs cannot occupy a lattice point containing another cell, so a position occupied by an EC represents an immovable and impenetrable barrier to SMCs.

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