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Jayathungage Don TD, Safaei S, Maso Talou GD, Russell PS, Phillips ARJ, Reynolds HM. Computational fluid dynamic modeling of the lymphatic system: a review of existing models and future directions. Biomech Model Mechanobiol 2024; 23:3-22. [PMID: 37902894 PMCID: PMC10901951 DOI: 10.1007/s10237-023-01780-9] [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: 08/22/2023] [Accepted: 10/02/2023] [Indexed: 11/01/2023]
Abstract
Historically, research into the lymphatic system has been overlooked due to both a lack of knowledge and limited recognition of its importance. In the last decade however, lymphatic research has gained substantial momentum and has included the development of a variety of computational models to aid understanding of this complex system. This article reviews existing computational fluid dynamic models of the lymphatics covering each structural component including the initial lymphatics, pre-collecting and collecting vessels, and lymph nodes. This is followed by a summary of limitations and gaps in existing computational models and reasons that development in this field has been hindered to date. Over the next decade, efforts to further characterize lymphatic anatomy and physiology are anticipated to provide key data to further inform and validate lymphatic fluid dynamic models. Development of more comprehensive multiscale- and multi-physics computational models has the potential to significantly enhance the understanding of lymphatic function in both health and disease.
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Affiliation(s)
| | - Soroush Safaei
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Gonzalo D Maso Talou
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Peter S Russell
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
- Surgical and Translational Research Centre, Department of Surgery, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Anthony R J Phillips
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
- Surgical and Translational Research Centre, Department of Surgery, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
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2
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Thelen F, Wissmann S, Ruef N, Stein JV. The Tec Kinase Itk Integrates Naïve T Cell Migration and In Vivo Homeostasis. Front Immunol 2021; 12:716405. [PMID: 34566971 PMCID: PMC8458560 DOI: 10.3389/fimmu.2021.716405] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/19/2021] [Indexed: 11/13/2022] Open
Abstract
Naïve T cells (TN) constitutively recirculate through secondary lymphatic organs (SLOs), where they scan dendritic cells (DCs) for cognate peptide-loaded major histocompatibility complexes (pMHC). Continuous trafficking between SLOs not only enables rapid clonal selection but also ensures TN homeostasis by providing access to prosurvival signals from TCR, IL-7R, and the chemokine receptor CCR7. Inside the lymphoid tissue, CCR7-mediated TN motility is mainly driven by the Rac activator DOCK2, with a separate contribution by a phosphoinositide-3-kinase γ (PI3Kγ)-dependent pathway. Tec tyrosine kinases and the Rac activator Tiam1 constitute prominent downstream effectors of PI3K signaling. Yet, the precise role of Tec kinase versus Tiam1 signaling during CCR7-mediated TN migration and homeostasis remains incompletely understood. Here, we examined the function of the Tec family member interleukin-2-inducible T-cell kinase (Itk) and Tiam1 during TN migration in vitro and in vivo using intravital microscopy. Itk deficiency caused a mild decrease in CCR7-triggered TN migration, mirroring observations made with PI3Kγ;-/- T cells, while lack of Tiam1 did not affect TN motility. In silico modeling suggested that reduced migration in the absence of Itk does not result in a substantial decrease in the frequency of TN encounters with DCs within the lymphoid tissue. In contrast, Itk was important to maintain in vivo homeostasis of CD4+ TN, also in MHCII-deficient hosts. Taken together, our data suggest that Itk contributes to TN migration and survival by integrating chemokine receptor and TCR signaling pathways.
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Affiliation(s)
- Flavian Thelen
- Department of Medical Oncology and Hematology, University of Zürich and University Hospital Zürich, Zürich, Switzerland
| | - Stefanie Wissmann
- Department of Oncology, Microbiology and Immunology, University of Fribourg, Fribourg, Switzerland
| | - Nora Ruef
- Department of Oncology, Microbiology and Immunology, University of Fribourg, Fribourg, Switzerland
| | - Jens V Stein
- Department of Oncology, Microbiology and Immunology, University of Fribourg, Fribourg, Switzerland
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3
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Brown LV, Gaffney EA, Wagg J, Coles MC. An in silico model of cytotoxic T-lymphocyte activation in the lymph node following short peptide vaccination. J R Soc Interface 2019. [PMID: 29540543 DOI: 10.1098/rsif.2018.0041] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Tumour immunotherapy is dependent upon activation and expansion of tumour-targetting immune cells, known as cytotoxic T-lymphocytes (CTLs). Cancer vaccines developed in the past have had limited success and the mechanisms resulting in failure are not well characterized. To elucidate these mechanisms, we developed a human-parametrized, in silico, agent-based model of vaccination-driven CTL activation within a clinical short-peptide vaccination context. The simulations predict a sharp transition in the probability of CTL activation, which occurs with variation in the separation rate (or off-rate) of tumour-specific immune response-inducing peptides (cognate antigen) from the major histocompatibility class I (MHC-I) receptors of dendritic cells (DCs) originally at the vaccination site. For peptides with MHC-I off-rates beyond this transition, it is predicted that no vaccination strategy will lead to successful expansion of CTLs. For slower off-rates, below the transition, the probability of CTL activation becomes sensitive to the numbers of DCs and T cells that interact subsequent to DC migration to the draining lymph node of the vaccination site. Thus, the off-rate is a key determinant of vaccine design.
