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1
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Arthur L, Voulgaridou V, Papageorgiou G, Lu W, McDougall SR, Sboros V. Super-resolution ultrasound imaging of ischaemia flow: An in silico study. J Theor Biol 2025; 599:112018. [PMID: 39647660 DOI: 10.1016/j.jtbi.2024.112018] [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: 06/20/2024] [Revised: 10/29/2024] [Accepted: 12/01/2024] [Indexed: 12/10/2024]
Abstract
Super-resolution ultrasound (SRU) is a new ultrasound imaging mode that promises to facilitate the detection of microvascular disease by providing new vascular bio-markers that are directly linked to microvascular pathophysiology, thereby augmenting current knowledge and potentially enabling new treatment. Such a capability can be developed through thorough understanding as articulated by means of mathematical models. In this study, a 2D numerical flow model is adopted for generating flow adaptation in response to ischaemia, in order to determine the ability of SRU to register the resulting flow perturbations. The flow model results demonstrate that variations in flow behaviour in response to locally induced ischaemia can be significant throughout the entire vascular bed. Measured velocities have variations that are dependent on the location of ischaemia, with median values ranging between 2-7 mms-1. Moreover, the distinction between healthy and ischaemic networks are recorded accurately in the SRU results showing excellent agreement between SRU maps and the model. Up to 7-fold spatial resolution improvement to conventional contrast ultrasound was achieved in microbubble localisation while the detection precision and recall was consistently above 98%. The microbubble tracking precision was of a similar accuracy, whereas the recall was reduced (77%) under varying ischaemic impacted flow. Further, regions with velocities up to 30 mms-1 are in excellent agreement with SRU maps, while at regions that include a proportion of higher velocities, the median velocity values are within 1.28%-3.32% of the ground-truth. In conclusion, SRU is a highly promising methodology for the direct measurement of microvascular flow dynamics and may provide a valuable tool for the understanding and subsequent modelling of behaviour in the vascular bed.
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Affiliation(s)
- Lachlan Arthur
- School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, United Kingdom.
| | - Vasiliki Voulgaridou
- Translational Healthcare Technologies Team, Centre for Inflammation Research, University of Edinburgh, Edinburgh, EH16 4TJ, Scotland, United Kingdom.
| | - Georgios Papageorgiou
- School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, United Kingdom.
| | - Weiping Lu
- School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, United Kingdom.
| | - Steven R McDougall
- School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, United Kingdom.
| | - Vassilis Sboros
- School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, United Kingdom.
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2
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Gabriel EM, Bahr D, Rachamala HK, Madamsetty VS, Shreeder B, Bagaria S, Escobedo AL, Reid JM, Mukhopadhyay D. Liposomal Phenylephrine Nanoparticles Enhance the Antitumor Activity of Intratumoral Chemotherapy in a Preclinical Model of Melanoma. ACS Biomater Sci Eng 2024; 10:3412-3424. [PMID: 38613483 PMCID: PMC11301277 DOI: 10.1021/acsbiomaterials.4c00078] [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] [Indexed: 04/15/2024]
Abstract
Intratumoral injection of anticancer agents has limited efficacy and is not routinely used for most cancers. In this study, we aimed to improve the efficacy of intratumoral chemotherapy using a novel approach comprising peri-tumoral injection of sustained-release liposomal nanoparticles containing phenylephrine, which is a potent vasoconstrictor. Using a preclinical model of melanoma, we have previously shown that systemically administered (intravenous) phenylephrine could transiently shunt blood flow to the tumor at the time of drug delivery, which in turn improved antitumor responses. This approach was called dynamic control of tumor-associated vessels. Herein, we used liposomal phenylephrine nanoparticles as a "local" dynamic control strategy for the B16 melanoma. Local dynamic control was shown to increase the retention and exposure time of tumors to intratumorally injected chemotherapy (melphalan). C57BL/6 mice bearing B16 tumors were treated with intratumoral melphalan and peri-tumoral injection of sustained-release liposomal phenylephrine nanoparticles (i.e., the local dynamic control protocol). These mice had statistically significantly improved antitumor responses compared to melphalan alone (p = 0.0011), whereby 58.3% obtained long-term complete clinical response. Our novel approach of local dynamic control demonstrated significantly enhanced antitumor efficacy and is the subject of future clinical trials being designed by our group.
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Affiliation(s)
- Emmanuel M. Gabriel
- Department of Surgery, Division of Surgical Oncology, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Deborah Bahr
- Department of Molecular Biology, Mayo Clinic, Jacksonville, FL, 32224, USA
| | | | | | - Barath Shreeder
- Department of Immunology, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Sanjay Bagaria
- Department of Surgery, Division of Surgical Oncology, Mayo Clinic, Jacksonville, FL, 32224, USA
| | | | - Joel M. Reid
- Department of Pharmacology, Mayo Clinic, Rochester, MN, 55902, USA
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3
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Stepanova D, Byrne HM, Maini PK, Alarcón T. Computational modeling of angiogenesis: The importance of cell rearrangements during vascular growth. WIREs Mech Dis 2024; 16:e1634. [PMID: 38084799 DOI: 10.1002/wsbm.1634] [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: 07/04/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 03/16/2024]
Abstract
Angiogenesis is the process wherein endothelial cells (ECs) form sprouts that elongate from the pre-existing vasculature to create new vascular networks. In addition to its essential role in normal development, angiogenesis plays a vital role in pathologies such as cancer, diabetes and atherosclerosis. Mathematical and computational modeling has contributed to unraveling its complexity. Many existing theoretical models of angiogenic sprouting are based on the "snail-trail" hypothesis. This framework assumes that leading ECs positioned at sprout tips migrate toward low-oxygen regions while other ECs in the sprout passively follow the leaders' trails and proliferate to maintain sprout integrity. However, experimental results indicate that, contrary to the snail-trail assumption, ECs exchange positions within developing vessels, and the elongation of sprouts is primarily driven by directed migration of ECs. The functional role of cell rearrangements remains unclear. This review of the theoretical modeling of angiogenesis is the first to focus on the phenomenon of cell mixing during early sprouting. We start by describing the biological processes that occur during early angiogenesis, such as phenotype specification, cell rearrangements and cell interactions with the microenvironment. Next, we provide an overview of various theoretical approaches that have been employed to model angiogenesis, with particular emphasis on recent in silico models that account for the phenomenon of cell mixing. Finally, we discuss when cell mixing should be incorporated into theoretical models and what essential modeling components such models should include in order to investigate its functional role. This article is categorized under: Cardiovascular Diseases > Computational Models Cancer > Computational Models.
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Affiliation(s)
- Daria Stepanova
- Laboratorio Subterráneo de Canfranc, Canfranc-Estación, Huesca, Spain
| | - Helen M Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Tomás Alarcón
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Centre de Recerca Matemàtica, Bellaterra, Barcelona, Spain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, Bellaterra, Spain
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4
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Keshavanarayana P, Spill F. A mechanical modeling framework to study endothelial permeability. Biophys J 2024; 123:334-348. [PMID: 38169215 PMCID: PMC10870174 DOI: 10.1016/j.bpj.2023.12.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 12/06/2023] [Accepted: 12/28/2023] [Indexed: 01/05/2024] Open
Abstract
The inner lining of blood vessels, the endothelium, is made up of endothelial cells. Vascular endothelial (VE)-cadherin protein forms a bond with VE-cadherin from neighboring cells to determine the size of gaps between the cells and thereby regulate the size of particles that can cross the endothelium. Chemical cues such as thrombin, along with mechanical properties of the cell and extracellular matrix are known to affect the permeability of endothelial cells. Abnormal permeability is found in patients suffering from diseases including cardiovascular diseases, cancer, and COVID-19. Even though some of the regulatory mechanisms affecting endothelial permeability are well studied, details of how several mechanical and chemical stimuli acting simultaneously affect endothelial permeability are not yet understood. In this article, we present a continuum-level mechanical modeling framework to study the highly dynamic nature of the VE-cadherin bonds. Taking inspiration from the catch-slip behavior that VE-cadherin complexes are known to exhibit, we model the VE-cadherin homophilic bond as cohesive contact with damage following a traction-separation law. We explicitly model the actin cytoskeleton and substrate to study their role in permeability. Our studies show that mechanochemical coupling is necessary to simulate the influence of the mechanical properties of the substrate on permeability. Simulations show that shear between cells is responsible for the variation in permeability between bicellular and tricellular junctions, explaining the phenotypic differences observed in experiments. An increase in the magnitude of traction force due to disturbed flow that endothelial cells experience results in increased permeability, and it is found that the effect is higher on stiffer extracellular matrix. Finally, we show that the cylindrical monolayer exhibits higher permeability than the planar monolayer under unconstrained cases. Thus, we present a contact mechanics-based mechanochemical model to investigate the variation in the permeability of endothelial monolayer due to multiple loads acting simultaneously.
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Affiliation(s)
| | - Fabian Spill
- School of Mathematics, University of Birmingham, Birmingham, United Kingdom.
