1
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Amiri F, Benson JD. A three-dimensional lattice-free agent-based model of intracellular ice formation and propagation and intercellular mechanics in liver tissues. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231337. [PMID: 39021779 PMCID: PMC11252675 DOI: 10.1098/rsos.231337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/22/2024] [Indexed: 07/20/2024]
Abstract
A successful cryopreservation of tissues and organs is crucial for medical procedures and drug development acceleration. However, there are only a few instances of successful tissue cryopreservation. One of the main obstacles to successful cryopreservation is intracellular ice damage. Understanding how ice spreads can accelerate protocol development and enable model-based decision-making. Previous models of intracellular ice formation in individual cells have been extended to one-cell-wide arrays to establish the theory of intercellular ice propagation in tissues. The current lattice-based ice propagation models do not account for intercellular forces resulting from cell solidification, which could lead to mechanical disruption of tissue structures during freezing. Moreover, these models have not been expanded to include more realistic tissue architectures. In this article, we discuss the development and validation of a stochastic model for the formation and propagation of ice in small tissues using lattice-free agent-based model. We have improved the existing model by incorporating the mechanical effects of water crystallization within cells. Using information from previous research, we have also created a new model that accounts for ice growth in tissue slabs, spheroids and hepatocyte discs. Our model demonstrates that individual cell freezing can have mechanical consequences and is consistent with earlier findings.
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Affiliation(s)
- Fatemeh Amiri
- Department of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - James D. Benson
- Department of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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2
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Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024; 16:243-260. [PMID: 38374297 DOI: 10.1007/s12539-024-00603-4] [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: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhenqi Xiong
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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3
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Hickey JW, Agmon E, Horowitz N, Tan TK, Lamore M, Sunwoo JB, Covert MW, Nolan GP. Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy. Cell Syst 2024; 15:322-338.e5. [PMID: 38636457 PMCID: PMC11030795 DOI: 10.1016/j.cels.2024.03.004] [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: 12/14/2022] [Revised: 03/07/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Center for Cell Analysis and Modeling, University of Connecticut Health, Farmington, CT 06032, USA
| | - Nina Horowitz
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Tze-Kai Tan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Lamore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - John B Sunwoo
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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4
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Hickey JW, Agmon E, Horowitz N, Lamore M, Sunwoo J, Covert M, Nolan GP. Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Transformation as a Critical Component of Therapeutic T Cell Efficacy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570168. [PMID: 38106218 PMCID: PMC10723382 DOI: 10.1101/2023.12.06.570168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here we integrated CODEX multiplexed tissue imaging with multiscale modeling software, to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface.
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Affiliation(s)
- John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Nina Horowitz
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Matthew Lamore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - John Sunwoo
- Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - Markus Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
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5
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Lee ND, Kaveh K, Bozic I. Clonal interactions in cancer: integrating quantitative models with experimental and clinical data. Semin Cancer Biol 2023; 92:61-73. [PMID: 37023969 DOI: 10.1016/j.semcancer.2023.04.002] [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: 11/30/2022] [Revised: 02/16/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023]
Abstract
Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.
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Affiliation(s)
- Nathan D Lee
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Kamran Kaveh
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America; Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
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6
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Millar-Wilson A, Ward Ó, Duffy E, Hardiman G. Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies. iScience 2022; 25:105421. [DOI: 10.1016/j.isci.2022.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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7
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Lagzian M, Ehsan Razavi S, Goharimanesh M. Investigation on tumor cells growth by Taguchi method. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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8
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Prybutok AN, Yu JS, Leonard JN, Bagheri N. Mapping CAR T-Cell Design Space Using Agent-Based Models. Front Mol Biosci 2022; 9:849363. [PMID: 35903149 PMCID: PMC9315201 DOI: 10.3389/fmolb.2022.849363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 05/23/2022] [Indexed: 12/15/2022] Open
Abstract
Chimeric antigen receptor (CAR) T-cell therapy shows promise for treating liquid cancers and increasingly for solid tumors as well. While potential design strategies exist to address translational challenges, including the lack of unique tumor antigens and the presence of an immunosuppressive tumor microenvironment, testing all possible design choices in vitro and in vivo is prohibitively expensive, time consuming, and laborious. To address this gap, we extended the modeling framework ARCADE (Agent-based Representation of Cells And Dynamic Environments) to include CAR T-cell agents (CAR T-cell ARCADE, or CARCADE). We conducted in silico experiments to investigate how clinically relevant design choices and inherent tumor features—CAR T-cell dose, CD4+:CD8+ CAR T-cell ratio, CAR-antigen affinity, cancer and healthy cell antigen expression—individually and collectively impact treatment outcomes. Our analysis revealed that tuning CAR affinity modulates IL-2 production by balancing CAR T-cell proliferation and effector function. It also identified a novel multi-feature tuned treatment strategy for balancing selectivity and efficacy and provided insights into how spatial effects can impact relative treatment performance in different contexts. CARCADE facilitates deeper biological understanding of treatment design and could ultimately enable identification of promising treatment strategies to accelerate solid tumor CAR T-cell design-build-test cycles.