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Affiliation(s)
- Liam V Brown
- Wolfson Centre For Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Eamonn A Gaffney
- Wolfson Centre For Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Jonathan Wagg
- Clinical Pharmacology, Roche Innovation Center Basel, Basel, Switzerland
| | - Mark C Coles
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
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4
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Moses ME, Cannon JL, Gordon DM, Forrest S. Distributed Adaptive Search in T Cells: Lessons From Ants. Front Immunol 2019; 10:1357. [PMID: 31263465 PMCID: PMC6585175 DOI: 10.3389/fimmu.2019.01357] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 05/29/2019] [Indexed: 11/13/2022] Open
Abstract
There are striking similarities between the strategies ant colonies use to forage for food and immune systems use to search for pathogens. Searchers (ants and cells) use the appropriate combination of random and directed motion, direct and indirect agent-agent interactions, and traversal of physical structures to solve search problems in a variety of environments. An effective immune response requires immune cells to search efficiently and effectively for diverse types of pathogens in different tissues and organs, just as different species of ants have evolved diverse search strategies to forage effectively for a variety of resources in a variety of habitats. Successful T cell search is required to initiate the adaptive immune response in lymph nodes and to eradicate pathogens at sites of infection in peripheral tissue. Ant search strategies suggest novel predictions about T cell search. In both systems, the distribution of targets in time and space determines the most effective search strategy. We hypothesize that the ability of searchers to sense and adapt to dynamic targets and environmental conditions enhances search effectiveness through adjustments to movement and communication patterns. We also suggest that random motion is a more important component of search strategies than is generally recognized. The behavior we observe in ants reveals general design principles and constraints that govern distributed adaptive search in a wide variety of complex systems, particularly the immune system.
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Affiliation(s)
- Melanie E Moses
- Moses Biological Computation Laboratory, Department of Computer Science, University of New Mexico, Albuquerque, NM, United States.,Biology Department, University of New Mexico, Albuquerque, NM, United States.,Santa Fe Institute, Santa Fe, NM, United States
| | - Judy L Cannon
- The Cannon Laboratory, Department of Molecular Genetics & Microbiology, University of New Mexico School of Medicine, Albuquerque, NM, United States.,Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM, United States.,Autophagy, Inflammation, and Metabolism Center of Biomedical Research Excellence, University of New Mexico School of Medicine, Albuquerque, NM, United States
| | - Deborah M Gordon
- Santa Fe Institute, Santa Fe, NM, United States.,Department of Biology, Stanford University, Stanford, CA, United States
| | - Stephanie Forrest
- Santa Fe Institute, Santa Fe, NM, United States.,Biodesign Institute and School for Computing, Informatics, and Decision Sciences Engineering, Arizona State University, Tempe, AZ, United States
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Azarov I, Peskov K, Helmlinger G, Kosinsky Y. Role of T Cell-To-Dendritic Cell Chemoattraction in T Cell Priming Initiation in the Lymph Node: An Agent-Based Modeling Study. Front Immunol 2019; 10:1289. [PMID: 31244840 PMCID: PMC6579912 DOI: 10.3389/fimmu.2019.01289] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 05/21/2019] [Indexed: 01/14/2023] Open
Abstract
The adaptive immune response is initiated in lymph nodes by contact between antigen-bearing dendritic cells (DCs) and antigen-specific T cells. A selected number of naïve T cells that recognize a specific antigen may proliferate into expanded clones, differentiate, and acquire an effector phenotype. Despite growing experimental knowledge, certain mechanistic aspects of T cell behavior in lymph nodes remain poorly understood. Computational modeling approaches may help in addressing such gaps. Here we introduce an agent-based model describing T cell movements and their interactions with DCs, leading to activation and expansion of cognate T cell clones, in a two-dimensional representation of the lymph node paracortex. The primary objective was to test the putative role of T cell chemotaxis toward DCs, and quantitatively assess the impact of chemotaxis with respect to T cell priming efficacy. Firstly, we evaluated whether chemotaxis of naïve T cells toward a nearest DC may accelerate the scanning process, by quantifying, through simulations, the number of unique T cell—DC contact events. We demonstrate that, in the presence of naïve T cell-to-DC chemoattraction, a higher total number of contacts occurs, as compared to a T cell random walk scenario. However, the forming swarm of naïve T cells, as these cells get attracted to the neighborhood of a DC, may then physically restrict access of additional T cells to the DC, leading to an actual decrease in the cumulative number of unique contacts between naïve T cells and DCs. Secondly, we investigated the potential role of chemotaxis in maintaining cognate T cell clone expansion. The time course of cognate T cells number in the system was used as a quantitative characteristic of the expansion. Model-based simulations indicate that inclusion of chemotaxis, which is selective for already activated (but not naïve) antigen-specific T cells, may strongly accelerate the time of immune response occurrence, which subsequently increases the overall amplitude of the T cell clone expansion process.
