Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention

Zhang, Yanhang; Barocas, Victor H.; Berceli, Scott A.; Clancy, Colleen E.; Eckmann, David M.; Garbey, Marc; Kassab, Ghassan S.; Lochner, Donna R.; McCulloch, Andrew D.; Tran‐Son‐Tay, Roger; Trayanova, Natalia A.

doi:10.1007/s10439-016-1628-0

Cited by 54 publications

(33 citation statements)

References 180 publications

(187 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal of this third point is to lay the foundations for the development of a predictive model that can reproduce animal experimental evidences that were retrieved by our group in a recent publication (Zhang Y et al 2016). The innovative predictive model will be characterized by a level of complexity between multiple scales and adaptation mechanisms that will go beyond our present controlled and simplified “theoretical biology” experiments.…”

Section: Methodsmentioning

confidence: 99%

Vascular Adaptation: Pattern Formation and Cross Validation between an Agent Based Model and a Dynamical System

Garbey

Casarin

Berceli

2017

Journal of Theoretical Biology

Self Cite

View full text Add to dashboard Cite

Myocardial infarction is the global leading cause of mortality (Go AS et al. 2014). Coronary artery occlusion is its main etiology and it is commonly treated by Coronary Artery Bypass Graft (CABG) surgery (Wilson CT 2007). The long-term outcome remains unsatisfactory (Benedetto U 2016) as the graft faces the phenomenon of restenosis during the post-surgery, which consists of re-occlusion of the lumen and usually requires secondary intervention even within one year after the initial surgery (Harskamp RE 2013). In this work, we propose an extensive study of the restenosis phenomenon by implementing two mathematical models previously developed by our group: a heuristic Dynamical System (DS) (Garbey M et al. 2013), and a stochastic Agent Based Model (ABM) (Garbey M et al. 2015). With an extensive use of the ABM, we retrieved the pattern formations of the cellular events that mainly lead the restenosis, especially focusing on mitosis in intima, caused by alteration in shear stress, and mitosis in media, fostered by alteration in wall tension. A deep understanding of the elements at the base of the restenosis is indeed crucial in order to improve the final outcome of vein graft bypass. We also turned the ABM closer to the physiological reality by abating its original assumption of circumferential symmetry. This allowed us to finely replicate the trigger event of the restenosis, i.e. the loss of the endothelium in the early stage of the post-surgical follow up (Roubos N et al. 1995) and to simulate the encroachment of the lumen in a fashion aligned with histological evidences (Owens CD et al. 2015). Finally, we cross-validated the two models by creating an accurate matching procedure. In this way we added the degree of accuracy given by the ABM to a simplified model (DS) that can serve as powerful predictive tool for the clinic.

show abstract

Section: Methodsmentioning

confidence: 99%

Vascular Adaptation: Pattern Formation and Cross Validation between an Agent Based Model and a Dynamical System

Garbey

Casarin

Berceli

2017

Journal of Theoretical Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Despite this, partial reviews can be found in other works. 10,66,67 These fluid-electro-mechanical models solve the three set of equations in a coupled manner. Now, we present a brief insight on each one of the three-physics models in existence.…”

Section: Coupled Multi-physics Cardiac Computational Modeling State Omentioning

confidence: 99%

“…The reason for this is, probably, the scarcity of such models. Despite this, partial reviews can be found in other works . These fluid‐electro‐mechanical models solve the three set of equations in a coupled manner.…”

Section: Introductionmentioning

confidence: 99%

Fully coupled fluid‐electro‐mechanical model of the human heart for supercomputers

Santiago

Aguado-Sierra

Zavala-Aké

et al. 2018

Numer Methods Biomed Eng

View full text Add to dashboard Cite

In this work, we present a fully coupled fluid-electro-mechanical model of a 50th percentile human heart. The model is implemented on Alya, the BSC multi-physics parallel code, capable of running efficiently in supercomputers. Blood in the cardiac cavities is modeled by the incompressible Navier-Stokes equations and an arbitrary Lagrangian-Eulerian (ALE) scheme. Electrophysiology is modeled with a monodomain scheme and the O'Hara-Rudy cell model. Solid mechanics is modeled with a total Lagrangian formulation for discrete strains using the Holzapfel-Ogden cardiac tissue material model. The three problems are simultaneously and bidirectionally coupled through an electromechanical feedback and a fluid-structure interaction scheme. In this paper, we present the scheme in detail and propose it as a computational cardiac workbench.

show abstract

“…Biomarkers can identify individuals with an increased cardiovascular risk, and biomarker-guided therapy represents an attractive option, with troponin-guided therapy of acute coronary syndromes as a successful example [55] . Multiscale modeling of cardiac electrophysiology and mechanics for diagnosis as well as clinical decision support will facilitate the development of personalized cardiology, e.g., in arrhythmia and HF [56] . The introduction of new technologies such as high-resolution CT coronary imaging with fractional flow reserve, virtual reality, vascular robotic systems, and 3-D printing can be incorporated into personalized care to improve the efficacy as well as the safety of the treatment of patients with CVD [57] .…”

Section: Concluding Remarks and The Future Of Personalized Cardiologymentioning

confidence: 99%

Personalized Management of Cardiovascular Disorders

Jain¹

2017

Med Princ Pract

View full text Add to dashboard Cite

Personalized management of cardiovascular disorders (CVD), also referred to as personalized or precision cardiology in accordance with general principles of personalized medicine, is selection of the best treatment for an individual patient. It involves the integration of various “omics” technologies such as genomics and proteomics as well as other new technologies such as nanobiotechnology. Molecular diagnostics and biomarkers are important for linking diagnosis with therapy and monitoring therapy. Because CVD involve perturbations of large complex biological networks, a systems biology approach to CVD risk stratification may be used for improving risk-estimating algorithms, and modeling of personalized benefit of treatment may be helpful for guiding the choice of intervention. Bioinformatics tools are helpful in analyzing and integrating large amounts of data from various sources. Personalized therapy is considered during drug development, including methods of targeted drug delivery and clinical trials. Individualized recommendations consider multiple factors - genetic as well as epigenetic - for patients' risk of heart disease. Examples of personalized treatment are those of chronic myocardial ischemia, heart failure, and hypertension. Similar approaches can be used for the management of atrial fibrillation and hypercholesterolemia, as well as the use of anticoagulants. Personalized management includes pharmacotherapy, surgery, lifestyle modifications, and combinations thereof. Further progress in understanding the pathomechanism of complex cardiovascular diseases and identification of causative factors at the individual patient level will provide opportunities for the development of personalized cardiology. Application of principles of personalized medicine will improve the care of the patients with CVD.

show abstract

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention

Cited by 54 publications

References 180 publications

Vascular Adaptation: Pattern Formation and Cross Validation between an Agent Based Model and a Dynamical System

Vascular Adaptation: Pattern Formation and Cross Validation between an Agent Based Model and a Dynamical System

Fully coupled fluid‐electro‐mechanical model of the human heart for supercomputers

Personalized Management of Cardiovascular Disorders

Contact Info

Product

Resources

About