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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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2
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Crossley RM, Johnson S, Tsingos E, Bell Z, Berardi M, Botticelli M, Braat QJS, Metzcar J, Ruscone M, Yin Y, Shuttleworth R. Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist. Front Cell Dev Biol 2024; 12:1354132. [PMID: 38495620 PMCID: PMC10940354 DOI: 10.3389/fcell.2024.1354132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/12/2024] [Indexed: 03/19/2024] Open
Abstract
The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.
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Affiliation(s)
- Rebecca M. Crossley
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Samuel Johnson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Erika Tsingos
- Computational Developmental Biology Group, Institute of Biodynamics and Biocomplexity, Utrecht University, Utrecht, Netherlands
| | - Zoe Bell
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Massimiliano Berardi
- LaserLab, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Optics11 life, Amsterdam, Netherlands
| | | | - Quirine J. S. Braat
- Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, Netherlands
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
- Department of Informatics, Indiana University, Bloomington, IN, United States
| | | | - Yuan Yin
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
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Comlekoglu T, Dzamba BJ, Pacheco GG, Shook DR, Sego TJ, Glazier JA, Peirce SM, DeSimone DW. Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration of Xenopus mesendoderm. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.16.562601. [PMID: 37904937 PMCID: PMC10614848 DOI: 10.1101/2023.10.16.562601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Collectively migrating Xenopus mesendoderm cells are arranged into leader and follower rows with distinct adhesive properties and protrusive behaviors. In vivo, leading row mesendoderm cells extend polarized protrusions and migrate along a fibronectin matrix assembled by blastocoel roof cells. Traction stresses generated at the leading row result in the pulling forward of attached follower row cells. Mesendoderm explants removed from embryos provide an experimentally tractable system for characterizing collective cell movements and behaviors, yet the cellular mechanisms responsible for this mode of migration remain elusive. We introduce an agent-based computational model of migrating mesendoderm in the Cellular-Potts computational framework to investigate the relative contributions of multiple parameters specific to the behaviors of leader and follower row cells. Sensitivity analyses identify cohesotaxis, tissue geometry, and cell intercalation as key parameters affecting the migration velocity of collectively migrating cells. The model predicts that cohesotaxis and tissue geometry in combination promote cooperative migration of leader cells resulting in increased migration velocity of the collective. Radial intercalation of cells towards the substrate is an additional mechanism to increase migratory speed of the tissue. Summary Statement We present a novel Cellular-Potts model of collective cell migration to investigate the relative roles of cohesotaxis, tissue geometry, and cell intercalation on migration velocity of Xenopus mesendoderm.
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Borzouei M, Mardaani M, Emadi-Baygi M, Rabani H. Development of a coupled modeling for tumor growth, angiogenesis, oxygen delivery, and phenotypic heterogeneity. Biomech Model Mechanobiol 2023; 22:1067-1081. [PMID: 36869277 DOI: 10.1007/s10237-023-01701-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 02/05/2023] [Indexed: 03/05/2023]
Abstract
Analysis of the evolution and growth dynamics of tumors is crucial for understanding cancer and the development of individually optimized therapies. During tumor growth, a hypoxic microenvironment around cancer cells caused by excessive non-vascular tumor growth induces tumor angiogenesis that plays a key role in the ensuing tumor growth and its progression into higher stages. Various mathematical simulation models have been introduced to simulate these biologically and physically complex hallmarks of cancer. Here, we developed a hybrid two-dimensional computational model that integrates spatiotemporally different components of the tumor system to investigate both angiogenesis and tumor growth/proliferation. This spatiotemporal evolution is based on partial diffusion equations, the cellular automation method, transition and probabilistic rules, and biological assumptions. The new vascular network provided by angiogenesis affects tumor microenvironmental conditions and drives individual cells to adapt themselves to spatiotemporal conditions. Furthermore, some stochastic rules are involved besides microenvironmental conditions. Overall, the conditions promote some commonly observed cellular states, i.e., proliferative, migrative, quiescent, and cell death, depending on the condition of each cell. Altogether, our results offer a theoretical basis for the biological evidence that regions of the tumor tissue near blood vessels are densely populated by proliferative phenotypic variants, while poorly oxygenated regions are sparsely populated by hypoxic phenotypic variants.