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5
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Flandoli F, Leocata M, Ricci C. The Mathematical modeling of Cancer growth and angiogenesis by an individual based interacting system. J Theor Biol 2023; 562:111432. [PMID: 36746298 DOI: 10.1016/j.jtbi.2023.111432] [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: 04/05/2022] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 02/07/2023]
Abstract
We present a mathematical model for the complex system for the growth of a solid tumor. The system embeds proliferation of cells depending on the surrounding oxygen field, hypoxia caused by insufficient oxygen when the tumor reaches a certain size, consequent VEGF release and angiogenic new vasculature growth, re-oxygenation of the tumor and subsequent tumor growth restart. Specifically cancerous cells are represented by individual units, interacting as proliferating particles of a solid body, oxygen, and VEGF are fields with a source and a sink, and new angiogenic vasculature is described by a network of growing curves. The model, as shown by numerical simulations, captures both the time-evolution of the tumor growth before and after angiogenesis and its spatial properties, with different distribution of proliferating and hypoxic cells in the external and deep layers of the tumor, and the spatial structure of the angiogenic network. The microscopic description of the growth opens the possibility of tuning the model to patient-specific scenarios.
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Affiliation(s)
- Franco Flandoli
- Scuola Normale Superiore, P.za dei Cavalieri, 7, Pisa, 56126, Italy.
| | - Marta Leocata
- Scuola Normale Superiore, P.za dei Cavalieri, 7, Pisa, 56126, Italy.
| | - Cristiano Ricci
- University of Pisa, Department of Economics and Management, Via Cosimo Ridolfi, 10, Pisa, 56124, Italy.
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6
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Mohammadi M, Soltani M, Aghanajafi C, Kohandel M. Investigation of the evolution of tumor-induced microvascular network under the inhibitory effect of anti-angiogenic factor, angiostatin: A mathematical study. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:5448-5480. [PMID: 36896553 DOI: 10.3934/mbe.2023252] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Anti-angiogenesis as a treatment strategy for normalizing the microvascular network of tumors is of great interest among researchers, especially in combination with chemotherapy or radiotherapy. According to the vital role that angiogenesis plays in tumor growth and in exposing the tumor to therapeutic agents, this work develops a mathematical framework to study the influence of angiostatin, a plasminogen fragment that shows the anti-angiogenic function, in the evolutionary behavior of tumor-induced angiogenesis. Angiostatin-induced microvascular network reformation is investigated in a two-dimensional space by considering two parent vessels around a circular tumor by a modified discrete angiogenesis model in different tumor sizes. The effects of imposing modifications on the existing model, i.e., the matrix-degrading enzyme effect, proliferation and death of endothelial cells, matrix density function, and a more realistic chemotactic function, are investigated in this study. Results show a decrease in microvascular density in response to the angiostatin. A functional relationship exists between angiostatin's ability to normalize the capillary network and tumor size or progression stage, such that capillary density decreases by 55%, 41%, 24%, and 13% in tumors with a non-dimensional radius of 0.4, 0.3, 0.2, and 0.1, respectively, after angiostatin administration.
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Affiliation(s)
- Mahya Mohammadi
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19919-43344, Iran
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19919-43344, Iran
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran 19697-64499, Iran
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Cyrus Aghanajafi
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19919-43344, Iran
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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7
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Abdalrahman T, Checa S. On the role of mechanical signals on sprouting angiogenesis through computer modeling approaches. Biomech Model Mechanobiol 2022; 21:1623-1640. [PMID: 36394779 PMCID: PMC9700567 DOI: 10.1007/s10237-022-01648-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/08/2022] [Indexed: 11/19/2022]
Abstract
Sprouting angiogenesis, the formation of new vessels from preexisting vasculature, is an essential process in the regeneration of new tissues as well as in the development of some diseases like cancer. Although early studies identified chemical signaling as the main driver of this process, many recent studies have shown a strong role of mechanical signals in the formation of new capillaries. Different types of mechanical signals (e.g., external forces, cell traction forces, and blood flow-induced shear forces) have been shown to play distinct roles in the process; however, their interplay remains still largely unknown. During the last decades, mathematical and computational modeling approaches have been developed to investigate and better understand the mechanisms behind mechanically driven angiogenesis. In this manuscript, we review computational models of angiogenesis with a focus on models investigating the role of mechanics on the process. Our aim is not to provide a detailed review on model methodology but to describe what we have learnt from these models. We classify models according to the mechanical signals being investigated and describe how models have looked into their role on the angiogenic process. We show that a better understanding of the mechanobiology of the angiogenic process will require the development of computer models that incorporate the interactions between the multiple mechanical signals and their effect on cellular responses, since they all seem to play a key in sprout patterning. In the end, we describe some of the remaining challenges of computational modeling of angiogenesis and discuss potential avenues for future research.
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8
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Koyuncu B, Melek A, Yilmaz D, Tuzer M, Unlu MB. Chemotherapy response prediction with diffuser elapser network. Sci Rep 2022; 12:1628. [PMID: 35102179 PMCID: PMC8803972 DOI: 10.1038/s41598-022-05460-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 11/10/2021] [Indexed: 12/31/2022] Open
Abstract
In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof of concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The model predicts tumor microenvironment maps seven days ahead with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of [Formula: see text].
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Affiliation(s)
- Batuhan Koyuncu
- Department of Computer Engineering, Bogazici University, Istanbul, 34342, Turkey
- Center for Life Sciences and Technologies, Bogazici University, Istanbul, 34342, Turkey
| | - Ahmet Melek
- Department of Management, Bogazici University, Istanbul, 34342, Turkey
- Center for Life Sciences and Technologies, Bogazici University, Istanbul, 34342, Turkey
| | - Defne Yilmaz
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
- Center for Life Sciences and Technologies, Bogazici University, Istanbul, 34342, Turkey
| | - Mert Tuzer
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey
- Center for Life Sciences and Technologies, Bogazici University, Istanbul, 34342, Turkey
| | - Mehmet Burcin Unlu
- Department of Physics, Bogazici University, Istanbul, 34342, Turkey.
- Center for Life Sciences and Technologies, Bogazici University, Istanbul, 34342, Turkey.
- Hokkaido University, Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Sapporo, 060-8648, Japan.
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9
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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10
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Goodin DA, Frieboes HB. Simulation of 3D centimeter-scale continuum tumor growth at sub-millimeter resolution via distributed computing. Comput Biol Med 2021; 134:104507. [PMID: 34157612 DOI: 10.1016/j.compbiomed.2021.104507] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/15/2021] [Accepted: 05/16/2021] [Indexed: 12/28/2022]
Abstract
Simulation of cm-scale tumor growth has generally been constrained by the computational cost to numerically solve the associated equations, with models limited to representing mm-scale or smaller tumors. While the work has proven useful to the study of small tumors and micro-metastases, a biologically-relevant simulation of cm-scale masses as would be typically detected and treated in patients has remained an elusive goal. This study presents a distributed computing (parallelized) implementation of a mixture model of tumor growth to simulate 3D cm-scale vascularized tissue at sub-mm resolution. The numerical solving scheme utilizes a two-stage parallelization framework. The solution is written for GPU computation using the CUDA framework, which handles all Multigrid-related computations. Message Passing Interface (MPI) handles distribution of information across multiple processes, freeing the program from RAM and the processing limitations found on single systems. On each system, Nvidia's CUDA library allows for fast processing of model data using GPU-bound computing on fewer systems. The results show that a combined MPI-CUDA implementation enables the continuum modeling of cm-scale tumors at reasonable computational cost. Further work to calibrate model parameters to particular tumor conditions could enable simulation of patient-specific tumors for clinical application.
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Affiliation(s)
- Dylan A Goodin
- Department of Bioengineering, University of Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, KY, USA; James Graham Brown Cancer Center, University of Louisville, KY, USA; Center for Predictive Medicine, University of Louisville, KY, USA.
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11
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Macnamara CK. Biomechanical modelling of cancer: Agent‐based force‐based models of solid tumours within the context of the tumour microenvironment. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021. [DOI: 10.1002/cso2.1018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Cicely K. Macnamara
- School of Mathematics and Statistics Mathematical Institute University of St Andrews St Andrews Fife UK
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12
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The biomedical significance of multifunctional nanobiomaterials: The key components for site-specific delivery of therapeutics. Life Sci 2021; 277:119400. [PMID: 33794255 DOI: 10.1016/j.lfs.2021.119400] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 03/08/2021] [Accepted: 03/13/2021] [Indexed: 01/07/2023]
Abstract
The emergence of nanotechnology has provided the possibilities to overcome the potential problems associated with the development of pharmaceuticals including the low solubility, non-specific cellular uptake or action, and rapid clearance. Regarding the biomaterials (BMs), huge efforts have been made for improving their multi-functionalities via incorporation of various nanomaterials (NMs). Nanocomposite hydrogels with suitable properties could exhibit a variety of beneficial effects in biomedicine particularly in the delivery of therapeutics or tissue engineering. NMs including the silica- or carbon-based ones are capable of integration into various BMs that might be due to their special compositions or properties such as the hydrophilicity, hydrophobicity, magnetic or electrical characteristics, and responsiveness to various stimuli. This might provide multi-functional nanobiomaterials against a wide variety of disorders. Meanwhile, inappropriate distribution or penetration into the cells or tissues, bio-nano interface complexity, targeting ability loss, or any other unpredicted phenomena are the serious challenging issues. Computational simulations and models enable development of NMs with optimal characteristics and provide a deeper knowledge of NM interaction with biosystems. This review highlights the biomedical significance of the multifunctional NMs particularly those applied for the development of 2-D or 3-D BMs for a variety of applications including the site-specific delivery of therapeutics. The powerful impacts of the computational techniques on the design process of NMs, quantitation and prediction of protein corona formation, risk assessment, and individualized therapy for improved therapeutic outcomes have also been discussed.
