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Kunz LV, Bosque JJ, Nikmaneshi M, Chamseddine I, Munn LL, Schuemann J, Paganetti H, Bertolet A. AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response. Bull Math Biol 2024; 86:139. [PMID: 39460828 DOI: 10.1007/s11538-024-01371-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 10/09/2024] [Indexed: 10/28/2024]
Abstract
Computational models of tumor growth are valuable for simulating the dynamics of cancer progression and treatment responses. In particular, agent-based models (ABMs) tracking individual agents and their interactions are useful for their flexibility and ability to model complex behaviors. However, ABMs have often been confined to small domains or, when scaled up, have neglected crucial aspects like vasculature. Additionally, the integration into tumor ABMs of precise radiation dose calculations using gold-standard Monte Carlo (MC) methods, crucial in contemporary radiotherapy, has been lacking. Here, we introduce AMBER, an Agent-based fraMework for radioBiological Effects in Radiotherapy that computationally models tumor growth and radiation responses. AMBER is based on a voxelized geometry, enabling realistic simulations at relevant pre-clinical scales by tracking temporally discrete states stepwise. Its hybrid approach, combining traditional ABM techniques with continuous spatiotemporal fields of key microenvironmental factors such as oxygen and vascular endothelial growth factor, facilitates the generation of realistic tortuous vascular trees. Moreover, AMBER is integrated with TOPAS, an MC-based particle transport algorithm that simulates heterogeneous radiation doses. The impact of radiation on tumor dynamics considers the microenvironmental factors that alter radiosensitivity, such as oxygen availability, providing a full coupling between the biological and physical aspects. Our results show that simulations with AMBER yield accurate tumor evolution and radiation treatment outcomes, consistent with established volumetric growth laws and radiobiological understanding. Thus, AMBER emerges as a promising tool for replicating essential features of tumor growth and radiation response, offering a modular design for future expansions to incorporate specific biological traits.
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Affiliation(s)
- Louis V Kunz
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Jesús J Bosque
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Mohammad Nikmaneshi
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Ibrahim Chamseddine
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Lance L Munn
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Alejandro Bertolet
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
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2
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Crossley RM, Johnson S, Tsingos E, Bell Z, Berardi M, Botticelli M, Braat QJS, Metzcar J, Ruscone M, Yin Y, Shuttleworth R. Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist. Front Cell Dev Biol 2024; 12:1354132. [PMID: 38495620 PMCID: PMC10940354 DOI: 10.3389/fcell.2024.1354132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/12/2024] [Indexed: 03/19/2024] Open
Abstract
The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.
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Affiliation(s)
- Rebecca M. Crossley
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Samuel Johnson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Erika Tsingos
- Computational Developmental Biology Group, Institute of Biodynamics and Biocomplexity, Utrecht University, Utrecht, Netherlands
| | - Zoe Bell
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Massimiliano Berardi
- LaserLab, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Optics11 life, Amsterdam, Netherlands
| | | | - Quirine J. S. Braat
- Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, Netherlands
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
- Department of Informatics, Indiana University, Bloomington, IN, United States
| | | | - Yuan Yin
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
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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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Oliveira RHDM, Annex BH, Popel AS. Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway. Front Physiol 2024; 15:1351753. [PMID: 38455844 PMCID: PMC10917925 DOI: 10.3389/fphys.2024.1351753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024] Open
Abstract
Introduction: Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between endothelial cells under normoxia and hypoxia and their response to different stimuli can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. Methods: We calibrate and fit the model parameters based on well-established modeling techniques that include structural and practical parameter identifiability, uncertainty quantification, and global sensitivity. Results: Our results indicate that the main pathways involved in patterning tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia interferes with how different stimuli affect patterning. Additionally, our simulations indicate that Notch signaling might regulate vascular permeability and establish different Nitric Oxide release patterns for tip/stalk cells. Following simulations with various stimuli, our model suggests that factors such as time under hypoxia and oxygen availability must be considered for EC pattern control. Discussion: This project provides insights into the signaling and patterning of endothelial cells under various oxygen levels and stimulation by VEGFA and is our first integrative approach toward achieving EC control as a method for improving angiogenesis. Overall, our model provides a computational framework that can be built on to test angiogenesis-related therapies by modulation of different pathways, such as the Notch pathway.
