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Azimi-Boulali J, Mahler GJ, Murray BT, Huang P. Multiscale computational modeling of aortic valve calcification. Biomech Model Mechanobiol 2024; 23:581-599. [PMID: 38093148 DOI: 10.1007/s10237-023-01793-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/13/2023] [Indexed: 03/26/2024]
Abstract
Calcific aortic valve disease (CAVD) is a common cardiovascular disease that affects millions of people worldwide. The disease is characterized by the formation of calcium nodules on the aortic valve leaflets, which can lead to stenosis and heart failure if left untreated. The pathogenesis of CAVD is still not well understood, but involves several signaling pathways, including the transforming growth factor beta (TGF β ) pathway. In this study, we developed a multiscale computational model for TGF β -stimulated CAVD. The model framework comprises cellular behavior dynamics, subcellular signaling pathways, and tissue-level diffusion fields of pertinent chemical species, where information is shared among different scales. Processes such as endothelial to mesenchymal transition (EndMT), fibrosis, and calcification are incorporated. The results indicate that the majority of myofibroblasts and osteoblast-like cells ultimately die due to lack of nutrients as they become trapped in areas with higher levels of fibrosis or calcification, and they subsequently act as sources for calcium nodules, which contribute to a polydispersed nodule size distribution. Additionally, fibrosis and calcification processes occur more frequently in regions closer to the endothelial layer where the cell activity is higher. Our results provide insights into the mechanisms of CAVD and TGF β signaling and could aid in the development of novel therapeutic approaches for CAVD and other related diseases such as cancer. More broadly, this type of modeling framework can pave the way for unraveling the complexity of biological systems by incorporating several signaling pathways in subcellular models to simulate tissue remodeling in diseases involving cellular mechanobiology.
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Affiliation(s)
- Javid Azimi-Boulali
- Department of Mechanical Engineering, Binghamton University, Binghamton, NY, 13902, USA
| | - Gretchen J Mahler
- Department of Biomedical Engineering, Binghamton University, Binghamton, NY, 13902, USA
| | - Bruce T Murray
- Department of Mechanical Engineering, Binghamton University, Binghamton, NY, 13902, USA
| | - Peter Huang
- Department of Mechanical Engineering, Binghamton University, Binghamton, NY, 13902, USA.
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Reyes-Aldasoro CC. Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"? Cancers (Basel) 2023; 15:3796. [PMID: 37568612 PMCID: PMC10416922 DOI: 10.3390/cancers15153796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.
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Peng Q, Shan D, Cui K, Li K, Zhu B, Wu H, Wang B, Wong S, Norton V, Dong Y, Lu YW, Zhou C, Chen H. The Role of Endothelial-to-Mesenchymal Transition in Cardiovascular Disease. Cells 2022; 11:1834. [PMID: 35681530 PMCID: PMC9180466 DOI: 10.3390/cells11111834] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 02/07/2023] Open
Abstract
Endothelial-to-mesenchymal transition (EndoMT) is the process of endothelial cells progressively losing endothelial-specific markers and gaining mesenchymal phenotypes. In the normal physiological condition, EndoMT plays a fundamental role in forming the cardiac valves of the developing heart. However, EndoMT contributes to the development of various cardiovascular diseases (CVD), such as atherosclerosis, valve diseases, fibrosis, and pulmonary arterial hypertension (PAH). Therefore, a deeper understanding of the cellular and molecular mechanisms underlying EndoMT in CVD should provide urgently needed insights into reversing this condition. This review summarizes a 30-year span of relevant literature, delineating the EndoMT process in particular, key signaling pathways, and the underlying regulatory networks involved in CVD.
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Affiliation(s)
- Qianman Peng
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Dan Shan
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Kui Cui
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Kathryn Li
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Bo Zhu
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Hao Wu
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Beibei Wang
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Scott Wong
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Vikram Norton
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Yunzhou Dong
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Yao Wei Lu
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
| | - Changcheng Zhou
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, CA 92521, USA;
| | - Hong Chen
- Vascular Biology Program, Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (Q.P.); (D.S.); (K.C.); (K.L.); (B.Z.); (H.W.); (B.W.); (S.W.); (V.N.); (Y.D.); (Y.W.L.)
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Abstract
Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.
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