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Affiliation(s)
| | - Kirill Peskov
- M&S Decisions, Moscow, Russia.,Computational Oncology Group, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, Russia
| | - Gabriel Helmlinger
- Clinical Pharmacology & Safety Sciences, AstraZeneca, Boston, MA, United States
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Shinde SB, Kurhekar MP. Review of the systems biology of the immune system using agent-based models. IET Syst Biol 2019; 12:83-92. [PMID: 29745901 DOI: 10.1049/iet-syb.2017.0073] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.
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Affiliation(s)
- Snehal B Shinde
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India.
| | - Manish P Kurhekar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
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Norton KA, Gong C, Jamalian S, Popel AS. Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment. Processes (Basel) 2019; 7:37. [PMID: 30701168 PMCID: PMC6349239 DOI: 10.3390/pr7010037] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.
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Affiliation(s)
- Kerri-Ann Norton
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Computer Science Program, Department of Science, Mathematics, and Computing, Bard College, Annandale-on-Hudson, NY 12504, USA
| | - Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Samira Jamalian
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
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Brown LV, Gaffney EA, Wagg J, Coles MC. Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development. Clin Exp Immunol 2018; 193:284-292. [PMID: 30240512 PMCID: PMC6150250 DOI: 10.1111/cei.13182] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2018] [Indexed: 12/15/2022] Open
Abstract
The application of in-silico modelling is beginning to emerge as a key methodology to advance our understanding of mechanisms of disease pathophysiology and related drug action, and in the design of experimental medicine and clinical studies. From this perspective, we will present a non-technical discussion of a small number of recent and historical applications of mathematical, statistical and computational modelling to clinical and experimental immunology. We focus specifically upon mechanistic questions relating to human viral infection, tumour growth and metastasis and T cell activation. These exemplar applications highlight the potential of this approach to impact upon human immunology informed by ever-expanding experimental, clinical and 'omics' data. Despite the capacity of mechanistic modelling to accelerate therapeutic discovery and development and to de-risk clinical trial design, it is not utilized widely across the field. We outline ongoing challenges facing the integration of mechanistic modelling with experimental and clinical immunology, and suggest how these may be overcome. Advances in key technologies, including multi-scale modelling, machine learning and the wealth of 'omics' data sets, coupled with advancements in computational capacity, are providing the basis for mechanistic modelling to impact on immunotherapeutic discovery and development during the next decade.
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Affiliation(s)
- L. V. Brown
- Wolfson Centre for Mathematical BiologyMathematical InstituteUniversity of OxfordOxfordUK
| | - E. A. Gaffney
- Wolfson Centre for Mathematical BiologyMathematical InstituteUniversity of OxfordOxfordUK
| | - J. Wagg
- Pharmaceutical Sciences, Clinical PharmacologyRoche Innovation CenterBaselSwitzerland
| | - M. C. Coles
- Kennedy Institute of RheumatologyUniversity of OxfordOxfordUK
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Mathematical Models for Immunology: Current State of the Art and Future Research Directions. Bull Math Biol 2016; 78:2091-2134. [PMID: 27714570 PMCID: PMC5069344 DOI: 10.1007/s11538-016-0214-9] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 09/26/2016] [Indexed: 01/01/2023]
Abstract
The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.
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