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Affiliation(s)
- Alexis N. Prybutok
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Jessica S. Yu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
- Department of Biology, University of Washington, Seattle, WA, United States
| | - Joshua N. Leonard
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
- Center for Synthetic Biology, Northwestern University, Evanston, IL, United States
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, United States
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, IL, United States
- *Correspondence: Neda Bagheri, ; Joshua N. Leonard,
| | - Neda Bagheri
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
- Department of Biology, University of Washington, Seattle, WA, United States
- Center for Synthetic Biology, Northwestern University, Evanston, IL, United States
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, United States
- Department of Chemical Engineering, University of Washington, Seattle, WA, United States
- *Correspondence: Neda Bagheri, ; Joshua N. Leonard,
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9
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Amereh M, Seyfoori A, Akbari M. In Vitro Brain Organoids and Computational Models to Study Cell Death in Brain Diseases. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2515:281-296. [PMID: 35776358 DOI: 10.1007/978-1-0716-2409-8_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Understanding the mechanisms underlying the formation and progression of brain diseases is challenging due to the vast variety of involved genetic/epigenetic factors and the complexity of the environment of the brain. Current preclinical monolayer culture systems fail to faithfully recapitulate the in vivo complexities of the brain. Organoids are three-dimensional (3D) culture systems that mimic much of the complexities of the brain including cell-cell and cell-matrix interactions. Complemented with a theoretical framework to model the dynamic interactions between different components of the brain, organoids can be used as a potential tool for studying disease progression, transport of therapeutic agents in tissues, drug screening, and toxicity analysis. In this chapter, we first report on the fabrication and use of a novel self-filling microwell arrays (SFMWs) platform that is self-filling and enables the formation of organoids with uniform size distributions. Next, we will introduce a mathematical framework that predicts the organoid growth, cell death, and the therapeutic responses of the organoids to different therapeutic agents. Through systematic investigations, the computational model can identify shortcomings of in vitro assays and reduce the time and effort required to improve preclinical tumor models' design. Lastly, the mathematical model provides new testable hypotheses and encourages mathematically driven experiments.
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Affiliation(s)
- Meitham Amereh
- Laboratory for Innovations in Microengineering (LiME), University of Victoria, Victoria, BC, Canada.,Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada
| | - Amir Seyfoori
- Laboratory for Innovations in Microengineering (LiME), University of Victoria, Victoria, BC, Canada.,Center for Advanced Materials and Related Technologies (CAMTEC), University of Victoria, Victoria, BC, Canada
| | - Mohsen Akbari
- Laboratory for Innovations in Microengineering (LiME), University of Victoria, Victoria, BC, Canada. .,Center for Advanced Materials and Related Technologies (CAMTEC), University of Victoria, Victoria, BC, Canada.
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10
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Corti A, Colombo M, Migliavacca F, Rodriguez Matas JF, Casarin S, Chiastra C. Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models. Front Bioeng Biotechnol 2021; 9:744560. [PMID: 34796166 PMCID: PMC8593007 DOI: 10.3389/fbioe.2021.744560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/04/2021] [Indexed: 12/20/2022] Open
Abstract
The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.
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Affiliation(s)
- Anna Corti
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Monika Colombo
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Jose Felix Rodriguez Matas
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Stefano Casarin
- Department of Surgery, Houston Methodist Hospital, Houston, TX, United States.,Center for Computational Surgery, Houston Methodist Research Institute, Houston, TX, United States.,Houston Methodist Academic Institute, Houston, TX, United States
| | - Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
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11
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Gradeci D, Bove A, Vallardi G, Lowe AR, Banerjee S, Charras G. Cell-scale biophysical determinants of cell competition in epithelia. eLife 2021; 10:e61011. [PMID: 34014166 PMCID: PMC8137148 DOI: 10.7554/elife.61011] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 04/23/2021] [Indexed: 11/25/2022] Open
Abstract
How cells with different genetic makeups compete in tissues is an outstanding question in developmental biology and cancer research. Studies in recent years have revealed that cell competition can either be driven by short-range biochemical signalling or by long-range mechanical stresses in the tissue. To date, cell competition has generally been characterised at the population scale, leaving the single-cell-level mechanisms of competition elusive. Here, we use high time-resolution experimental data to construct a multi-scale agent-based model for epithelial cell competition and use it to gain a conceptual understanding of the cellular factors that governs competition in cell populations within tissues. We find that a key determinant of mechanical competition is the difference in homeostatic density between winners and losers, while differences in growth rates and tissue organisation do not affect competition end result. In contrast, the outcome and kinetics of biochemical competition is strongly influenced by local tissue organisation. Indeed, when loser cells are homogenously mixed with winners at the onset of competition, they are eradicated; however, when they are spatially separated, winner and loser cells coexist for long times. These findings suggest distinct biophysical origins for mechanical and biochemical modes of cell competition.
