Mechanobiological model of arterial growth and remodeling

Keshavarzian, Maziyar; Meyer, Clark A.; Hayenga, Heather N.

doi:10.1007/s10237-017-0946-y

Cited by 31 publications

(43 citation statements)

References 81 publications

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“…9, and also by using computational models. 3,5,13,18,34 The computational models of ISR usually represent cells by agents, either freely moving 5,18,34 or placed on a lattice. 3,13 These agents take cues from the blood flow, concentration of drugs eluted from the stent, and from the vessel damage and mechanics, which affect the growth and proliferation of the cells.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Nikishova

Veen

Zun

et al. 2018

Cardiovasc Eng Tech

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Purpose-Coronary artery stenosis, or abnormal narrowing, is a widespread and potentially fatal cardiac disease. After treatment by balloon angioplasty and stenting, restenosis may occur inside the stent due to excessive neointima formation. Simulations of in-stent restenosis can provide new insight into this process. However, uncertainties due to variability in patient-specific parameters must be taken into account. Methods-We performed an uncertainty quantification (UQ) study on a complex two-dimensional in-stent restenosis model. We used a quasi-Monte Carlo method for UQ of the neointimal area, and the Sobol sensitivity analysis (SA) to estimate the proportions of aleatory and epistemic uncertainties and to determine the most important input parameters. Results-We observe approximately 30% uncertainty in the mean neointimal area as simulated by the model. Depending on whether a fast initial endothelium recovery occurs, the proportion of the model variance due to natural variability ranges from 15 to 35%. The endothelium regeneration time is identified as the most influential model parameter. Conclusion-The model output contains a moderate quantity of uncertainty, and the model precision can be increased by obtaining a more certain value on the endothelium regeneration time. We conclude that the quasi-Monte Carlo UQ and the Sobol SA are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.

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Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Nikishova

Veen

Zun

et al. 2018

Cardiovasc Eng Tech

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“…We used a modified version of a coupled AB-FEA computational framework, which was previously established by our group, 25 to study the growth and remodeling of TEVGs under various loading scenarios. The coupled AB-FEA framework utilizes REPAST Simphony libraries 24 for the ABM simulations and the commercial FEA package ANSYS (ANSYS, Inc., Canonsburg, PA).…”

Section: Methodsmentioning

confidence: 99%

“…Relative to our prior work, 25 we updated the rule that described collagen production by SMCs to account for the nonlinear nature of its dependence on TGF-b. The TGF-b production rule was adapted to a strain-based version.…”

Section: Production Of Growth Factors Cytokines and Mmpsmentioning

confidence: 99%

“…We defined the baseline proliferation rate as the rate in which the SMCs proliferate in the absence of any external stimuli (growth factor, cytokine, mechanical loads, or serum) and calculated this value based on the experiments performed by Chapman et al 33 Compared to prior models, 25,34 we have updated the SMC proliferation rules to account for differences between various PDGF isoforms (PDGF-AA, PDGF-AB, and PDGF-BB). See Table 1, rules 2-6.…”

Section: Proliferation Of Smcsmentioning

confidence: 99%

“…In this study, we extend a coupled AB-FEA computational modeling framework, previously developed by our group, 25 to investigate the effect of various loading scenarios (uniaxial, biaxial, and equibiaxial), boundary conditions, stretch magnitudes, and growth factor concentrations on growth and remodeling of TEVGs. We set up the model based on the work of Huang et al 16 The AB-FEA model reported the production and alignment of collagen fibers, proliferation of the smooth muscle cells (SMCs), and concentrations of platelet-derived growth factor (PDGF), transforming growth factor-b (TGF-b), and vascular endothelial growth factor (VEGF) over time.…”

Section: Introductionmentioning

confidence: 99%

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In SilicoTissue Engineering: A Coupled Agent-Based Finite Element Approach

Keshavarzian

Meyer

Hayenga

2019

Tissue Engineering Part C: Methods

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Over the past two decades, the increase in prevalence of cardiovascular diseases and the limited availability of autologous blood vessels and saphenous vein grafts have motivated the development of tissue-engineered vascular grafts (TEVGs). However, compliance mismatch and poor mechanical properties of the TEVGs remain as two major issues that need to be addressed. Researchers have investigated the role of various culture conditions and mechanical conditioning in deposition and orientation of collagen fibers, which are the key structural components in the vascular wall; however, the intrinsic complexity of mechanobiological interactions demands implementing new engineering approaches that allow researchers to investigate various scenarios more efficiently. In this study, we utilized a coupled agent-based finite element analysis (AB-FEA) modeling approach to study the effect of various loading modes (uniaxial, biaxial, and equibiaxial), boundary conditions, stretch magnitudes, and growth factor concentrations on growth and remodeling of smooth muscle cellpopulated TEVGs, with specific focus on collagen deposition and orientation. Our simulations (12 weeks of culture) showed that biaxial cyclic loading (and not uniaxial or equibiaxial) leads to alignment of collagen fibers in the physiological directions. Moreover, axial boundary conditions of the TEVG act as determinants of fiber orientations. Decreasing the serum concentration, from 10% to 5% or 1%, significantly decreased the growth and remodeling speed, but only affected the fiber orientation in the 1% serum case. In conclusion, in silico tissue engineering has the potential to evolve the future of tissue engineering, as it will allow researchers to conceptualize various interactions and investigate numerous scenarios with great speed. In this study, we were able to predict the orientation of collagen fibers in TEVGs using a coupled AB-FEA model in less than 8 h.Tissue-engineered vascular grafts (TEVGs) hold potential to replace the current gold standard of vascular grafting, saphenous vein grafts. However, developing TEVGs that mimic the mechanical performance of the native tissue remains a challenging task. We developed a computational model of the grafts' remodeling processes and studied the effects of various loading mechanisms and culture conditions on collagen fiber orientation, which is a key factor in mechanical performance of the grafts. We were able to predict the fiber orientations accurately and show that biaxial loading and axial boundary conditions are important factors in collagen fiber organization.

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