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Affiliation(s)
| | - Brian H. Annex
- Medical College of Georgia, Augusta University, Augusta, GA, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
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5
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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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6
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Köry J, Narain V, Stolz BJ, Kaeppler J, Markelc B, Muschel RJ, Maini PK, Pitt-Francis JM, Byrne HM. Enhanced perfusion following exposure to radiotherapy: A theoretical investigation. PLoS Comput Biol 2024; 20:e1011252. [PMID: 38363799 PMCID: PMC10903964 DOI: 10.1371/journal.pcbi.1011252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 02/29/2024] [Accepted: 01/23/2024] [Indexed: 02/18/2024] Open
Abstract
Tumour angiogenesis leads to the formation of blood vessels that are structurally and spatially heterogeneous. Poor blood perfusion, in conjunction with increased hypoxia and oxygen heterogeneity, impairs a tumour's response to radiotherapy. The optimal strategy for enhancing tumour perfusion remains unclear, preventing its regular deployment in combination therapies. In this work, we first identify vascular architectural features that correlate with enhanced perfusion following radiotherapy, using in vivo imaging data from vascular tumours. Then, we present a novel computational model to determine the relationship between these architectural features and blood perfusion in silico. If perfusion is defined to be the proportion of vessels that support blood flow, we find that vascular networks with small mean diameters and large numbers of angiogenic sprouts show the largest increases in perfusion post-irradiation for both biological and synthetic tumours. We also identify cases where perfusion increases due to the pruning of hypoperfused vessels, rather than blood being rerouted. These results indicate the importance of considering network composition when determining the optimal irradiation strategy. In the future, we aim to use our findings to identify tumours that are good candidates for perfusion enhancement and to improve the efficacy of combination therapies.
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Affiliation(s)
- Jakub Köry
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Vedang Narain
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Bernadette J. Stolz
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Laboratory for Topology and Neuroscience, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jakob Kaeppler
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Bostjan Markelc
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Ruth J. Muschel
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Philip K. Maini
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Joe M. Pitt-Francis
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Helen M. Byrne
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
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7
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Noerr PS, Zamora Alvarado JE, Golnaraghi F, McCloskey KE, Gopinathan A, Dasbiswas K. Optimal mechanical interactions direct multicellular network formation on elastic substrates. Proc Natl Acad Sci U S A 2023; 120:e2301555120. [PMID: 37910554 PMCID: PMC10636364 DOI: 10.1073/pnas.2301555120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/09/2023] [Indexed: 11/03/2023] Open
Abstract
Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how intercellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue engineering. Here, by combining an agent-based model for contractile cells on elastic substrates with endothelial cell culture experiments, we show that substrate deformation-mediated mechanical interactions between cells can cluster and align them into branched networks. Motivated by the structure and function of vasculogenic networks, we predict how measures of network connectivity like percolation probability and fractal dimension as well as local morphological features including junctions, branches, and rings depend on cell contractility and density and on substrate elastic properties including stiffness and compressibility. We predict and confirm with experiments that cell network formation is substrate stiffness dependent, being optimal at intermediate stiffness. We also show the agreement between experimental data and predicted cell cluster types by mapping a combined phase diagram in cell density substrate stiffness. Overall, we show that long-range, mechanical interactions provide an optimal and general strategy for multicellular self-organization, leading to more robust and efficient realizations of space-spanning networks than through just local intercellular interactions.
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Affiliation(s)
- Patrick S. Noerr
- Department of Physics, University of California, Merced, CA95343
| | - Jose E. Zamora Alvarado
- Department of Materials and Biomaterials Science and Engineering, University of California, Merced, CA95343
| | | | - Kara E. McCloskey
- Department of Materials and Biomaterials Science and Engineering, University of California, Merced, CA95343
| | - Ajay Gopinathan
- Department of Physics, University of California, Merced, CA95343
| | - Kinjal Dasbiswas
- Department of Physics, University of California, Merced, CA95343
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8
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Terragni F, Martinson WD, Carretero M, Maini PK, Bonilla LL. Soliton approximation in continuum models of leader-follower behavior. Phys Rev E 2023; 108:054407. [PMID: 38115402 DOI: 10.1103/physreve.108.054407] [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: 07/28/2023] [Accepted: 10/17/2023] [Indexed: 12/21/2023]
Abstract
Complex biological processes involve collective behavior of entities (bacteria, cells, animals) over many length and time scales and can be described by discrete models that track individuals or by continuum models involving densities and fields. We consider hybrid stochastic agent-based models of branching morphogenesis and angiogenesis (new blood vessel creation from preexisting vasculature), which treat cells as individuals that are guided by underlying continuous chemical and/or mechanical fields. In these descriptions, leader (tip) cells emerge from existing branches and follower (stalk) cells build the new sprout in their wake. Vessel branching and fusion (anastomosis) occur as a result of tip and stalk cell dynamics. Coarse graining these hybrid models in appropriate limits produces continuum partial differential equations (PDEs) for endothelial cell densities that are more analytically tractable. While these models differ in nonlinearity, they produce similar equations at leading order when chemotaxis is dominant. We analyze this leading order system in a simple quasi-one-dimensional geometry and show that the numerical solution of the leading order PDE is well described by a soliton wave that evolves from vessel to source. This wave is an attractor for intermediate times until it arrives at the hypoxic region releasing the growth factor. The mathematical techniques used here thus identify common features of discrete and continuum approaches and provide insight into general biological mechanisms governing their collective dynamics.