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13
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Taiji R, Nishiofuku H, Tanaka T, Minamiguchi K, Fukuoka Y, Saito N, Taguchi H, Matsumoto T, Marugami N, Hirai T, Kichikawa K. Useful Parameters in Dynamic Contrast-enhanced Ultrasonography for Identifying Early Response to Chemotherapy in a Rat Liver Tumor Model. J Clin Imaging Sci 2021; 11:15. [PMID: 33767907 PMCID: PMC7981939 DOI: 10.25259/jcis_6_2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/15/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives The objective of the study is to determine a parameter on the time-intensity curve (TIC) of dynamic contrast-enhanced ultrasonography (DCE-US) that best correlates with tumor growth and to evaluate whether the parameter could correlate with the early response to irinotecan in a rat liver tumor model. Material and Methods Twenty rats with tumors were evaluated (control: Saline, n = 6; treatment: Irinotecan, n = 14) regarding four parameters from TIC: Peak intensity (PI), k value, slope (PI × k), and time to peak (TTP). Relative changes in maximum tumor diameter between day 0 and 10, and parameters in the first 3 days were evaluated. The Mann-Whitney U-test was used to compare differences in tumor size and other parameters. Pearson's correlation coefficients (r) between tumor size and parameters in the control group were calculated. In the treatment group, relative changes of parameters in the first 3 days were compared between responder and non-responder (<20% and ≥20% increase in size on day 10, respectively). Results PI, k value, PI × k, and TTP significantly correlated with tumor growth (r = 0.513, 0.911, 0.665, and 0.741, respectively). The mean RC in k value among responders (n = 6) was significantly lower than non-responders (n = 8) (mean k value, 4.96 vs. 72.5; P = 0.003). Conclusion Parameters of DCE-US could be a useful parameter for identifying early response to irinotecan.
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Affiliation(s)
- Ryosuke Taiji
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | | | - Toshihiro Tanaka
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | | | - Yasushi Fukuoka
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | - Natsuhiko Saito
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | - Hidehiko Taguchi
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | - Takeshi Matsumoto
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | - Nagaaki Marugami
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | - Toshiko Hirai
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
| | - Kimihiko Kichikawa
- Department of Radiology, Nara Medical University, Kashihara, Nara, Japan
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14
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Kara E, Rahman A, Aulisa E, Ghosh S. Tumor ablation due to inhomogeneous anisotropic diffusion in generic three-dimensional topologies. Phys Rev E 2020; 102:062425. [PMID: 33466110 DOI: 10.1103/physreve.102.062425] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 11/23/2020] [Indexed: 11/07/2022]
Abstract
In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.
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Affiliation(s)
- Erdi Kara
- Department of Mathematics and Statistics, Texas Tech University, Lubbock TX
| | - Aminur Rahman
- Department of Applied Mathematics, University of Washington, Seattle WA
| | - Eugenio Aulisa
- Department of Mathematics and Statistics, Texas Tech University, Lubbock TX
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, Lincoln NB
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15
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Ali M, Sharifi N, McGrath N, Cree MJ, Chen Y. Self-Regulated and Co-ordinated Smart Tumor Homing for Complex Vascular Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:378-381. [PMID: 33018007 DOI: 10.1109/embc44109.2020.9176014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Inspired by the collaborative movements of the living cells in different natural biological processes, this paper highlights a self-regulated and co-ordinated targeting strategy to detect tumor in complex human vasculature. We show by computational experiments that simple and non-centralized nanoparticles with limited locomotive capability, when they collaborate and move in the form of groups, can be very robust in the tumor homing process. Once reaching tumor location, such nanoparticles (potential contrast agents) can deposit themselves on tumor through the enhanced permeability and retention (EPR) effect thereby making the malignant cancerous area more prominent. Hence, existing medical imaging techniques with limited resolution can be used to detect small malignant tumors. We believe that our work will help in early cancer detection through an independent, non-centralized and self-controlled system.Clinical Relevance:- With the help of target amplification through contrast agents such as self-regulated nanoparticles contrast enhanced medical imaging can detect the tumor at its initial stages when 90% of the cancers are curable.
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16
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Indirect Contributions to Tumor Dynamics in the First Stage of the Avascular Phase. Symmetry (Basel) 2020. [DOI: 10.3390/sym12091546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A continuum model for tumor invasion in a two-dimensional spatial domain based on the interaction of the urokinase plasminogen activation system with a model for cancer cell dynamics is proposed. The arising system of partial differential equations is numerically solved using the finite element method. We simulated a portion of biological tissue imposing no flux boundary conditions. We monitored the cancer cell dynamics, as well the degradation of an extra cellular matrix representative, vitronectin, and the evolution of a specific degrading enzyme, plasmin, inside the biological tissue. The computations were parameterized as a function of the indirect cell proliferation induced by a plasminogen activator inhibitor binding to vitronectin and of the indirect plasmin deactivation due to the plasminogen activator inhibitor binding to the urokinase plasminogen activator. Their role during the cancer dynamical evolution was identified, together with a possible marker helping the mapping of the cancer invasive front. Our results indicate that indirect cancer cell proliferation biases the speed of the tumor invasive front as well as the heterogeneity of the cancer cell clustering and networking, as it ultimately acts on the proteolytic activity supporting cancer formation. Because of the initial conditions imposed, the numerical solutions of the model show a symmetrical dynamical evolution of heterogeneities inside the simulated domain. Moreover, an increase of up to about 12% in the invasion speed was observed, increasing the rate of indirect cancer cell proliferation, while increasing the plasmin deactivation rate inhibits heterogeneities and networking. As cancer cell proliferation causes vitronectin consumption and plasmin formation, the intensities of the concentration maps of both vitronectin and plasmin are superimposable to the cancer cell concentration maps. The qualitative imprinting that cancer cells leave on the extra cellular matrix during the time evolution as well their activity area is identified, framing the numerical results in the context of a methodology aimed at diagnostic and therapeutic improvement.
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17
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Drug delivery systems based on nanoparticles and related nanostructures. Eur J Pharm Sci 2020; 151:105412. [DOI: 10.1016/j.ejps.2020.105412] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/31/2020] [Accepted: 06/02/2020] [Indexed: 12/11/2022]
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Sharma N, Sharma M, Sajid Jamal QM, Kamal MA, Akhtar S. Nanoinformatics and biomolecular nanomodeling: a novel move en route for effective cancer treatment. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:19127-19141. [PMID: 31025282 DOI: 10.1007/s11356-019-05152-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 04/10/2019] [Indexed: 06/09/2023]
Abstract
Empowering role of nanoinformatics in design and elucidation of nanoparticles for effective cancer treatment has made this field a fascinating area for researchers, inspiring them to enhance up the quality and efficacy of existing anticancer medicines. Theoretical and computational modeling is being seen as a forefront solution for problems related to surface chemistry, optimized geometry, or other properties in nanoparticle designing and drug delivery. The current review aims to acquaint with the insight story of the incubation of in silico tools and techniques in nanotechnology to develop better anticancer nanomedicines. The review also recapitulates the assets and liabilities of this field and present an outline of existing inventiveness and endeavors of nanoinformatics. We propose how nanoinformatics could hasten up the advancements in anticancer nanomedicines through use of computational tools, nanoparticles repositories & various modeling and simulation methods.
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Affiliation(s)
- Neha Sharma
- Department of Bioengineering, Faculty of Engineering, Integral University, Lucknow, UP, 226026, India
- Advanced Center of Bioengineering and Bioinformatics, Integral Information and Research Centre, Integral University, Lucknow, UP, 226026, India
| | - Mala Sharma
- Advanced Center of Bioengineering and Bioinformatics, Integral Information and Research Centre, Integral University, Lucknow, UP, 226026, India
- Department of Biosciences, Integral University, Lucknow, UP, 226026, India
| | - Qazi M Sajid Jamal
- Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, King Abdulaziz Rd, Al Bukayriyah, 52741, Al Qassim, Saudi Arabia
| | - Mohammad A Kamal
- King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Enzymoics, 7, Peterlee Place, Hebersham, NSW, 2770, Australia
- Novel Global Community Educational Foundation, Peterlee Place, Hebersham, NSW, 2770, Australia
| | - Salman Akhtar
- Department of Bioengineering, Faculty of Engineering, Integral University, Lucknow, UP, 226026, India.
- Advanced Center of Bioengineering and Bioinformatics, Integral Information and Research Centre, Integral University, Lucknow, UP, 226026, India.