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Affiliation(s)
- Daniel Gradeci
- Department of Physics and Astronomy, University College LondonLondonUnited Kingdom
- London Centre for Nanotechnology, University College LondonLondonUnited Kingdom
| | - Anna Bove
- London Centre for Nanotechnology, University College LondonLondonUnited Kingdom
- Department of Cell and Developmental Biology, University College LondonLondonUnited Kingdom
| | - Giulia Vallardi
- Institute for Structural and Molecular Biology, University College LondonLondonUnited Kingdom
| | - Alan R Lowe
- London Centre for Nanotechnology, University College LondonLondonUnited Kingdom
- Institute for Structural and Molecular Biology, University College LondonLondonUnited Kingdom
- Institute for the Physics of Living Systems, University College LondonLondonUnited Kingdom
| | - Shiladitya Banerjee
- Department of Physics and Astronomy, University College LondonLondonUnited Kingdom
- Institute for the Physics of Living Systems, University College LondonLondonUnited Kingdom
- Department of Physics, Carnegie Mellon UniversityPittsburghUnited States
| | - Guillaume Charras
- London Centre for Nanotechnology, University College LondonLondonUnited Kingdom
- Department of Cell and Developmental Biology, University College LondonLondonUnited Kingdom
- Institute for the Physics of Living Systems, University College LondonLondonUnited Kingdom
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12
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Lecca P. Control Theory and Cancer Chemotherapy: How They Interact. Front Bioeng Biotechnol 2021; 8:621269. [PMID: 33520972 PMCID: PMC7841331 DOI: 10.3389/fbioe.2020.621269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/09/2020] [Indexed: 12/01/2022] Open
Abstract
Control theory arises in most modern real-life applications, not least in biological and medical applications. In particular, in biological and medical contexts, the role of control theory began to take shape in the early 1980s when the first works appeared on the application of control theory in models of pharmacokinetics and pharmacodynamics for antitumor therapies. Forty years after those first works, the theory of control continues to be considered a mathematical analysis tool of extreme importance and usefulness, but the challenges it must overcome in order to manage the complexity of biological processes are in fact not yet overcome. In this article, we introduce the reader to the basic ideas of control theory, its aims and its mathematical formalization, and we review its use in cell phase-specific models for cancer chemotherapy. We discuss strengths and limitations of the control theory approach to the analysis pharmacokinetics and pharmacodynamics models, and we will see that most of them are strongly related to data availability and mathematical form of the model. We propose some future research directions that could prove useful in overcoming the these limitations and we indicate the crucial steps preliminary to a useful and informative application of control theory to cancer chemotherapy modeling.
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Affiliation(s)
- Paola Lecca
- Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy
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13
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Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
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Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
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14
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Hamis S, Powathil GG, Chaplain MAJ. Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments. JCO Clin Cancer Inform 2020; 3:1-11. [PMID: 30742485 DOI: 10.1200/cci.18.00068] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line-specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.
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Affiliation(s)
- Sara Hamis
- Swansea University, Swansea, Wales, United Kingdom
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15
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Cassani S, Olson SD. A Hybrid Model of Cartilage Regeneration Capturing the Interactions Between Cellular Dynamics and Porosity. Bull Math Biol 2020; 82:18. [PMID: 31970523 DOI: 10.1007/s11538-020-00695-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 12/27/2019] [Indexed: 12/31/2022]
Abstract
To accelerate the development of strategies for cartilage tissue engineering, models are necessary to investigate the interactions between cellular dynamics and the local microenvironment. We use a discrete framework to capture the individual behavior of cells, modeling experiments where cells are seeded in a porous scaffold or hydrogel and over the time course of a month, the scaffold slowly degrades while cells divide and synthesize extracellular matrix constituents. The movement of cells and the ability to proliferate is a function of the local porosity, defined as the volume fraction of fluid in the surrounding region. A phenomenological approach is used to capture a continuous profile for the degrading scaffold and accumulating matrix, which will then change the local porosity throughout the construct. We parameterize the model by first matching total cell counts in the construct to chondrocytes seeded in a polyglycolic acid scaffold (Freed et al. in Biotechnol Bioeng 43:597-604, 1994). We investigate the influence of initial scaffold porosity on the total cell count and spatial profiles of cell and ECM in the construct. Cell counts were higher at day 30 in scaffolds of lower initial porosity, and similar cell counts were obtained using different models of scaffold degradation and matrix accumulation (either uniform or cell-specific). Using this modeling framework, we study the interplay between a phenomenological representation of scaffold architecture and porosity as well as the potential continuous application of growth factors. We determine parameter regimes where large cellular aggregates occur, which can hinder matrix accumulation and cellular proliferation. The developed modeling framework can easily be extended and can be used to identify optimal scaffolds and culture conditions that lead to a desired distribution of extracellular matrix and cell counts throughout the construct.