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Affiliation(s)
- F Terragni
- Gregorio Millán Institute for Fluid Dynamics, Nanoscience and Industrial Mathematics, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Department of Mathematics, Universidad Carlos III de Madrid, 28911 Leganés, Spain
| | - W D Martinson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - M Carretero
- Gregorio Millán Institute for Fluid Dynamics, Nanoscience and Industrial Mathematics, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Department of Mathematics, Universidad Carlos III de Madrid, 28911 Leganés, Spain
| | - P K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - L L Bonilla
- Gregorio Millán Institute for Fluid Dynamics, Nanoscience and Industrial Mathematics, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Department of Mathematics, Universidad Carlos III de Madrid, 28911 Leganés, Spain
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9
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Romero-Arias JR, González-Castro CA, Ramírez-Santiago G. A multiscale model of the role of microenvironmental factors in cell segregation and heterogeneity in breast cancer development. PLoS Comput Biol 2023; 19:e1011673. [PMID: 37992135 DOI: 10.1371/journal.pcbi.1011673] [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: 05/18/2023] [Revised: 12/06/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023] Open
Abstract
We analyzed a quantitative multiscale model that describes the epigenetic dynamics during the growth and evolution of an avascular tumor. A gene regulatory network (GRN) formed by a set of ten genes that are believed to play an important role in breast cancer development was kinetically coupled to the microenvironmental agents: glucose, estrogens, and oxygen. The dynamics of spontaneous mutations was described by a Yule-Furry master equation whose solution represents the probability that a given cell in the tissue undergoes a certain number of mutations at a given time. We assumed that the mutation rate is modified by a spatial gradient of nutrients. The tumor mass was simulated by means of cellular automata supplemented with a set of reaction diffusion equations that described the transport of microenvironmental agents. By analyzing the epigenetic state space described by the GRN dynamics, we found three attractors that were identified with cellular epigenetic states: normal, precancer and cancer. For two-dimensional (2D) and three-dimensional (3D) tumors we calculated the spatial distribution of the following quantities: (i) number of mutations, (ii) mutation of each gene and, (iii) phenotypes. Using estrogen as the principal microenvironmental agent that regulates cell proliferation process, we obtained tumor shapes for different values of estrogen consumption and supply rates. It was found that he majority of mutations occurred in cells that were located close to the 2D tumor perimeter or close to the 3D tumor surface. Also, it was found that the occurrence of different phenotypes in the tumor are controlled by estrogen concentration levels since they can change the individual cell threshold and gene expression levels. All results were consistently observed for 2D and 3D tumors.
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Affiliation(s)
- J Roberto Romero-Arias
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
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10
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Uçar MC, Hannezo E, Tiilikainen E, Liaqat I, Jakobsson E, Nurmi H, Vaahtomeri K. Self-organized and directed branching results in optimal coverage in developing dermal lymphatic networks. Nat Commun 2023; 14:5878. [PMID: 37735168 PMCID: PMC10514270 DOI: 10.1038/s41467-023-41456-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/05/2023] [Indexed: 09/23/2023] Open
Abstract
Branching morphogenesis is a ubiquitous process that gives rise to high exchange surfaces in the vasculature and epithelial organs. Lymphatic capillaries form branched networks, which play a key role in the circulation of tissue fluid and immune cells. Although mouse models and correlative patient data indicate that the lymphatic capillary density directly correlates with functional output, i.e., tissue fluid drainage and trafficking efficiency of dendritic cells, the mechanisms ensuring efficient tissue coverage remain poorly understood. Here, we use the mouse ear pinna lymphatic vessel network as a model system and combine lineage-tracing, genetic perturbations, whole-organ reconstructions and theoretical modeling to show that the dermal lymphatic capillaries tile space in an optimal, space-filling manner. This coverage is achieved by two complementary mechanisms: initial tissue invasion provides a non-optimal global scaffold via self-organized branching morphogenesis, while VEGF-C dependent side-branching from existing capillaries rapidly optimizes local coverage by directionally targeting low-density regions. With these two ingredients, we show that a minimal biophysical model can reproduce quantitatively whole-network reconstructions, across development and perturbations. Our results show that lymphatic capillary networks can exploit local self-organizing mechanisms to achieve tissue-scale optimization.
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Affiliation(s)
- Mehmet Can Uçar
- Institute of Science and Technology Austria (IST Austria), Am Campus 1, 3400, Klosterneuburg, Austria
| | - Edouard Hannezo
- Institute of Science and Technology Austria (IST Austria), Am Campus 1, 3400, Klosterneuburg, Austria.
| | - Emmi Tiilikainen
- Translational Cancer Medicine Research Program, University of Helsinki, Biomedicum Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
| | - Inam Liaqat
- Translational Cancer Medicine Research Program, University of Helsinki, Biomedicum Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
| | - Emma Jakobsson
- Translational Cancer Medicine Research Program, University of Helsinki, Biomedicum Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
| | - Harri Nurmi
- Translational Cancer Medicine Research Program, University of Helsinki, Biomedicum Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Wihuri Research Institute, Biomedicum Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
| | - Kari Vaahtomeri
- Translational Cancer Medicine Research Program, University of Helsinki, Biomedicum Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland.
- Wihuri Research Institute, Biomedicum Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland.