- Novel Global Community Educational Foundation, Peterlee Place, Hebersham, NSW, 2770, Australia.
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19
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From tumour perfusion to drug delivery and clinical translation of in silico cancer models. Methods 2020; 185:82-93. [PMID: 32147442 DOI: 10.1016/j.ymeth.2020.02.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 02/13/2020] [Accepted: 02/24/2020] [Indexed: 12/14/2022] Open
Abstract
In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution: first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine.
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Hahn A, Bode J, Krüwel T, Kampf T, Buschle LR, Sturm VJF, Zhang K, Tews B, Schlemmer HP, Heiland S, Bendszus M, Ziener CH, Breckwoldt MO, Kurz FT. Gibbs point field model quantifies disorder in microvasculature of U87-glioblastoma. J Theor Biol 2020; 494:110230. [PMID: 32142806 DOI: 10.1016/j.jtbi.2020.110230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 10/28/2019] [Accepted: 03/02/2020] [Indexed: 10/24/2022]
Abstract
Microvascular proliferation in glioblastoma multiforme is a biological key mechanism to facilitate tumor growth and infiltration and a main target for treatment interventions. The vascular architecture can be obtained by Single Plane Illumination Microscopy (SPIM) to evaluate vascular heterogeneity in tumorous tissue. We make use of the Gibbs point field model to quantify the order of regularity in capillary distributions found in the U87 glioblastoma model in a murine model and to compare tumorous and healthy brain tissue. A single model parameter Γ was assigned that is linked to tissue-specific vascular topology through Monte-Carlo simulations. Distributions of the model parameter Γ differ significantly between glioblastoma tissue with mean 〈ΓG〉=2.1±0.4, as compared to healthy brain tissue with mean 〈ΓH〉=4.9±0.4, suggesting that the average Γ-value allows for tissue differentiation. These results may be used for diagnostic magnetic resonance imaging, where it has been shown recently that Γ is linked to tissue-inherent relaxation parameters.
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Affiliation(s)
- Artur Hahn
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany; Department of Physics and Astronomy, University of Heidelberg, Im Neuenheimer Feld 226, Heidelberg 69120, Germany
| | - Julia Bode
- Molecular Mechanisms of Tumor Invasion, Schaller Research Group, University of Heidelberg and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg 69120, Germany
| | - Thomas Krüwel
- Molecular Mechanisms of Tumor Invasion, Schaller Research Group, University of Heidelberg and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg 69120, Germany
| | - Thomas Kampf
- Department of Experimental Physics 5, University of Würzburg, Am Hubland, Würzburg 97074, Germany; Department of Neuroradiology, University Hospital Würzburg, Josef-Schneider-Straße 2, Würzburg 97080, Germany
| | - Lukas R Buschle
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany; Department of Radiology E010, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Volker J F Sturm
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany; Department of Radiology E010, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Ke Zhang
- Department of Radiology E010, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Björn Tews
- Molecular Mechanisms of Tumor Invasion, Schaller Research Group, University of Heidelberg and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg 69120, Germany
| | - Heinz-Peter Schlemmer
- Department of Radiology E010, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany
| | - Christian H Ziener
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany; Department of Radiology E010, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Michael O Breckwoldt
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany; Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Felix T Kurz
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg 69120, Germany; Department of Radiology E010, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
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21
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Boutchuen A, Zimmerman D, Arabshahi A, Melnyczuk J, Palchoudhury S. Understanding nanoparticle flow with a new in vitro experimental and computational approach using hydrogel channels. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2020; 11:296-309. [PMID: 32117668 PMCID: PMC7034222 DOI: 10.3762/bjnano.11.22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 01/27/2020] [Indexed: 06/10/2023]
Abstract
Nanoparticles (NPs) are considered as one of the most promising drug delivery vehicles and a next-generation solution for current medical challenges. In this context, variables related to flow of NPs such as the quantity of NPs lost during transport and flow trajectory greatly affect the clinical efficiency of NP drug delivery systems. Currently, there is little knowledge of the physical mechanisms dominating NP flow inside the human body due to the limitations of available experimental tools for mimicking complex physiological environments at the preclinical stage. Here, we report a coupled experimental and computational fluid dynamics (CFD)-based novel in vitro approach to predict the flow velocity and binding of NP drug delivery systems during transport through vasculature. Poly(hydroxyethyl)methacrylate hydrogels were used to form soft cylindrical constructs mimicking vascular sections as flow channels for synthesized iron oxide NPs in these first-of-its-kind transport experiments. Brownian dynamics and material of the flow channels played key roles in NP flow, based on the measurements of NP flow velocity over seven different mass concentrations. A fully developed laminar flow of the NPs under these conditions was simultaneously predicted using CFD. Results from the mass loss of NPs during flow indicated a diffusion-dominated flow at higher particle concentrations but a flow controlled by the surrounding fluid and Brownian dynamics at the lowest NP concentrations. The CFD model predicted a mass loss of 1.341% and 6.253% for the 4.12 g·mL-1 and 2.008 g·mL-1 inlet mass concentrations of the NPs, in close confirmation with the experimental results. This further highlights the reliability of our new in vitro technique in providing mechanistic insights of NP flow for potential preclinical stage applications.
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Affiliation(s)
- Armel Boutchuen
- Department of Civil and Chemical Engineering, University of Tennessee at Chattanooga, Chattanooga, Tennessee 37403, United States
| | - Dell Zimmerman
- Department of Civil and Chemical Engineering, University of Tennessee at Chattanooga, Chattanooga, Tennessee 37403, United States
| | - Abdollah Arabshahi
- SimCenter, University of Tennessee at Chattanooga, Chattanooga, Tennessee 37403, United States
| | - John Melnyczuk
- Department of Chemistry, Clark Atlanta University, Georgia 30314, United States
| | - Soubantika Palchoudhury
- Department of Civil and Chemical Engineering, University of Tennessee at Chattanooga, Chattanooga, Tennessee 37403, United States
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22
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Notch signaling and taxis mechanisms regulate early stage angiogenesis: A mathematical and computational model. PLoS Comput Biol 2020; 16:e1006919. [PMID: 31986145 PMCID: PMC7021322 DOI: 10.1371/journal.pcbi.1006919] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 02/14/2020] [Accepted: 10/16/2019] [Indexed: 12/20/2022] Open
Abstract
During angiogenesis, new blood vessels sprout and grow from existing ones. This process plays a crucial role in organ development and repair, in wound healing and in numerous pathological processes such as cancer progression or diabetes. Here, we present a mathematical model of early stage angiogenesis that permits exploration of the relative importance of mechanical, chemical and cellular cues. Endothelial cells proliferate and move over an extracellular matrix by following external gradients of Vessel Endothelial Growth Factor, adhesion and stiffness, which are incorporated to a Cellular Potts model with a finite element description of elasticity. The dynamics of Notch signaling involving Delta-4 and Jagged-1 ligands determines tip cell selection and vessel branching. Through their production rates, competing Jagged-Notch and Delta-Notch dynamics determine the influence of lateral inhibition and lateral induction on the selection of cellular phenotypes, branching of blood vessels, anastomosis (fusion of blood vessels) and angiogenesis velocity. Anastomosis may be favored or impeded depending on the mechanical configuration of strain vectors in the ECM near tip cells. Numerical simulations demonstrate that increasing Jagged production results in pathological vasculatures with thinner and more abundant vessels, which can be compensated by augmenting the production of Delta ligands. Angiogenesis is the process by which new blood vessels grow from existing ones. This process plays a crucial role in organ development, in wound healing and in numerous pathological processes such as cancer growth or in diabetes. Angiogenesis is a complex, multi-step and well regulated process where biochemistry and physics are intertwined. The process entails signaling in vessel cells being driven by both chemical and mechanical mechanisms that result in vascular cell movement, deformation and proliferation. Mathematical models have the ability to bring together these mechanisms in order to explore their relative relevance in vessel growth. Here, we present a mathematical model of early stage angiogenesis that is able to explore the role of biochemical signaling and tissue mechanics. We use this model to unravel the regulating role of Jagged, Notch and Delta dynamics in vascular cells. These membrane proteins have an important part in determining the leading cell in each neo-vascular sprout. Numerical simulations demonstrate that increasing Jagged production results in pathological vasculatures with thinner and more abundant vessels, which can be compensated by augmenting the production of Delta ligands.