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Affiliation(s)
- Simone Cassani
- Department of Mathematics, University at Buffalo, The State University of New York, 244 Mathematics Building, Buffalo, NY, 14260, USA
| | - Sarah D Olson
- Department of Mathematical Sciences, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
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16
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Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, Teixeira J. Towards Accurate Simulation of Global Challenges on Data Centers Infrastructures via Coupling of Models and Data Sources. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7304766 DOI: 10.1007/978-3-030-50433-5_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Accurate digital twinning of the global challenges (GC) leads to computationally expensive coupled simulations. These simulations bring together not only different models, but also various sources of massive static and streaming data sets. In this paper, we explore ways to bridge the gap between traditional high performance computing (HPC) and data-centric computation in order to provide efficient technological solutions for accurate policy-making in the domain of GC. GC simulations in HPC environments give rise to a number of technical challenges related to coupling. Being intended to reflect current and upcoming situation for policy-making, GC simulations extensively use recent streaming data coming from external data sources, which requires changing traditional HPC systems operation. Another common challenge stems from the necessity to couple simulations and exchange data across data centers in GC scenarios. By introducing a generalized GC simulation workflow, this paper shows commonality of the technical challenges for various GC and reflects on the approaches to tackle these technical challenges in the HiDALGO project.
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17
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Parallelisation strategies for agent based simulation of immune systems. BMC Bioinformatics 2019; 20:579. [PMID: 31823716 PMCID: PMC6905091 DOI: 10.1186/s12859-019-3181-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 10/29/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches. RESULTS This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment. CONCLUSIONS Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.
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18
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Franssen LC, Lorenzi T, Burgess AEF, Chaplain MAJ. A Mathematical Framework for Modelling the Metastatic Spread of Cancer. Bull Math Biol 2019; 81:1965-2010. [PMID: 30903592 PMCID: PMC6503893 DOI: 10.1007/s11538-019-00597-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 03/08/2019] [Indexed: 12/13/2022]
Abstract
Cancer is a complex disease that starts with mutations of key genes in one cell or a small group of cells at a primary site in the body. If these cancer cells continue to grow successfully and, at some later stage, invade the surrounding tissue and acquire a vascular network, they can spread to distant secondary sites in the body. This process, known as metastatic spread, is responsible for around 90% of deaths from cancer and is one of the so-called hallmarks of cancer. To shed light on the metastatic process, we present a mathematical modelling framework that captures for the first time the interconnected processes of invasion and metastatic spread of individual cancer cells in a spatially explicit manner-a multigrid, hybrid, individual-based approach. This framework accounts for the spatiotemporal evolution of mesenchymal- and epithelial-like cancer cells, membrane-type-1 matrix metalloproteinase (MT1-MMP) and the diffusible matrix metalloproteinase-2 (MMP-2), and for their interactions with the extracellular matrix. Using computational simulations, we demonstrate that our model captures all the key steps of the invasion-metastasis cascade, i.e. invasion by both heterogeneous cancer cell clusters and by single mesenchymal-like cancer cells; intravasation of these clusters and single cells both via active mechanisms mediated by matrix-degrading enzymes (MDEs) and via passive shedding; circulation of cancer cell clusters and single cancer cells in the vasculature with the associated risk of cell death and disaggregation of clusters; extravasation of clusters and single cells; and metastatic growth at distant secondary sites in the body. By faithfully reproducing experimental results, our simulations support the evidence-based hypothesis that the membrane-bound MT1-MMP is the main driver of invasive spread rather than diffusible MDEs such as MMP-2.