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11
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Michael C, Pancaldi F, Britton S, Kim OV, Peshkova AD, Vo K, Xu Z, Litvinov RI, Weisel JW, Alber M. Combined computational modeling and experimental study of the biomechanical mechanisms of platelet-driven contraction of fibrin clots. Commun Biol 2023; 6:869. [PMID: 37620422 PMCID: PMC10449797 DOI: 10.1038/s42003-023-05240-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 08/10/2023] [Indexed: 08/26/2023] Open
Abstract
While blood clot formation has been relatively well studied, little is known about the mechanisms underlying the subsequent structural and mechanical clot remodeling called contraction or retraction. Impairment of the clot contraction process is associated with both life-threatening bleeding and thrombotic conditions, such as ischemic stroke, venous thromboembolism, and others. Recently, blood clot contraction was observed to be hindered in patients with COVID-19. A three-dimensional multiscale computational model is developed and used to quantify biomechanical mechanisms of the kinetics of clot contraction driven by platelet-fibrin pulling interactions. These results provide important biological insights into contraction of platelet filopodia, the mechanically active thin protrusions of the plasma membrane, described previously as performing mostly a sensory function. The biomechanical mechanisms and modeling approach described can potentially apply to studying other systems in which cells are embedded in a filamentous network and exert forces on the extracellular matrix modulated by the substrate stiffness.
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Affiliation(s)
- Christian Michael
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Francesco Pancaldi
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
| | - Samuel Britton
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
| | - Oleg V Kim
- Department of Cell and Developmental Biology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
- Department of Biomedical Engineering and Mechanics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Alina D Peshkova
- Department of Pharmacology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
| | - Khoi Vo
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA
| | - Zhiliang Xu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Rustem I Litvinov
- Department of Cell and Developmental Biology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
| | - John W Weisel
- Department of Cell and Developmental Biology, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA.
| | - Mark Alber
- Department of Mathematics, University of California Riverside, Riverside, CA, 92521, USA.
- Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, 92521, USA.
- Department of Bioengineering, University of California Riverside, Riverside, CA, 92521, USA.
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12
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Oguma T, Takigawa-Imamura H, Shinoda T, Ogura S, Uemura A, Miyata T, Maini PK, Miura T. Analyzing the effect of cell rearrangement on Delta-Notch pattern formation. Phys Rev E 2023; 107:064404. [PMID: 37464594 DOI: 10.1103/physreve.107.064404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 04/24/2023] [Indexed: 07/20/2023]
Abstract
The Delta-Notch system plays a vital role in many areas of biology and typically forms a salt and pepper pattern in which cells strongly expressing Delta and cells strongly expressing Notch are alternately aligned via lateral inhibition. In this study, we consider cell rearrangement events, such as cell mixing and proliferation, that alter the spatial structure itself and affect the pattern dynamics. We model cell rearrangement events by a Poisson process and analyze the model while preserving the discrete properties of the spatial structure. We investigate the effects of the intermittent perturbations arising from these cell rearrangement events on the discrete spatial structure itself in the context of pattern formation and by using an analytical approach, coupled with numerical simulation. We find that the homogeneous expression pattern is stabilized if the frequency of cell rearrangement events is sufficiently large. We analytically obtain the balanced frequencies of the cell rearrangement events where the decrease of the pattern amplitude, as a result of cell rearrangement, is balanced by the increase in amplitude due to the Delta-Notch interaction dynamics. Our framework, while applied here to the specific case of the Delta-Notch system, is applicable more widely to other pattern formation mechanisms.
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Affiliation(s)
- Toshiki Oguma
- Kyushu University Graduate School of Medical Sciences, Fukuoka 812-8582, Japan
| | | | - Tomoyasu Shinoda
- Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
| | - Shuntaro Ogura
- Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
| | - Akiyoshi Uemura
- Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan
| | - Takaki Miyata
- Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford OX2 6GG, United Kingdom
| | - Takashi Miura
- Kyushu University Graduate School of Medical Sciences, Fukuoka 812-8582, Japan
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13
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Oliveira RHM, Annex BH, Popel AS. Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.03.539270. [PMID: 37205581 PMCID: PMC10187169 DOI: 10.1101/2023.05.03.539270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between normoxia and hypoxia can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. We calibrate and fit the model parameters based on well-established modeling techniques. Our results indicate that the main pathways involved in the patterning of tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia affects how a reaction affects patterning. Interestingly, the interaction of receptors with Neuropilin1 is also relevant for cell patterning. Our simulations under different oxygen concentrations indicate time- and oxygen-availability-dependent responses for the two cells. Following simulations with various stimuli, our model suggests that factors such as period under hypoxia and oxygen availability must be considered for pattern control. This project provides insights into the signaling and patterning of endothelial cells under hypoxia, contributing to studies in the field.