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23
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Flegg JA, Menon SN, Byrne HM, McElwain DLS. A Current Perspective on Wound Healing and Tumour-Induced Angiogenesis. Bull Math Biol 2020; 82:23. [DOI: 10.1007/s11538-020-00696-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 01/02/2020] [Indexed: 12/19/2022]
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24
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Krause AL, Beliaev D, Van Gorder RA, Waters SL. Lattice and continuum modelling of a bioactive porous tissue scaffold. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2019; 36:325-360. [PMID: 30107530 DOI: 10.1093/imammb/dqy012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 01/18/2018] [Accepted: 07/16/2018] [Indexed: 12/29/2022]
Abstract
A contemporary procedure to grow artificial tissue is to seed cells onto a porous biomaterial scaffold and culture it within a perfusion bioreactor to facilitate the transport of nutrients to growing cells. Typical models of cell growth for tissue engineering applications make use of spatially homogeneous or spatially continuous equations to model cell growth, flow of culture medium, nutrient transport and their interactions. The network structure of the physical porous scaffold is often incorporated through parameters in these models, either phenomenologically or through techniques like mathematical homogenization. We derive a model on a square grid lattice to demonstrate the importance of explicitly modelling the network structure of the porous scaffold and compare results from this model with those from a modified continuum model from the literature. We capture two-way coupling between cell growth and fluid flow by allowing cells to block pores, and by allowing the shear stress of the fluid to affect cell growth and death. We explore a range of parameters for both models and demonstrate quantitative and qualitative differences between predictions from each of these approaches, including spatial pattern formation and local oscillations in cell density present only in the lattice model. These differences suggest that for some parameter regimes, corresponding to specific cell types and scaffold geometries, the lattice model gives qualitatively different model predictions than typical continuum models. Our results inform model selection for bioactive porous tissue scaffolds, aiding in the development of successful tissue engineering experiments and eventually clinically successful technologies.
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Affiliation(s)
- Andrew L Krause
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
| | - Dmitry Beliaev
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
| | - Robert A Van Gorder
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
| | - Sarah L Waters
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
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25
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Chen Y, Ali M, Shi S, Cheang UK. Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization. IEEE Trans Nanobioscience 2019; 18:498-509. [PMID: 31144640 DOI: 10.1109/tnb.2019.2919132] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted "activator" nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the "brute-force" search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.
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26
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Metzcar J, Wang Y, Heiland R, Macklin P. A Review of Cell-Based Computational Modeling in Cancer Biology. JCO Clin Cancer Inform 2019; 3:1-13. [PMID: 30715927 PMCID: PMC6584763 DOI: 10.1200/cci.18.00069] [Citation(s) in RCA: 193] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2018] [Indexed: 12/14/2022] Open
Abstract
Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer's ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.
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27
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d'Esposito A, Sweeney PW, Ali M, Saleh M, Ramasawmy R, Roberts TA, Agliardi G, Desjardins A, Lythgoe MF, Pedley RB, Shipley R, Walker-Samuel S. Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours. Nat Biomed Eng 2018; 2:773-787. [PMID: 31015649 DOI: 10.1038/s41551-018-0306-y] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 09/06/2018] [Indexed: 01/02/2023]
Abstract
Understanding the uptake of a drug by diseased tissue, and the drug's subsequent spatiotemporal distribution, are central factors in the development of effective targeted therapies. However, the interaction between the pathophysiology of diseased tissue and individual therapeutic agents can be complex, and can vary across tissue types and across subjects. Here, we show that the combination of mathematical modelling, high-resolution optical imaging of intact and optically cleared tumour tissue from animal models, and in vivo imaging of vascular perfusion predicts the heterogeneous uptake, by large tissue samples, of specific therapeutic agents, as well as their spatiotemporal distribution. In particular, by using murine models of colorectal cancer and glioma, we report and validate predictions of steady-state blood flow and intravascular and interstitial fluid pressure in tumours, of the spatially heterogeneous uptake of chelated gadolinium by tumours, and of the effect of a vascular disrupting agent on tumour vasculature.
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Affiliation(s)
- Angela d'Esposito
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK
| | - Paul W Sweeney
- Department of Mechanical Engineering, University College London, London, UK
| | - Morium Ali
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK
| | - Magdy Saleh
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK
| | - Rajiv Ramasawmy
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK
| | - Thomas A Roberts
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK
| | - Giulia Agliardi
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK
| | - Adrien Desjardins
- Department of Medical Physics, University College London, London, UK
| | - Mark F Lythgoe
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK
| | | | - Rebecca Shipley
- Department of Mechanical Engineering, University College London, London, UK.
| | - Simon Walker-Samuel
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK.
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Hassanzadeh P, Atyabi F, Dinarvand R. Ignoring the modeling approaches: Towards the shadowy paths in nanomedicine. J Control Release 2018; 280:58-75. [DOI: 10.1016/j.jconrel.2018.04.042] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 04/22/2018] [Accepted: 04/23/2018] [Indexed: 12/30/2022]
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Barradas-Bautista D, Alvarado-Mentado M, Agostino M, Cocho G. Cancer growth and metastasis as a metaphor of Go gaming: An Ising model approach. PLoS One 2018; 13:e0195654. [PMID: 29718932 PMCID: PMC5931478 DOI: 10.1371/journal.pone.0195654] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 03/27/2018] [Indexed: 01/03/2023] Open
Abstract
This work aims for modeling and simulating the metastasis of cancer, via the analogy between the cancer process and the board game Go. In the game of Go, black stones that play first could correspond to a metaphor of the birth, growth, and metastasis of cancer. Moreover, playing white stones on the second turn could correspond the inhibition of cancer invasion. Mathematical modeling and algorithmic simulation of Go may therefore benefit the efforts to deploy therapies to surpass cancer illness by providing insight into the cellular growth and expansion over a tissue area. We use the Ising Hamiltonian, that models the energy exchange in interacting particles, for modeling the cancer dynamics. Parameters in the energy function refer the biochemical elements that induce cancer birth, growth, and metastasis; as well as the biochemical immune system process of defense.
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Affiliation(s)
| | | | - Mark Agostino
- Curtin Health Innovation Research Institute and Curtin Institute Computation, Curtin University, Perth, Australia
| | - Germinal Cocho
- Complex Sciences Center, UNAM, Mexico City, Mexico.,Physics Institute, UNAM, Mexico City, Mexico
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Guo M, Cai Y, Yao X, Li Z. Mathematical modeling of atherosclerotic plaque destabilization: Role of neovascularization and intraplaque hemorrhage. J Theor Biol 2018; 450:53-65. [PMID: 29704490 DOI: 10.1016/j.jtbi.2018.04.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 03/29/2018] [Accepted: 04/23/2018] [Indexed: 01/03/2023]
Abstract
Observational studies have identified angiogenesis from the adventitial vasa vasorum and intraplaque hemorrhage (IPH) as critical factors in atherosclerotic plaque progression and destabilization. Here we propose a mathematical model incorporating intraplaque neovascularization and hemodynamic calculation with plaque destabilization for the quantitative evaluation of the role of neoangiogenesis and IPH in the vulnerable atherosclerotic plaque formation. An angiogenic microvasculature is generated by two-dimensional nine-point discretization of endothelial cell proliferation and migration from the vasa vasorum. Three key cells (endothelial cells, smooth muscle cells and macrophages) and three key chemicals (vascular endothelial growth factors, extracellular matrix and matrix metalloproteinase) are involved in the plaque progression model, and described by the reaction-diffusion partial differential equations. The hemodynamic calculation of the microcirculation on the generated microvessel network is carried out by coupling the intravascular, interstitial and transvascular flow. The plasma concentration in the interstitial domain is defined as the description of IPH area according to the diffusion and convection with the interstitial fluid flow, as well as the extravascular movement across the leaky vessel wall. The simulation results demonstrate a series of pathophysiological phenomena during the vulnerable progression of an atherosclerotic plaque, including the expanding necrotic core, the exacerbated inflammation, the high microvessel density (MVD) region at the shoulder areas, the transvascular flow through the capillary wall and the IPH. The important role of IPH in the plaque destabilization is evidenced by simulations with varied model parameters. It is found that the IPH can significantly speed up the plaque vulnerability by increasing necrotic core and thinning fibrous cap. In addition, the decreased MVD and vessel permeability may slow down the process of plaque destabilization by reducing the IPH dramatically. We envision that the present model and its future advances can serve as a valuable theoretical platform for studying the dynamic changes in the microenvironment during the plaque destabilization.
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Affiliation(s)
- Muyi Guo
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yan Cai
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xinke Yao
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Zhiyong Li
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China; School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, QLD 4001, Australia.
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31
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Mahlbacher G, Curtis LT, Lowengrub J, Frieboes HB. Mathematical modeling of tumor-associated macrophage interactions with the cancer microenvironment. J Immunother Cancer 2018; 6:10. [PMID: 29382395 PMCID: PMC5791333 DOI: 10.1186/s40425-017-0313-7] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 12/20/2017] [Indexed: 02/06/2023] Open
Abstract
Background Immuno-oncotherapy has emerged as a promising means to target cancer. In particular, therapeutic manipulation of tumor-associated macrophages holds promise due to their various and sometimes opposing roles in tumor progression. It is established that M1-type macrophages suppress tumor progression while M2-types support it. Recently, Tie2-expressing macrophages (TEM) have been identified as a distinct sub-population influencing tumor angiogenesis and vascular remodeling as well as monocyte differentiation. Methods This study develops a modeling framework to evaluate macrophage interactions with the tumor microenvironment, enabling assessment of how these interactions may affect tumor progression. M1, M2, and Tie2 expressing variants are integrated into a model of tumor growth representing a metastatic lesion in a highly vascularized organ, such as the liver. Behaviors simulated include M1 release of nitric oxide (NO), M2 release of growth-promoting factors, and TEM facilitation of angiogenesis via Angiopoietin-2 and promotion of monocyte differentiation into M2 via IL-10. Results The results show that M2 presence leads to larger tumor growth regardless of TEM effects, implying that immunotherapeutic strategies that lead to TEM ablation may fail to restrain growth when the M2 represents a sizeable population. As TEM pro-tumor effects are less pronounced and on a longer time scale than M1-driven tumor inhibition, a more nuanced approach to influence monocyte differentiation taking into account the tumor state (e.g., under chemotherapy) may be desirable. Conclusions The results highlight the dynamic interaction of macrophages within a growing tumor, and, further, establish the initial feasibility of a mathematical framework that could longer term help to optimize cancer immunotherapy.