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Affiliation(s)
- Mahmood Borzouei
- Department of Physics, Faculty of Sciences, Shahrekord University, P.O. Box 115, Shahrekord, Iran
| | - Mohammad Mardaani
- Department of Physics, Faculty of Sciences, Shahrekord University, P.O. Box 115, Shahrekord, Iran
- Nanotechnology Research Center, Shahrekord University, Shahrekord, 8818634141, Iran
| | - Modjtaba Emadi-Baygi
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord, Iran.
| | - Hassan Rabani
- Department of Physics, Faculty of Sciences, Shahrekord University, P.O. Box 115, Shahrekord, Iran
- Nanotechnology Research Center, Shahrekord University, Shahrekord, 8818634141, Iran
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Heidary Z, Haghjooy Javanmard S, Izadi I, Zare N, Ghaisari J. Multiscale modeling of collective cell migration elucidates the mechanism underlying tumor-stromal interactions in different spatiotemporal scales. Sci Rep 2022; 12:16242. [PMID: 36171274 PMCID: PMC9519582 DOI: 10.1038/s41598-022-20634-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/15/2022] [Indexed: 11/09/2022] Open
Abstract
Metastasis is the pathogenic spread of cancer cells from a primary tumor to a secondary site which happens at the late stages of cancer. It is caused by a variety of biological, chemical, and physical processes, such as molecular interactions, intercellular communications, and tissue-level activities. Complex interactions of cancer cells with their microenvironment components such as cancer associated fibroblasts (CAFs) and extracellular matrix (ECM) cause them to adopt an invasive phenotype that promotes tumor growth and migration. This paper presents a multiscale model for integrating a wide range of time and space interactions at the molecular, cellular, and tissue levels in a three-dimensional domain. The modeling procedure starts with presenting nonlinear dynamics of cancer cells and CAFs using ordinary differential equations based on TGFβ, CXCL12, and LIF signaling pathways. Unknown kinetic parameters in these models are estimated using hybrid unscented Kalman filter and the models are validated using experimental data. Then, the principal role of CAFs on metastasis is revealed by spatial-temporal modeling of circulating signals throughout the TME. At this stage, the model has evolved into a coupled ODE-PDE system that is capable of determining cancer cells' status in one of the quiescent, proliferating or migratory conditions due to certain metastasis factors and ECM characteristics. At the tissue level, we consider a force-based framework to model the cancer cell proliferation and migration as the final step towards cancer cell metastasis. The ability of the multiscale model to depict cancer cells' behavior in different levels of modeling is confirmed by comparing its outputs with the results of RT PCR and wound scratch assay techniques. Performance evaluation of the model indicates that the proposed multiscale model can pave the way for improving the efficiency of therapeutic methods in metastasis prevention.
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Affiliation(s)
- Zarifeh Heidary
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - Shaghayegh Haghjooy Javanmard
- Department of Physiology, Applied Physiology Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | - Iman Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - Nasrin Zare
- School of Medicine, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Jafar Ghaisari
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
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An Agent-Based Interpretation of Leukocyte Chemotaxis in Cancer-on-Chip Experiments. MATHEMATICS 2022. [DOI: 10.3390/math10081338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The present paper was inspired by recent developments in laboratory experiments within the framework of cancer-on-chip technology, an immune-oncology microfluidic chip aiming at studying the fundamental mechanisms of immunocompetent behavior. We focus on the laboratory setting where cancer is treated with chemotherapy drugs, and in this case, the effects of the treatment administration hypothesized by biologists are: the absence of migration and proliferation of tumor cells, which are dying; the stimulation of the production of chemical substances (annexin); the migration of leukocytes in the direction of higher concentrations of chemicals. Here, following the physiological hypotheses made by biologists on the phenomena occurring in these experiments, we introduce an agent-based model reproducing the dynamics of two cell populations (agents), i.e., tumor cells and leukocytes living in the microfluidic chip environment. Our model aims at proof of concept, demonstrating that the observations of the biological phenomena can be obtained by the model on the basis of the explicit assumptions made. In this framework, close adherence of the computational model to the biological results, as shown in the section devoted to the first calibration of the model with respect to available observations, is successfully accomplished.