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Affiliation(s)
- Rebeca Hannah M Oliveira
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| | - Brian H Annex
- Medical College of Georgia, Augusta University, Augusta, Georgia, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, Maryland, 21205, USA
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14
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Chen D, Forghany Z, Liu X, Wang H, Merks RMH, Baker DA. A new model of Notch signalling: Control of Notch receptor cis-inhibition via Notch ligand dimers. PLoS Comput Biol 2023; 19:e1010169. [PMID: 36668673 PMCID: PMC9891537 DOI: 10.1371/journal.pcbi.1010169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 02/01/2023] [Accepted: 12/30/2022] [Indexed: 01/22/2023] Open
Abstract
All tissue development and replenishment relies upon the breaking of symmetries leading to the morphological and operational differentiation of progenitor cells into more specialized cells. One of the main engines driving this process is the Notch signal transduction pathway, a ubiquitous signalling system found in the vast majority of metazoan cell types characterized to date. Broadly speaking, Notch receptor activity is governed by a balance between two processes: 1) intercellular Notch transactivation triggered via interactions between receptors and ligands expressed in neighbouring cells; 2) intracellular cis inhibition caused by ligands binding to receptors within the same cell. Additionally, recent reports have also unveiled evidence of cis activation. Whilst context-dependent Notch receptor clustering has been hypothesized, to date, Notch signalling has been assumed to involve an interplay between receptor and ligand monomers. In this study, we demonstrate biochemically, through a mutational analysis of DLL4, both in vitro and in tissue culture cells, that Notch ligands can efficiently self-associate. We found that the membrane proximal EGF-like repeat of DLL4 was necessary and sufficient to promote oligomerization/dimerization. Mechanistically, our experimental evidence supports the view that DLL4 ligand dimerization is specifically required for cis-inhibition of Notch receptor activity. To further substantiate these findings, we have adapted and extended existing ordinary differential equation-based models of Notch signalling to take account of the ligand dimerization-dependent cis-inhibition reported here. Our new model faithfully recapitulates our experimental data and improves predictions based upon published data. Collectively, our work favours a model in which net output following Notch receptor/ligand binding results from ligand monomer-driven Notch receptor transactivation (and cis activation) counterposed by ligand dimer-mediated cis-inhibition.
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Affiliation(s)
- Daipeng Chen
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
- Mathematical Institute, Leiden University, Leiden, The Netherlands
| | - Zary Forghany
- Leiden University Medical Center (LUMC), Department of Cell & Chemical Biology, Leiden, The Netherlands
| | - Xinxin Liu
- Leiden University Medical Center (LUMC), Department of Cell & Chemical Biology, Leiden, The Netherlands
| | - Haijiang Wang
- Leiden University Medical Center (LUMC), Department of Cell & Chemical Biology, Leiden, The Netherlands
- Department of General Surgery, The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Roeland M. H. Merks
- Mathematical Institute, Leiden University, Leiden, The Netherlands
- Institute of Biology Leiden, Leiden University, Leiden, The Netherlands
- * E-mail: (RMHM); (DAB)
| | - David A. Baker
- Leiden University Medical Center (LUMC), Department of Cell & Chemical Biology, Leiden, The Netherlands
- * E-mail: (RMHM); (DAB)
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15
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Kretschmer M, Mamistvalov R, Sprinzak D, Vollmar AM, Zahler S. Matrix stiffness regulates Notch signaling activity in endothelial cells. J Cell Sci 2023; 136:286810. [PMID: 36718783 DOI: 10.1242/jcs.260442] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/22/2022] [Indexed: 02/01/2023] Open
Abstract
Notch signaling is critical for many developmental and disease-related processes. It is widely accepted that Notch has a mechanotransduction module that regulates receptor cleavage. However, the role of biomechanical properties of the cellular environment in Notch signaling in general is still poorly understood. During angiogenesis, differentiation of endothelial cells into tip and stalk cells is regulated by Notch signaling, and remodeling of the extracellular matrix occurs. We investigated the influence of substrate stiffness on the Notch signaling pathway in endothelial cells. Using stiffness-tuned polydimethylsiloxane (PDMS) substrates, we show that activity of the Notch signaling pathway inversely correlates with a physiologically relevant range of substrate stiffness (i.e. increased Notch signaling activity on softer substrates). Trans-endocytosis of the Notch extracellular domain, but not the overall endocytosis, is regulated by substrate stiffness, and integrin cell-matrix connections are both stiffness dependent and influenced by Notch signaling. We conclude that mechanotransduction of Notch activation is modulated by substrate stiffness, highlighting the role of substrate rigidity as an important cue for signaling. This might have implications in pathological situations associated with stiffening of the extracellular matrix, such as tumor growth.