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Affiliation(s)
- Grace Mahlbacher
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40208, USA
| | - Louis T Curtis
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40208, USA
| | - John Lowengrub
- Department of Mathematics, University of California, 540H Rowland Hall, Irvine, CA, 92697, USA.,Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40208, USA. .,James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA. .,Department of Pharmacology & Toxicology, University of Louisville, Louisville, KY, USA.
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A microfluidic device for study of the effect of tumor vascular structures on the flow field and HepG2 cellular flow behaviors. Biochem Biophys Res Commun 2018; 496:238-243. [PMID: 29309789 DOI: 10.1016/j.bbrc.2018.01.035] [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: 01/01/2018] [Accepted: 01/04/2018] [Indexed: 11/23/2022]
Abstract
To build a microfluidic device with various morphological features of the tumor vasculature for study of the effects of tumor vascular structures on the flow field and tumor cellular flow behaviors. The designed microfluidic device was able to approximatively simulate the in vivo structures of tumor vessels and the flow within it. In this models, the influences of the angle of bifurcation, the number of branches, and the narrow channels on the flow field and the influence of vorticity on the retention of HepG2 cells were significant. Additionally, shear stress below physiological conditions of blood circulation has considerable effect on the formation of the lumen-like structures (LLSs) of HepG2 cells. These results can provide some data and reference in the understanding of the interaction between hemorheological properties and tumor vascular structures in solid tumors.
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Chen Y, Shi S, Yao X, Nakano T. Touchable Computing: Computing-Inspired Bio-Detection. IEEE Trans Nanobioscience 2018; 16:810-821. [PMID: 29364125 DOI: 10.1109/tnb.2017.2769162] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We propose a new computing-inspired bio-detection framework called touchable computing (TouchComp). Under the rubric of TouchComp, the best solution is the cancer to be detected, the parameter space is the tissue region at high risk of malignancy, and the agents are the nanorobots loaded with contrast medium molecules for tracking purpose. Subsequently, the cancer detection procedure (CDP) can be interpreted from the computational optimization perspective: a population of externally steerable agents (i.e., nanorobots) locate the optimal solution (i.e., cancer) by moving through the parameter space (i.e., tissue under screening), whose landscape (i.e., a prescribed feature of tissue environment) may be altered by these agents but the location of the best solution remains unchanged. One can then infer the landscape by observing the movement of agents by applying the "seeing-is-sensing" principle. The term "touchable" emphasizes the framework's similarity to controlling by touching the screen with a finger, where the external field for controlling and tracking acts as the finger. Given this analogy, we aim to answer the following profound question: can we look to the fertile field of computational optimization algorithms for solutions to achieve effective cancer detection that are fast, accurate, and robust? Along this line of thought, we consider the classical particle swarm optimization (PSO) as an example and propose the PSO-inspired CDP, which differs from the standard PSO by taking into account realistic in vivo propagation and controlling of nanorobots. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the PSO-inspired CDP for different blood flow velocity profiles caused by tumor-induced angiogenesis. The proposed TouchComp bio-detection framework may be regarded as one form of natural computing that employs natural materials to compute.
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34
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Developing Computational Geometry and Network Graph Models of Human Lymphatic System. COMPUTATION 2017. [DOI: 10.3390/computation6010001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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35
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Yonucu S, Yιlmaz D, Phipps C, Unlu MB, Kohandel M. Quantifying the effects of antiangiogenic and chemotherapy drug combinations on drug delivery and treatment efficacy. PLoS Comput Biol 2017; 13:e1005724. [PMID: 28922358 PMCID: PMC5633204 DOI: 10.1371/journal.pcbi.1005724] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 10/09/2017] [Accepted: 08/14/2017] [Indexed: 11/18/2022] Open
Abstract
Tumor-induced angiogenesis leads to the development of leaky tumor vessels devoid of structural and morphological integrity. Due to angiogenesis, elevated interstitial fluid pressure (IFP) and low blood perfusion emerge as common properties of the tumor microenvironment that act as barriers for drug delivery. In order to overcome these barriers, normalization of vasculature is considered to be a viable option. However, insight is needed into the phenomenon of normalization and in which conditions it can realize its promise. In order to explore the effect of microenvironmental conditions and drug scheduling on normalization benefit, we build a mathematical model that incorporates tumor growth, angiogenesis and IFP. We administer various theoretical combinations of antiangiogenic agents and cytotoxic nanoparticles through heterogeneous vasculature that displays a similar morphology to tumor vasculature. We observe differences in drug extravasation that depend on the scheduling of combined therapy; for concurrent therapy, total drug extravasation is increased but in adjuvant therapy, drugs can penetrate into deeper regions of tumor.
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Affiliation(s)
- Sirin Yonucu
- Department of Physics, Bogazici University, Bebek, Istanbul, Turkey
- Center for Life Sciences and Technologies, Bogazici University, Bebek, Istanbul, Turkey
| | - Defne Yιlmaz
- Department of Physics, Bogazici University, Bebek, Istanbul, Turkey
- Center for Life Sciences and Technologies, Bogazici University, Bebek, Istanbul, Turkey
| | - Colin Phipps
- School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada
| | - Mehmet Burcin Unlu
- Department of Physics, Bogazici University, Bebek, Istanbul, Turkey
- Center for Life Sciences and Technologies, Bogazici University, Bebek, Istanbul, Turkey
- * E-mail:
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
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36
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ZHAO GAIPING, CHEN ERYUN, YU XIAOLI, CUI HAIPO, LV JIE, WU JIE. THREE-DIMENSIONAL MODEL OF METASTATIC TUMOR ANGIOGENESIS IN RESPONSE TO ANTI-ANGIOGENIC FACTOR ANGIOSTATIN. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417500944] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Surgeons observed that primary tumors are capable of suppressing the growth of their metastases by generating anti-angiogenic factor angiostatin. A three-dimensional (3D) mathematical model of development of the metastatic tumor vasculature is presented to simulate the morphology and construction of 3D microvascular networks under the inhibitory effect of anti-angiogenic factor angiostatin excreted by the primary tumor. The simulation results demonstrate that metastatic tumor microvascular density (MVD) decreases by about 60%, 58% and 52%, respectively, at [Formula: see text], 7 and 14 days under the effect of anti-angiogenic factor angiostatin. The abnormal geometric and morphological features of 3D microvasculature networks inside and outside the metastatic tumor improve in the presence of angiostatin. The present model may allow to simulate experimental tests and may provide theoretical models for clinical research of anti-angiogenic therapy strategies.
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Affiliation(s)
- GAIPING ZHAO
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - ERYUN CHEN
- School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093 P. R. China
| | - XIAOLI YU
- Fudan University Shanghai Cancer Center, Shanghai 200032, P. R. China
| | - HAIPO CUI
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - JIE LV
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - JIE WU
- School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
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Macklin P, Frieboes HB, Sparks JL, Ghaffarizadeh A, Friedman SH, Juarez EF, Jonckheere E, Mumenthaler SM. Progress Towards Computational 3-D Multicellular Systems Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 936:225-246. [PMID: 27739051 DOI: 10.1007/978-3-319-42023-3_12] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Tumors cannot be understood in isolation from their microenvironment. Tumor and stromal cells change phenotype based upon biochemical and biophysical inputs from their surroundings, even as they interact with and remodel the microenvironment. Cancer should be investigated as an adaptive, multicellular system in a dynamical microenvironment. Computational modeling offers the potential to detangle this complex system, but the modeling platform must ideally account for tumor heterogeneity, substrate and signaling factor biotransport, cell and tissue biophysics, tissue and vascular remodeling, microvascular and interstitial flow, and links between all these sub-systems. Such a platform should leverage high-throughput experimental data, while using open data standards for reproducibility. In this chapter, we review advances by our groups in these key areas, particularly in advanced models of tissue mechanics and interstitial flow, open source simulation software, high-throughput phenotypic screening, and multicellular data standards. In the future, we expect a transformation of computational cancer biology from individual groups modeling isolated parts of cancer, to coalitions of groups combining compatible tools to simulate the 3-D multicellular systems biology of cancer tissues.