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Ferraro R, Ascione F, Dogra P, Cristini V, Guido S, Caserta S. Diffusion‐induced anisotropic cancer invasion: a novel experimental method based on tumour spheroids. AIChE J 2022. [DOI: 10.1002/aic.17678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Rosalia Ferraro
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
- CEINGE Advanced Biotechnologies Naples Italy
| | - Flora Ascione
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
| | - Prashant Dogra
- Mathematics in Medicine Program Houston Methodist Research Institute Houston Texas USA
- Department of Physiology and Biophysics Weill Cornell Medical College New York New York USA
| | - Vittorio Cristini
- Mathematics in Medicine Program Houston Methodist Research Institute Houston Texas USA
- Department of Imaging Physics University of Texas MD Anderson Cancer Center Houston Texas USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences Weill Cornell Medicine New York New York USA
| | - Stefano Guido
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
- CEINGE Advanced Biotechnologies Naples Italy
| | - Sergio Caserta
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
- CEINGE Advanced Biotechnologies Naples Italy
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Estimation Algorithm for a Hybrid PDE–ODE Model Inspired by Immunocompetent Cancer-on-Chip Experiment. AXIOMS 2021. [DOI: 10.3390/axioms10040243] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The present work is motivated by the development of a mathematical model mimicking the mechanisms observed in lab-on-chip experiments, made to reproduce on microfluidic chips the in vivo reality. Here we consider the Cancer-on-Chip experiment where tumor cells are treated with chemotherapy drug and secrete chemical signals in the environment attracting multiple immune cell species. The in silico model here proposed goes towards the construction of a “digital twin” of the experimental immune cells in the chip environment to better understand the complex mechanisms of immunosurveillance. To this aim, we develop a tumor-immune microfluidic hybrid PDE–ODE model to describe the concentration of chemicals in the Cancer-on-Chip environment and immune cells migration. The development of a trustable simulation algorithm, able to reproduce the immunocompetent dynamics observed in the chip, requires an efficient tool for the calibration of the model parameters. In this respect, the present paper represents a first methodological work to test the feasibility and the soundness of the calibration technique here proposed, based on a multidimensional spline interpolation technique for the time-varying velocity field surfaces obtained from cell trajectories.
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Nazari M, Babaei N, Nazari M. Nonlinear SDRE based adaptive fuzzy control approach for age-specific drug delivery in mixed chemotherapy and immunotherapy. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102687] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Liu R, Higley KA, Swat MH, Chaplain MAJ, Powathil GG, Glazier JA. Development of a coupled simulation toolkit for computational radiation biology based on Geant4 and CompuCell3D. Phys Med Biol 2021; 66:045026. [PMID: 33339019 DOI: 10.1088/1361-6560/abd4f9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Understanding and designing clinical radiation therapy is one of the most important areas of state-of-the-art oncological treatment regimens. Decades of research have gone into developing sophisticated treatment devices and optimization protocols for schedules and dosages. In this paper, we presented a comprehensive computational platform that facilitates building of the sophisticated multi-cell-based model of how radiation affects the biology of living tissue. We designed and implemented a coupled simulation method, including a radiation transport model, and a cell biology model, to simulate the tumor response after irradiation. The radiation transport simulation was implemented through Geant4 which is an open-source Monte Carlo simulation platform that provides many flexibilities for users, as well as low energy DNA damage simulation physics, Geant4-DNA. The cell biology simulation was implemented using CompuCell3D (CC3D) which is a cell biology simulation platform. In order to couple Geant4 solver with CC3D, we developed a 'bridging' module, RADCELL, that extracts tumor cellular geometry of the CC3D simulation (including specification of the individual cells) and ported it to the Geant4 for radiation transport simulation. The cell dose and cell DNA damage distribution in multicellular system were obtained using Geant4. The tumor response was simulated using cell-based tissue models based on CC3D, and the cell dose and cell DNA damage information were fed back through RADCELL to CC3D for updating the cell properties. By merging two powerful and widely used modeling platforms, CC3D and Geant4, we delivered a novel tool that can give us the ability to simulate the dynamics of biological tissue in the presence of ionizing radiation, which provides a framework for quantifying the biological consequences of radiation therapy. In this introductory methods paper, we described our modeling platform in detail and showed how it can be applied to study the application of radiotherapy to a vascularized tumor.
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Affiliation(s)
- Ruirui Liu
- School of Nuclear Science and Engineering, Oregon State University, 100 Radiation Center, Corvallis, OR 97331, United States of America.,Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, 63110, United States of America
| | - Kathryn A Higley
- School of Nuclear Science and Engineering, Oregon State University, 100 Radiation Center, Corvallis, OR 97331, United States of America
| | - Maciej H Swat
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Mark A J Chaplain
- School of Mathematics and Statistics, Mathematical Institute, University of St Andrews, St Andrews KY16 9SS, Fife, United Kingdom
| | - Gibin G Powathil
- Department of Mathematics, College of Science, Swansea University, Swansea, SA2 8PP, United Kingdom
| | - James A Glazier
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
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