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Affiliation(s)
- Maibritt Kretschmer
- Department of Pharmacy, Pharmaceutical Biology, Ludwig-Maximilians-Universität München, Butenandtstraße 5-13, 81377 Munich, Germany
| | - Rose Mamistvalov
- The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - David Sprinzak
- The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Angelika M Vollmar
- Department of Pharmacy, Pharmaceutical Biology, Ludwig-Maximilians-Universität München, Butenandtstraße 5-13, 81377 Munich, Germany
| | - Stefan Zahler
- Department of Pharmacy, Pharmaceutical Biology, Ludwig-Maximilians-Universität München, Butenandtstraße 5-13, 81377 Munich, Germany
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16
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Apeldoorn C, Safaei S, Paton J, Maso Talou GD. Computational models for generating microvascular structures: Investigations beyond medical imaging resolution. WIREs Mech Dis 2023; 15:e1579. [PMID: 35880683 PMCID: PMC10077909 DOI: 10.1002/wsbm.1579] [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: 12/15/2021] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 01/31/2023]
Abstract
Angiogenesis, arteriogenesis, and pruning are revascularization processes essential to our natural vascular development and adaptation, as well as central players in the onset and development of pathologies such as tumoral growth and stroke recovery. Computational modeling allows for repeatable experimentation and exploration of these complex biological processes. In this review, we provide an introduction to the biological understanding of the vascular adaptation processes of sprouting angiogenesis, intussusceptive angiogenesis, anastomosis, pruning, and arteriogenesis, discussing some of the more significant contributions made to the computational modeling of these processes. Each computational model represents a theoretical framework for how biology functions, and with rises in computing power and study of the problem these frameworks become more accurate and complete. We highlight physiological, pathological, and technological applications that can be benefit from the advances performed by these models, and we also identify which elements of the biology are underexplored in the current state-of-the-art computational models. This article is categorized under: Cancer > Computational Models Cardiovascular Diseases > Computational Models.
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Affiliation(s)
- Cameron Apeldoorn
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Soroush Safaei
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Julian Paton
- Cardiovascular Autonomic Research Cluster, Department of Physiology, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Gonzalo D Maso Talou
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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17
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Phillips CM, Lima EABF, Gadde M, Jarrett AM, Rylander MN, Yankeelov TE. Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis. PLoS Comput Biol 2023; 19:e1009499. [PMID: 36652468 PMCID: PMC9886306 DOI: 10.1371/journal.pcbi.1009499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/30/2023] [Accepted: 12/13/2022] [Indexed: 01/19/2023] Open
Abstract
The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein concentration data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ± 3.6% using one and four calibrated parameters, respectively. We then assess the model's ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. In this study, we have rigorously calibrated a mechanism-based, multiscale, mathematical model of angiogenic sprouting to multimodal experimental data to make specific, testable predictions.
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Affiliation(s)
- Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, Texas, United States of America
| | - Manasa Gadde
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas, United States of America
| | - Marissa Nichole Rylander
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Oncology, The University of Texas at Austin, Austin, Texas, United States of America
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Imaging Physics, The University of Texas at Austin, MD Anderson Cancer Center, Houston, Texas, United States of America
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18
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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: 0] [Impact Index Per Article: 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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19
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Naiche LA, Villa SR, Kitajewski JK. Endothelial Cell Fate Determination: A Top Notch Job in Vascular Decision-Making. Cold Spring Harb Perspect Med 2022; 12:a041183. [PMID: 35288401 PMCID: PMC9619357 DOI: 10.1101/cshperspect.a041183] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
As vascular networks form, endothelial cells (ECs) undergo cell fate decisions that determine whether they become tip or stalk cells of the developing vascular plexus or mature into arterial, venous, or lymphatic endothelium. EC fate decisions are coordinated with neighboring cells to initiate sprouting, maintain endothelial barrier, or ensure appropriate specialization of vessels. We describe mechanisms that control EC fate at specific steps in these processes, with an emphasis on the role of the Notch signaling pathway. Specific EC fate determination steps that are highlighted are tip/stalk selection during sprouting angiogenesis, venous-arterial specification, arteriogenesis, lymphatic vessel specification, and lymphatic valve formation.
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Affiliation(s)
- L A Naiche
- Department of Physiology and Biophysics, University of Illinois Chicago, Chicago, Illinois 60612, USA
| | - Stephanie R Villa
- Department of Physiology and Biophysics, University of Illinois Chicago, Chicago, Illinois 60612, USA
| | - Jan K Kitajewski
- Department of Physiology and Biophysics, University of Illinois Chicago, Chicago, Illinois 60612, USA
- University of Illinois Cancer Center, Chicago, Illinois 60612, USA
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20
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Gorick CM, Saucerman JJ, Price RJ. Computational model of brain endothelial cell signaling pathways predicts therapeutic targets for cerebral pathologies. J Mol Cell Cardiol 2022; 164:17-28. [PMID: 34798125 PMCID: PMC8958390 DOI: 10.1016/j.yjmcc.2021.11.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 10/13/2021] [Accepted: 11/13/2021] [Indexed: 11/25/2022]
Abstract
Brain endothelial cells serve many critical homeostatic functions. In addition to sensing and regulating blood flow, they maintain blood-brain barrier function, including precise control of nutrient exchange and efflux of xenobiotics. Many signaling pathways in brain endothelial cells have been implicated in both health and disease; however, our understanding of how these signaling pathways functionally integrate is limited. A model capable of integrating these signaling pathways could both advance our understanding of brain endothelial cell signaling networks and potentially identify promising molecular targets for endothelial cell-based drug or gene therapies. To this end, we developed a large-scale computational model, wherein brain endothelial cell signaling pathways were reconstructed from the literature and converted into a network of logic-based differential equations. The model integrates 63 nodes (including proteins, mRNA, small molecules, and cell phenotypes) and 82 reactions connecting these nodes. Specifically, our model combines signaling pathways relating to VEGF-A, BDNF, NGF, and Wnt signaling, in addition to incorporating pathways relating to focused ultrasound as a therapeutic delivery tool. To validate the model, independently established relationships between selected inputs and outputs were simulated, with the model yielding correct predictions 73% of the time. We identified influential and sensitive nodes under different physiological or pathological contexts, including altered brain endothelial cell conditions during glioma, Alzheimer's disease, and ischemic stroke. Nodes with the greatest influence over combinations of desired model outputs were identified as potential druggable targets for these disease conditions. For example, the model predicts therapeutic benefits from inhibiting AKT, Hif-1α, or cathepsin D in the context of glioma - each of which are currently being studied in clinical or pre-clinical trials. Notably, the model also permits testing multiple combinations of node alterations for their effects on the network and the desired outputs (such as inhibiting AKT and overexpressing the P75 neurotrophin receptor simultaneously in the context of glioma), allowing for the prediction of optimal combination therapies. In all, our approach integrates results from over 100 past studies into a coherent and powerful model, capable of both revealing network interactions unapparent from studying any one pathway in isolation and predicting therapeutic targets for treating devastating brain pathologies.