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Affiliation(s)
- Linnea C Franssen
- School of Mathematics and Statistics, University of St Andrews, St Andrews, UK.
| | - Tommaso Lorenzi
- School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
| | | | - Mark A J Chaplain
- School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
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19
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Bouchnita A, Hellander S, Hellander A. A 3D Multiscale Model to Explore the Role of EGFR Overexpression in Tumourigenesis. Bull Math Biol 2019; 81:2323-2344. [PMID: 31016574 PMCID: PMC6612322 DOI: 10.1007/s11538-019-00607-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 04/15/2019] [Indexed: 11/30/2022]
Abstract
The epidermal growth factor receptor (EGFR) signalling cascade is one of the main pathways that regulate the survival and division of mammalian cells. It is also one of the most altered transduction pathways in cancer. Acquired mutations in the EGFR/ERK pathway can cause the overexpression of EGFR on the surface of the cell, while others downregulate the inactivation of switched on intracellular proteins such as Ras and Raf. This upregulates the activity of ERK and promotes cell division. We develop a 3D multiscale model to explore the role of EGFR overexpression on tumour initiation. In this model, cells are described as individual objects that move, interact, divide, proliferate, and die by apoptosis. We use Brownian Dynamics to describe the extracellular and intracellular regulations of cells as well as the spatial and stochastic effects influencing them. The fate of each cell depends on the number of active transcription factors in the nucleus. We use numerical simulations to investigate the individual and combined effects of mutations on the intracellular regulation of individual cells. Next, we show that the distance between active receptors increase the level of EGFR/ERK signalling. We demonstrate the usefulness of the model by quantifying the impact of mutational alterations in the EGFR/ERK pathway on the growth rate of in silico tumours.
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Affiliation(s)
- Anass Bouchnita
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 75105, Uppsala, Sweden.
| | - Stefan Hellander
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 75105, Uppsala, Sweden
| | - Andreas Hellander
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 75105, Uppsala, Sweden
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20
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Wu DJ, Aktipis A, Pepper JW. Energy oversupply to tissues: a single mechanism possibly underlying multiple cancer risk factors. Evol Med Public Health 2019; 2019:9-16. [PMID: 31893122 PMCID: PMC6379718 DOI: 10.1093/emph/eoz004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 01/15/2019] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Several major risk factors for cancer involve vascular oversupply of energy to affected tissues. These include obesity, diabetes and chronic inflammation. Here, we propose a potential mechanistic explanation for the association between energy oversupply and cancer risk, which we call the metabolic cancer suppression hypothesis: We hypothesize that oncogenesis is normally suppressed by organismal physiology that regulates and strictly limits normal energy supply to somatic cells, and that this protection is removed by abnormal oversupply of energy. METHODOLOGY We evaluate this hypothesis using a computational model of somatic cell evolution to simulate experimental manipulation of the vascular energy supply to a tissue. The model simulates the evolutionary dynamics of somatic cells during oncogenesis. RESULTS In our simulation experiment, we found that under plausible biological assumptions, elevated energy supply to a tissue led to the evolution of elevated energy uptake by somatic cells, leading to the rapid evolution of both defining traits of cancer cells: hyperproliferation, and tissue invasion. CONCLUSIONS AND IMPLICATIONS Our results support the hypothesis of metabolic cancer suppression, suggesting that vascular oversupply of energetic resources to somatic cells removes normal energetic limitations on cell proliferation, and that this accelerates cellular evolution toward cancer. Various predictions of this hypothesis are amenable to empirical testing, and have promising implications for translational research toward clinical cancer prevention.
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Affiliation(s)
- Daniel J Wu
- Department of Biology, Stanford University, Stanford, CA, USA
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Athena Aktipis
- Arizona State University, Biodesign Institute, Tempe, AZ, USA
| | - John W Pepper
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
- Santa Fe Institute, Santa Fe, NM, USA
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21
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Estimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-3938-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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22
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Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics. Processes (Basel) 2018. [DOI: 10.3390/pr6110217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular functions, such as death, division, or phenotype change. Cell death is implemented by terminating a parallel process, while cell division is carried out by creating a new process (daughter cell) from an existing one (mother cell). We first demonstrate these capabilities by creating two simple example models. In one model, we consider a relatively simple scenario where cells can evolve independently. In the other model, we consider interdependency among the cells, where cellular communication determines their collective behavior and evolution under a temporally evolving growth condition. We then demonstrate the framework’s capability by simulating a full-scale model of bacterial quorum sensing, where the dynamics of a population of bacterial cells is dictated by the intercellular communications in a time-evolving growth environment.