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Affiliation(s)
- Paul Macklin
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Jessica L Sparks
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Ahmadreza Ghaffarizadeh
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Samuel H Friedman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Edwin F Juarez
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA.,Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Edmond Jonckheere
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
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Grogan JA, Markelc B, Connor AJ, Muschel RJ, Pitt-Francis JM, Maini PK, Byrne HM. Predicting the Influence of Microvascular Structure On Tumor Response to Radiotherapy. IEEE Trans Biomed Eng 2017; 64:504-511. [PMID: 27623567 DOI: 10.1109/tbme.2016.2606563] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2024]
Abstract
OBJECTIVE The purpose of this study is to investigate how theoretical predictions of tumor response to radiotherapy (RT) depend on the morphology and spatial representation of the microvascular network. METHODS A hybrid multiscale model, which couples a cellular automaton model of tumor growth with a model for oxygen transport from blood vessels, is used to predict the viable fraction of cells following one week of simulated RT. Both artificial and biologically derived three-dimensional (3-D) vessel networks of well vascularized tumors are considered and predictions compared with 2-D descriptions. RESULTS For literature-derived values of the cellular oxygen consumption rate there is little difference in predicted viable fraction when 3-D network representations of biological or artificial vessel networks are employed. Different 2-D representations are shown to either over- or under-estimate viable fractions relative to the 3-D cases, with predictions based on point-wise descriptions shown to have greater sensitivity to vessel network morphology. CONCLUSION The predicted RT response is relatively insensitive to the morphology of the microvessel network when 3-D representations are adopted, however, sensitivity is greater in certain 2-D representations. SIGNIFICANCE By using realistic 3-D vessel network geometries this study shows that real and artificial network descriptions and assumptions of spatially uniform oxygen distributions lead to similar RT response predictions in relatively small tissue volumes. This suggests that either a more detailed description of oxygen transport in the microvasculature is required or that the oxygen enhancement ratio used in the well known linear-quadratic RT response model is relatively insensitive to microvascular structure.
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Affiliation(s)
- James A Grogan
- Mathematical Institute, University of Oxford, Oxford, U.K
| | - Bostjan Markelc
- Department of OncologyCRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford
| | | | - Ruth J Muschel
- Department of OncologyCRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford
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Ud-Din S, Bayat A. Non-animal models of wound healing in cutaneous repair: In silico, in vitro, ex vivo, and in vivo models of wounds and scars in human skin. Wound Repair Regen 2017; 25:164-176. [PMID: 28120405 DOI: 10.1111/wrr.12513] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 12/15/2016] [Indexed: 01/08/2023]
Affiliation(s)
- Sara Ud-Din
- Plastic and Reconstructive Surgery Research, Centre for Dermatology Research; University of Manchester; Manchester United Kingdom
| | - Ardeshir Bayat
- Plastic and Reconstructive Surgery Research, Centre for Dermatology Research; University of Manchester; Manchester United Kingdom
- Bioengineering Research Group, School of Materials, Faculty of Engineering & Physical Sciences; The University of Manchester; Manchester United Kingdom
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40
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A Multiscale Approach to the Migration of Cancer Stem Cells: Mathematical Modelling and Simulations. Bull Math Biol 2016; 79:209-235. [DOI: 10.1007/s11538-016-0233-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 11/04/2016] [Indexed: 11/29/2022]
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41
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Yan H, Romero-Lopez M, Frieboes HB, Hughes CCW, Lowengrub JS. Multiscale Modeling of Glioblastoma Suggests that the Partial Disruption of Vessel/Cancer Stem Cell Crosstalk Can Promote Tumor Regression Without Increasing Invasiveness. IEEE Trans Biomed Eng 2016; 64:538-548. [PMID: 27723576 DOI: 10.1109/tbme.2016.2615566] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE In glioblastoma, the crosstalk between vascular endothelial cells (VECs) and glioma stem cells (GSCs) has been shown to enhance tumor growth. We propose a multiscale mathematical model to study this mechanism, explore tumor growth under various initial and microenvironmental conditions, and investigate the effects of blocking this crosstalk. METHODS We develop a hybrid continuum-discrete model of highly organized vascularized tumors. VEC-GSC crosstalk is modeled via vascular endothelial growth factor (VEGF) production by tumor cells and by secretion of soluble factors by VECs that promote GSC self-renewal and proliferation. RESULTS VEC-GSC crosstalk increases both tumor size and GSC fraction by enhancing GSC activity and neovascular development. VEGF promotes vessel formation, and larger VEGF sources typically increase vessel numbers, which enhances tumor growth and stabilizes the tumor shape. Increasing the initial GSC fraction has a similar effect. Partially disrupting the crosstalk by blocking VEC secretion of GSC promoters reduces tumor size but does not increase invasiveness, which is in contrast to antiangiogenic therapies, which reduce tumor size but may significantly increase tumor invasiveness. SIGNIFICANCE Multiscale modeling supports the targeting of VEC-GSC crosstalk as a promising approach for cancer therapy.
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Boujelben A, Watson M, McDougall S, Yen YF, Gerstner ER, Catana C, Deisboeck T, Batchelor TT, Boas D, Rosen B, Kalpathy-Cramer J, Chaplain MAJ. Multimodality imaging and mathematical modelling of drug delivery to glioblastomas. Interface Focus 2016; 6:20160039. [PMID: 27708763 DOI: 10.1098/rsfs.2016.0039] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Patients diagnosed with glioblastoma, an aggressive brain tumour, have a poor prognosis, with a median overall survival of less than 15 months. Vasculature within these tumours is typically abnormal, with increased tortuosity, dilation and disorganization, and they typically exhibit a disrupted blood-brain barrier (BBB). Although it has been hypothesized that the 'normalization' of the vasculature resulting from anti-angiogenic therapies could improve drug delivery through improved blood flow, there is also evidence that suggests that the restoration of BBB integrity might limit the delivery of therapeutic agents and hence their effectiveness. In this paper, we apply mathematical models of blood flow, vascular permeability and diffusion within the tumour microenvironment to investigate the effect of these competing factors on drug delivery. Preliminary results from the modelling indicate that all three physiological parameters investigated-flow rate, vessel permeability and tissue diffusion coefficient-interact nonlinearly to produce the observed average drug concentration in the microenvironment.
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Affiliation(s)
- Ahmed Boujelben
- School of Petroleum Engineering , Heriot-Watt University , Edinburgh EH14 4AS , UK
| | - Michael Watson
- School of Petroleum Engineering , Heriot-Watt University , Edinburgh EH14 4AS , UK
| | - Steven McDougall
- School of Petroleum Engineering , Heriot-Watt University , Edinburgh EH14 4AS , UK
| | - Yi-Fen Yen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Harvard Medical School , Charlestown, MA 02129 , USA
| | - Elizabeth R Gerstner
- Department of Neurology, Massachusetts General Hospital , Harvard Medical School , Boston, MA , USA
| | - Ciprian Catana
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Harvard Medical School , Charlestown, MA 02129 , USA
| | - Thomas Deisboeck
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Harvard Medical School , Charlestown, MA 02129 , USA
| | - Tracy T Batchelor
- Department of Neurology, Massachusetts General Hospital , Harvard Medical School , Boston, MA , USA
| | - David Boas
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Harvard Medical School , Charlestown, MA 02129 , USA
| | - Bruce Rosen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Harvard Medical School , Charlestown, MA 02129 , USA
| | - Jayashree Kalpathy-Cramer
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Harvard Medical School , Charlestown, MA 02129 , USA
| | - Mark A J Chaplain
- School of Mathematics and Statistics , University of St Andrews , St Andrews KY16 9SS , UK
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Zakariapour M, Hamedi MH, Fatouraee N. Numerical Investigation of Magnetic Nanoparticles Distribution Inside a Cylindrical Porous Tumor Considering the Influences of Interstitial Fluid Flow. Transp Porous Media 2016. [DOI: 10.1007/s11242-016-0772-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Jiang C, Cui C, Zhong W, Li G, Li L, Shao Y. Tumor proliferation and diffusion on percolation clusters. J Biol Phys 2016; 42:637-658. [PMID: 27678112 DOI: 10.1007/s10867-016-9427-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 07/24/2016] [Indexed: 12/28/2022] Open
Abstract
We study in silico the influence of host tissue inhomogeneity on tumor cell proliferation and diffusion by simulating the mobility of a tumor on percolation clusters with different homogeneities of surrounding tissues. The proliferation and diffusion of a tumor in an inhomogeneous tissue could be characterized in the framework of the percolation theory, which displays similar thresholds (0.54, 0.44, and 0.37, respectively) for tumor proliferation and diffusion in three kinds of lattices with 4, 6, and 8 connecting near neighbors. Our study reveals the existence of a critical transition concerning the survival and diffusion of tumor cells with leaping metastatic diffusion movement in the host tissues. Tumor cells usually flow in the direction of greater pressure variation during their diffusing and infiltrating to a further location in the host tissue. Some specific sites suitable for tumor invasion were observed on the percolation cluster and around these specific sites a tumor can develop into scattered tumors linked by some advantage tunnels that facilitate tumor invasion. We also investigate the manner that tissue inhomogeneity surrounding a tumor may influence the velocity of tumor diffusion and invasion. Our simulation suggested that invasion of a tumor is controlled by the homogeneity of the tumor microenvironment, which is basically consistent with the experimental report by Riching et al. as well as our clinical observation of medical imaging. Both simulation and clinical observation proved that tumor diffusion and invasion into the surrounding host tissue is positively correlated with the homogeneity of the tissue.