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Affiliation(s)
- Catherine M. Gorick
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA,Corresponding authors at: Department of Biomedical Engineering, Box 800759, Health System, University of Virginia, Charlottesville, VA 22908, USA. (J.J. Saucerman), (R.J. Price)
| | - Richard J. Price
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA,Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA,Corresponding authors at: Department of Biomedical Engineering, Box 800759, Health System, University of Virginia, Charlottesville, VA 22908, USA. (J.J. Saucerman), (R.J. Price)
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21
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Jafari Nivlouei S, Soltani M, Shirani E, Salimpour MR, Travasso R, Carvalho J. A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach. Cell Prolif 2022; 55:e13187. [PMID: 35132721 PMCID: PMC8891571 DOI: 10.1111/cpr.13187] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/09/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVES Computational modeling of biological systems is a powerful tool to clarify diverse processes contributing to cancer. The aim is to clarify the complex biochemical and mechanical interactions between cells, the relevance of intracellular signaling pathways in tumor progression and related events to the cancer treatments, which are largely ignored in previous studies. MATERIALS AND METHODS A three-dimensional multiscale cell-based model is developed, covering multiple time and spatial scales, including intracellular, cellular, and extracellular processes. The model generates a realistic representation of the processes involved from an implementation of the signaling transduction network. RESULTS Considering a benign tumor development, results are in good agreement with the experimental ones, which identify three different phases in tumor growth. Simulating tumor vascular growth, results predict a highly vascularized tumor morphology in a lobulated form, a consequence of cells' motile behavior. A novel systematic study of chemotherapy intervention, in combination with targeted therapy, is presented to address the capability of the model to evaluate typical clinical protocols. The model also performs a dose comparison study in order to optimize treatment efficacy and surveys the effect of chemotherapy initiation delays and different regimens. CONCLUSIONS Results not only provide detailed insights into tumor progression, but also support suggestions for clinical implementation. This is a major step toward the goal of predicting the effects of not only traditional chemotherapy but also tumor-targeted therapies.
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Affiliation(s)
- Sahar Jafari Nivlouei
- Department of Mechanical Engineering, Isfahan University of Technology, Isafahan, Iran.,Department of Physics, CFisUC, University of Coimbra, Coimbra, Portugal
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.,Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada.,Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran.,Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Ebrahim Shirani
- Department of Mechanical Engineering, Isfahan University of Technology, Isafahan, Iran.,Department of Mechanical Engineering, Foolad Institute of Technology, Fooladshahr, Iran
| | | | - Rui Travasso
- Department of Physics, CFisUC, University of Coimbra, Coimbra, Portugal
| | - João Carvalho
- Department of Physics, CFisUC, University of Coimbra, Coimbra, Portugal
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22
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Chesnais F, Hue J, Roy E, Branco M, Stokes R, Pellon A, Le Caillec J, Elbahtety E, Battilocchi M, Danovi D, Veschini L. High content Image Analysis to study phenotypic heterogeneity in endothelial cell monolayers. J Cell Sci 2022; 135:273879. [PMID: 34982151 DOI: 10.1242/jcs.259104] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 12/15/2021] [Indexed: 11/20/2022] Open
Abstract
Endothelial cells (EC) are heterogeneous across and within tissues, reflecting distinct, specialised functions. EC heterogeneity has been proposed to underpin EC plasticity independently from vessel microenvironments. However, heterogeneity driven by contact-dependent or short-range cell-cell crosstalk cannot be evaluated with single cell transcriptomic approaches as spatial and contextual information is lost. Nonetheless, quantification of EC heterogeneity and understanding of its molecular drivers is key to developing novel therapeutics for cancer, cardiovascular diseases and for revascularisation in regenerative medicine. Here, we developed an EC profiling tool (ECPT) to examine individual cells within intact monolayers. We used ECPT to characterise different phenotypes in arterial, venous and microvascular EC populations. In line with other studies, we measured heterogeneity in terms of cell cycle, proliferation, and junction organisation. ECPT uncovered a previously under-appreciated single-cell heterogeneity in NOTCH activation. We correlated cell proliferation with different NOTCH activation states at the single cell and population levels. The positional and relational information extracted with our novel approach is key to elucidating the molecular mechanisms underpinning EC heterogeneity.