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23
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Hamis S, Nithiarasu P, Powathil GG. What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance. J Theor Biol 2018; 454:253-267. [DOI: 10.1016/j.jtbi.2018.06.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 06/04/2018] [Accepted: 06/12/2018] [Indexed: 12/01/2022]
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24
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Castro C, Flores DL, Cervantes-Vásquez D, Vargas-Viveros E, Gutiérrez-López E, Muñoz-Muñoz F. An agent-based model of the fission yeast cell cycle. Curr Genet 2018; 65:193-200. [PMID: 29916047 DOI: 10.1007/s00294-018-0859-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 06/13/2018] [Accepted: 06/15/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Carlos Castro
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Dora-Luz Flores
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico.
| | - David Cervantes-Vásquez
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Eunice Vargas-Viveros
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Everardo Gutiérrez-López
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
| | - Franklin Muñoz-Muñoz
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917, 22860, Ensenada, BC, Mexico
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25
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Soheilypour M, Mofrad MRK. Agent-Based Modeling in Molecular Systems Biology. Bioessays 2018; 40:e1800020. [PMID: 29882969 DOI: 10.1002/bies.201800020] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Indexed: 12/13/2022]
Abstract
Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.
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Affiliation(s)
- Mohammad Soheilypour
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Mohammad R K Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
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26
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Bowness R, Chaplain MAJ, Powathil GG, Gillespie SH. Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model. J Theor Biol 2018; 446:87-100. [PMID: 29524441 PMCID: PMC5901892 DOI: 10.1016/j.jtbi.2018.03.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 03/01/2018] [Accepted: 03/06/2018] [Indexed: 02/07/2023]
Abstract
If improvements are to be made in tuberculosis (TB) treatment, an increased understanding of disease in the lung is needed. Studies have shown that bacteria in a less metabolically active state, associated with the presence of lipid bodies, are less susceptible to antibiotics, and recent results have highlighted the disparity in concentration of different compounds into lesions. Treatment success therefore depends critically on the responses of the individual bacteria that constitute the infection. We propose a hybrid, individual-based approach that analyses spatio-temporal dynamics at the cellular level, linking the behaviour of individual bacteria and host cells with the macroscopic behaviour of the microenvironment. The individual elements (bacteria, macrophages and T cells) are modelled using cellular automaton (CA) rules, and the evolution of oxygen, drugs and chemokine dynamics are incorporated in order to study the effects of the microenvironment in the pathological lesion. We allow bacteria to switch states depending on oxygen concentration, which affects how they respond to treatment. This is the first multiscale model of its type to consider both oxygen-driven phenotypic switching of the Mycobacterium tuberculosis and antibiotic treatment. Using this model, we investigate the role of bacterial cell state and of initial bacterial location on treatment outcome. We demonstrate that when bacteria are located further away from blood vessels, less favourable outcomes are more likely, i.e. longer time before infection is contained/cleared, treatment failure or later relapse. We also show that in cases where bacteria remain at the end of simulations, the organisms tend to be slower-growing and are often located within granulomas, surrounded by caseous material.
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Affiliation(s)
- Ruth Bowness
- School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UK.
| | - Mark A J Chaplain
- School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews KY16 9SS, UK
| | - Gibin G Powathil
- Department of Mathematics, Talbot Building, Swansea University, Singleton Park, Swansea, SA2 8PP, UK
| | - Stephen H Gillespie
- School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, UK
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27
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Dufour A, Gontran E, Deroulers C, Varlet P, Pallud J, Grammaticos B, Badoual M. Modeling the dynamics of oligodendrocyte precursor cells and the genesis of gliomas. PLoS Comput Biol 2018; 14:e1005977. [PMID: 29590097 PMCID: PMC5903643 DOI: 10.1371/journal.pcbi.1005977] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 04/17/2018] [Accepted: 01/10/2018] [Indexed: 11/24/2022] Open
Abstract
Oligodendrocyte precursor cells (OPCs) have remarkable properties: they represent the most abundant cycling cell population in the adult normal brain and they manage to achieve a uniform and constant density throughout the adult brain. This equilibrium is obtained by the interplay of four processes: division, differentiation or death, migration and active self-repulsion. They are also strongly suspected to be at the origin of gliomas, when their equilibrium is disrupted. In this article, we present a model of the dynamics of OPCs, first in a normal tissue. This model is based on a cellular automaton and its rules are mimicking the ones that regulate the dynamics of real OPCs. The model is able to reproduce the homeostasis of the cell population, with the maintenance of a constant and uniform cell density and the healing of a lesion. We show that there exists a fair quantitative agreement between the simulated and experimental parameters, such as the cell velocity, the time taken to close a lesion, and the duration of the cell cycle. We present three possible scenarios of disruption of the equilibrium: the appearance of an over-proliferating cell, of a deadless/non-differentiating cell, or of a cell that lost any contact-inhibition. We show that the appearance of an over-proliferating cell is sufficient to trigger the growth of a tumor that has low-grade glioma features: an invasive behaviour, a linear radial growth of the tumor with a corresponding growth velocity of less than 2 mm per year, as well a cell density at the center which exceeds the one in normal tissue by a factor of less than two. The loss of contact inhibition leads to a more high-grade-like glioma. The results of our model contribute to the body of evidence that identify OPCs as possible cells of origin of gliomas. Gliomas are the most common brain tumors and result in more years of life lost than any other tumor. Standard treatments only confer a limited improvement in overall survival, underscoring the need for new therapies. Finding the type of cells at the origin of these tumors could lead to the development of new drugs, specifically targeted towards these cells. The oligodendrocyte precursor cells are suspected to be these cells of origin, because they continue to proliferate through all the adult life. In this article, we present a model of the dynamics of these cells, first in the normal brain, and then we extrapolate our model to the pathological situation. We study several scenarios where, from the normal situation, a cell appears with one property different from those of the normal cells. We show that the alteration of only one of the properties of these cells in the model can lead to the formation of gliomas with different aggressiveness and very similar to real gliomas, reinforcing the suspicion that the precursor cells are at the origin of gliomas.