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Affiliation(s)
- Chongming Jiang
- School of Physics, Sun Yat-sen University, Guangzhou, 510275, China.,BGI-Research in Shenzhen, Shenzhen, 518083, China
| | - Chunyan Cui
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Weirong Zhong
- Siyuan Laboratory, Guangzhou Key Laboratory of Vacuum Coating Technologies and New Energy Materials, Department of Physics, Jinan University, Guangzhou, 510632, China
| | - Gang Li
- School of Physics, Sun Yat-sen University, Guangzhou, 510275, China
| | - Li Li
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Yuanzhi Shao
- School of Physics, Sun Yat-sen University, Guangzhou, 510275, China.
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Naghavi N, Hosseini FS, Sardarabadi M, Kalani H. Simulation of tumor induced angiogenesis using an analytical adaptive modeling including dynamic sprouting and blood flow modeling. Microvasc Res 2016; 107:51-64. [PMID: 27179697 DOI: 10.1016/j.mvr.2016.05.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Revised: 04/18/2016] [Accepted: 05/08/2016] [Indexed: 11/26/2022]
Abstract
In this paper, an adaptive model for tumor induced angiogenesis is developed that integrates generation and diffusion of a growth factor originated from hypoxic cells, adaptive sprouting from a parent vessel, blood flow and structural adaptation. The proposed adaptive sprout spacing model (ASS) determines position, time and number of sprouts which are activated from a parent vessel and also the developed vascular network is modified by a novel sprout branching prediction algorithm. This algorithm couples local vascular endothelial growth factor (VEGF) concentrations, stresses due to the blood flow and stochastic branching to the structural reactions of each vessel segment in response to mechanical and biochemical stimuli. The results provide predictions for the time-dependent development of the network structure, including the position and diameters of each segment and the resulting distributions of blood flow and VEGF. Considering time delays between sprout progressions and number of sprouts activated at different time durations provides information about micro-vessel density in the network. Resulting insights could be useful for motivating experimental investigations of vascular pattern in tumor induced angiogenesis and development of therapies targeting angiogenesis.
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Affiliation(s)
- Nadia Naghavi
- Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; Center of Excellence on Soft Computing and Intelligent Information Processing, Mashhad, Iran.
| | - Farideh S Hosseini
- Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohammad Sardarabadi
- Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; Micro/Nano-fluidics and MEMS/NEMS Laboratory, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Hadi Kalani
- Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; Center of Excellence on Soft Computing and Intelligent Information Processing, Mashhad, Iran
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Giverso C, Ciarletta P. Tumour angiogenesis as a chemo-mechanical surface instability. Sci Rep 2016; 6:22610. [PMID: 26948692 PMCID: PMC4780075 DOI: 10.1038/srep22610] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 02/17/2016] [Indexed: 11/17/2022] Open
Abstract
The hypoxic conditions within avascular solid tumours may trigger the secretion of chemical factors, which diffuse to the nearby vasculature and promote the formation of new vessels eventually joining the tumour. Mathematical models of this process, known as tumour angiogenesis, have mainly investigated the formation of the new capillary networks using reaction-diffusion equations. Since angiogenesis involves the growth dynamics of the endothelial cells sprouting, we propose in this work an alternative mechanistic approach, developing a surface growth model for studying capillary formation and network dynamics. The model takes into account the proliferation of endothelial cells on the pre-existing capillary surface, coupled with the bulk diffusion of the vascular endothelial growth factor (VEGF). The thermo-dynamical consistency is imposed by means of interfacial and bulk balance laws. Finite element simulations show that both the morphology and the dynamics of the sprouting vessels are controlled by the bulk diffusion of VEGF and the chemo-mechanical and geometric properties at the capillary interface. Similarly to dendritic growth processes, we suggest that the emergence of tree-like vessel structures during tumour angiogenesis may result from the free boundary instability driven by competition between chemical and mechanical phenomena occurring at different length-scales.
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Affiliation(s)
- Chiara Giverso
- Dipartimento di Matematica - MOX, Politecnico di Milano and
Fondazione CEN, Piazza Leonardo da Vinci, 32-20133
Milano, Italy
| | - Pasquale Ciarletta
- Dipartimento di Matematica - MOX, Politecnico di Milano and
Fondazione CEN, Piazza Leonardo da Vinci, 32-20133
Milano, Italy
- CNRS and Sorbonne Universités, UPMC
Univ Paris 06, UMR 7190, Institut Jean le Rond d’Alembert, 4 place
Jussieu case 162, 75005
Paris, France
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Muralidhar A, Swadel E, Spiekerman M, Suei S, Fraser M, Ingerfeld M, Tayagui AB, Garrill A. A pressure gradient facilitates mass flow in the oomycete Achlya bisexualis. Microbiology (Reading) 2016; 162:206-213. [DOI: 10.1099/mic.0.000216] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
- Abishek Muralidhar
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Emma Swadel
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Marjolein Spiekerman
- Plant Cell Biology, Wageningen University, PO Box 633 6700 AP Wageningen, The Netherlands
| | - Sandy Suei
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Miranda Fraser
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Manfred Ingerfeld
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Ayelen B. Tayagui
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Ashley Garrill
- School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
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Computer Simulations of the Tumor Vasculature: Applications to Interstitial Fluid Flow, Drug Delivery, and Oxygen Supply. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 936:31-72. [PMID: 27739042 DOI: 10.1007/978-3-319-42023-3_3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Tumor vasculature, the blood vessel network supplying a growing tumor with nutrients such as oxygen or glucose, is in many respects different from the hierarchically organized arterio-venous blood vessel network in normal tissues. Angiogenesis (the formation of new blood vessels), vessel cooption (the integration of existing blood vessels into the tumor vasculature), and vessel regression remodel the healthy vascular network into a tumor-specific vasculature. Integrative models, based on detailed experimental data and physical laws, implement, in silico, the complex interplay of molecular pathways, cell proliferation, migration, and death, tissue microenvironment, mechanical and hydrodynamic forces, and the fine structure of the host tissue vasculature. With the help of computer simulations high-precision information about blood flow patterns, interstitial fluid flow, drug distribution, oxygen and nutrient distribution can be obtained and a plethora of therapeutic protocols can be tested before clinical trials. This chapter provides an overview over the current status of computer simulations of vascular remodeling during tumor growth including interstitial fluid flow, drug delivery, and oxygen supply within the tumor. The model predictions are compared with experimental and clinical data and a number of longstanding physiological paradigms about tumor vasculature and intratumoral solute transport are critically scrutinized.
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Yu X, Trase I, Ren M, Duval K, Guo X, Chen Z. Design of Nanoparticle-Based Carriers for Targeted Drug Delivery. JOURNAL OF NANOMATERIALS 2016; 2016:1087250. [PMID: 27398083 PMCID: PMC4936496 DOI: 10.1155/2016/1087250] [Citation(s) in RCA: 118] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Nanoparticles have shown promise as both drug delivery vehicles and direct antitumor systems, but they must be properly designed in order to maximize efficacy. Computational modeling is often used both to design new nanoparticles and to better understand existing ones. Modeled processes include the release of drugs at the tumor site and the physical interaction between the nanoparticle and cancer cells. In this article, we provide an overview of three different targeted drug delivery methods (passive targeting, active targeting and physical targeting), compare methods of action, advantages, limitations, and the current stage of research. For the most commonly used nanoparticle carriers, fabrication methods are also reviewed. This is followed by a review of computational simulations and models on nanoparticle-based drug delivery.
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Affiliation(s)
- Xiaojiao Yu
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | - Ian Trase
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | - Muqing Ren
- Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Kayla Duval
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | - Xing Guo
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | - Zi Chen
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
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The Tumor Microenvironment as a Barrier to Cancer Nanotherapy. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 936:165-190. [PMID: 27739048 DOI: 10.1007/978-3-319-42023-3_9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Although extensive research effort and resources have been dedicated to the development of nanotherapeutics to treat cancer, few formulations have reached clinical application. A major reason is that the large number of parameters available to tune nanotherapy characteristics coupled with the variability in tumor tissue precludes evaluation of complex interactions through experimentation alone. In order to optimize the nanotechnology design and gain further insight into these phenomena, mathematical modeling and computational simulation have been applied to complement empirical work. In this chapter, we discuss modeling work related to nanotherapy and the tumor microenvironment. We first summarize the biology underlying the dysregulated tumor microenvironment, followed by a description of major nano-scale parameters. We then present an overview of the mathematical modeling of cancer nanotherapy, including evaluation of nanotherapy in multi-dimensional tumor tissue, coupling of nanotherapy with vascular flow, modeling of nanotherapy in combination with in vivo imaging, modeling of nanoparticle transport based on in vitro data, modeling of vasculature-bound nanoparticles, evaluation of nanotherapy using pharmacokinetic modeling, and modeling of nano-based hyperthermia. We conclude that an even tighter interdisciplinary effort between biological, material, and physical scientists is needed in order to eventually overcome the tumor microenvironment barrier to successful nanotherapy.
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