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Affiliation(s)
- Francois Chesnais
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Jonas Hue
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Errin Roy
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Marco Branco
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Ruby Stokes
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Aize Pellon
- Centre for host-microbiome interactions, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Juliette Le Caillec
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Eyad Elbahtety
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Matteo Battilocchi
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Davide Danovi
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK.,bit.bio, Babraham Research Campus, The Dorothy Hodgkin Building, Cambridge CB22 3FH, UK
| | - Lorenzo Veschini
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
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23
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Zhang Y, Wang H, Oliveira RHM, Zhao C, Popel AS. Systems biology of angiogenesis signaling: Computational models and omics. WIREs Mech Dis 2021; 14:e1550. [PMID: 34970866 PMCID: PMC9243197 DOI: 10.1002/wsbm.1550] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 01/10/2023]
Abstract
Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases. This article is categorized under:Cardiovascular Diseases > Computational Models Cancer > Computational Models
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Affiliation(s)
- Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Rebeca Hannah M Oliveira
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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24
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Debir B, Meaney C, Kohandel M, Unlu MB. The role of calcium oscillations in the phenotype selection in endothelial cells. Sci Rep 2021; 11:23781. [PMID: 34893636 PMCID: PMC8664853 DOI: 10.1038/s41598-021-02720-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/07/2021] [Indexed: 11/10/2022] Open
Abstract
Angiogenesis is an important process in the formation and maintenance of tissues which is driven by a complex system of intracellular and intercellular signaling mechanisms. Endothelial cells taking part in early angiogenesis must select their phenotype as either a tip cells (leading, migratory) or a stalk cells (following). Recent experiments have demonstrated that rapid calcium oscillations within active cells characterize this phenotype selection process and that these oscillations play a necessary role in governing phenotype selection and eventual vessel architecture. In this work, we develop a mathematical model capable of describing these oscillations and their role in phenotype selection then use it to improve our understanding of the biological mechanisms at play. We developed a model based on two previously published and experimentally validated mathematical models of calcium and angiogenesis then use our resulting model to simulate various multi-cell scenarios. We are able to capture essential calcium oscillation dynamics and intercellular communication between neighboring cells. The results of our model show that although the late DLL4 (a transmembrane protein that activates Notch pathway) levels of a cell are connected with its initial IP3 (Inositol 1,4,5-trisphosphate) level, cell-to-cell communication determines its eventual phenotype.
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Affiliation(s)
- Birses Debir
- Department of Physics, Bogazici University, 34342, Istanbul, Turkey.
| | - Cameron Meaney
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
| | - M Burcin Unlu
- Department of Physics, Bogazici University, 34342, Istanbul, 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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25
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Hormuth DA, Phillips CM, Wu C, Lima EABF, Lorenzo G, Jha PK, Jarrett AM, Oden JT, Yankeelov TE. Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers (Basel) 2021; 13:3008. [PMID: 34208448 PMCID: PMC8234316 DOI: 10.3390/cancers13123008] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/07/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023] Open
Abstract
Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
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Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Prashant K. Jha
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Angela M. Jarrett
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - J. Tinsley Oden
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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26
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Nardini JT, Stolz BJ, Flores KB, Harrington HA, Byrne HM. Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis. PLoS Comput Biol 2021; 17:e1009094. [PMID: 34181657 PMCID: PMC8270459 DOI: 10.1371/journal.pcbi.1009094] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 07/09/2021] [Accepted: 05/18/2021] [Indexed: 12/27/2022] Open
Abstract
Angiogenesis is the process by which blood vessels form from pre-existing vessels. It plays a key role in many biological processes, including embryonic development and wound healing, and contributes to many diseases including cancer and rheumatoid arthritis. The structure of the resulting vessel networks determines their ability to deliver nutrients and remove waste products from biological tissues. Here we simulate the Anderson-Chaplain model of angiogenesis at different parameter values and quantify the vessel architectures of the resulting synthetic data. Specifically, we propose a topological data analysis (TDA) pipeline for systematic analysis of the model. TDA is a vibrant and relatively new field of computational mathematics for studying the shape of data. We compute topological and standard descriptors of model simulations generated by different parameter values. We show that TDA of model simulation data stratifies parameter space into regions with similar vessel morphology. The methodologies proposed here are widely applicable to other synthetic and experimental data including wound healing, development, and plant biology.
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Affiliation(s)
- John T. Nardini
- Department of Mathematics, North Carolina State University, Raleigh, North Carolina, United States of America
| | | | - Kevin B. Flores
- Department of Mathematics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Heather A. Harrington
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Helen M. Byrne
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
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