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Affiliation(s)
- Aloys Dufour
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Emilie Gontran
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Christophe Deroulers
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Pascale Varlet
- Department of Neuropathology, Sainte-Anne Hospital, IMA-Brain, INSERM U894, Univ Paris Descartes, Paris, France
| | - Johan Pallud
- Department of Neurosurgery, Sainte-Anne Hospital, IMA-Brain, INSERM U894, Univ Paris Descartes, Paris, France
| | - Basile Grammaticos
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Mathilde Badoual
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
- * E-mail:
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Santagiuliana R, Pereira RC, Schrefler BA, Decuzzi P. Predicting the role of microstructural and biomechanical cues in tumor growth and spreading. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2935. [PMID: 29083532 DOI: 10.1002/cnm.2935] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 09/09/2017] [Accepted: 10/17/2017] [Indexed: 06/07/2023]
Abstract
A continuum porous media model is developed for elucidating the role of the mechanical cues in regulating tumor growth and spreading. It is shown that stiffer matrices and higher cell-matrix adhesion limit tumor growth and spreading toward the surrounding tissue. Higher matrix porosities, conversely, favor the growth and the dissemination of tumor cells. This model could be used for predicting the response of malignant masses to novel therapeutic agents affecting directly the tumor microenvironment and its micromechanical cues.
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Affiliation(s)
- Raffaella Santagiuliana
- Laboratory of Nanotechnology for Precision Medicine, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, Genoa, 16163, Italy
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Via Marzolo 9, 35131, Padova, Italy
| | - Rui C Pereira
- Laboratory of Nanotechnology for Precision Medicine, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, Genoa, 16163, Italy
| | - Bernhard A Schrefler
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Via Marzolo 9, 35131, Padova, Italy
- Institute for Advanced Study, Technische Universität München, Munich, Germany
| | - Paolo Decuzzi
- Laboratory of Nanotechnology for Precision Medicine, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, Genoa, 16163, Italy
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Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection. Int J Mol Sci 2017; 18:ijms18122592. [PMID: 29194393 PMCID: PMC5751195 DOI: 10.3390/ijms18122592] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 11/23/2017] [Accepted: 11/26/2017] [Indexed: 11/16/2022] Open
Abstract
Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.
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Logsdon DK, Beeghly GF, Munson JM. Chemoprotection Across the Tumor Border: Cancer Cell Response to Doxorubicin Depends on Stromal Fibroblast Ratios and Interstitial Therapeutic Transport. Cell Mol Bioeng 2017; 10:463-481. [PMID: 31719872 PMCID: PMC6816789 DOI: 10.1007/s12195-017-0498-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 07/20/2017] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION Increasing evidence suggests that the tumor microenvironment reduces therapeutic delivery and may lead to chemotherapeutic resistance. At tumor borders, drug is convectively transported across a unique microenvironment composed of inverse gradients of stromal and tumor cells. These regions are particularly important to overall survival, as they are often missed through surgical intervention and contain many invading cells, often responsible for metastatic spread. An understanding of how cells in this tumor-border region respond to chemotherapy could begin to elucidate the role of transport and intercellular interactions in relation to chemoresistance. Here we examine the contribution of drug transport and stromal fibroblasts to breast cancer response to doxorubicin using in silico and in vitro models of the tumor-stroma interface. METHODS 2D culture systems were utilized to determine the effects of modulated ratios of fibroblasts and cancer cells on overall cancer cell viability. A homogenous breast mimetic in vitro 3D collagen I-based hydrogel system, with drug delivered via pressure driven flow (0.5 µm/s), was developed to determine the effects of transport and fibroblasts on doxorubicin treatment efficacy. Using a novel layered tumor bulk-to-stroma transition in vitro 3D hydrogel model, ratios of MDA-MB-231s and fibroblasts were seeded in successive layers creating cellular gradients, yielding insight into region specific cancer cell viability at the tumor border. In silico models, utilizing concentration profiles developed in COMSOL Multiphysics, were optimized for time dependent viability prediction and confirmation of in vitro findings. RESULTS In general, the addition of fibroblasts increased viability of cancer cells exposed to doxorubicin, indicating a protective effect of co-culture. More specifically, however, modulating ratios of cancer cells (MDA-MB-231):fibroblasts in 2D co-cultures, to mimic the tumor-stroma transition, resulted in a linear decrease in cancer cell viability from 77% (4:1) to 44% (1:4). Similar trends were seen in the breast-mimetic in vitro 3D collagen I-based homogenous hydrogel system. Our in vitro and in silico tumor border models indicate that MDA-MB-231s at the top of the gel, indicative of the tumor bulk, receive the greatest concentration of drug for the longest time, yet cellular death is lowest in this region. This trend is reversed for MDA-MB-231s alone. CONCLUSION Together, our data indicate that fibroblasts are chemoprotective at lower density, resulting in less tumor death in regions of higher chemotherapy concentration. Additionally, chemotherapeutic agent transport properties can modulate this effect.
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Affiliation(s)
- Daniel K. Logsdon
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA 22908 USA
| | - Garrett F. Beeghly
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA 22908 USA
| | - Jennifer M. Munson
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA 22908 USA
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Kelly Hall, 325 Stanger Street, Blacksburg, VA 24061 USA
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Cabrera-Becerril A, Vargas-De-León C, Hernández S, Miramontes P, Peralta R. Modeling the dynamics of chromosomal alteration progression in cervical cancer: A computational model. PLoS One 2017; 12:e0180882. [PMID: 28723940 PMCID: PMC5516994 DOI: 10.1371/journal.pone.0180882] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 06/22/2017] [Indexed: 12/16/2022] Open
Abstract
Computational modeling has been applied to simulate the heterogeneity of cancer behavior. The development of Cervical Cancer (CC) is a process in which the cell acquires dynamic behavior from non-deleterious and deleterious mutations, exhibiting chromosomal alterations as a manifestation of this dynamic. To further determine the progression of chromosomal alterations in precursor lesions and CC, we introduce a computational model to study the dynamics of deleterious and non-deleterious mutations as an outcome of tumor progression. The analysis of chromosomal alterations mediated by our model reveals that multiple deleterious mutations are more frequent in precursor lesions than in CC. Cells with lethal deleterious mutations would be eliminated, which would mitigate cancer progression; on the other hand, cells with non-deleterious mutations would become dominant, which could predispose them to cancer progression. The study of somatic alterations through computer simulations of cancer progression provides a feasible pathway for insights into the transformation of cell mechanisms in humans. During cancer progression, tumors may acquire new phenotype traits, such as the ability to invade and metastasize or to become clinically important when they develop drug resistance. Non-deleterious chromosomal alterations contribute to this progression.
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Affiliation(s)
- Augusto Cabrera-Becerril
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Cruz Vargas-De-León
- Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de México, México
| | - Sergio Hernández
- Programa de Dinámica Nolineal, Universidad Autónoma de la Ciudad de México, Ciudad de México, México
| | - Pedro Miramontes
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Raúl Peralta
- Centro de Investigación en Dinámica Celular, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, México
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Shams H, Soheilypour M, Peyro M, Moussavi-Baygi R, Mofrad MRK. Looking "Under the Hood" of Cellular Mechanotransduction with Computational Tools: A Systems Biomechanics Approach across Multiple Scales. ACS Biomater Sci Eng 2017; 3:2712-2726. [PMID: 33418698 DOI: 10.1021/acsbiomaterials.7b00117] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Signal modulation has been developed in living cells throughout evolution to promote utilizing the same machinery for multiple cellular functions. Chemical and mechanical modules of signal transmission and transduction are interconnected and necessary for organ development and growth. However, due to the high complexity of the intercommunication of physical intracellular connections with biochemical pathways, there are many missing details in our overall understanding of mechanotransduction processes, i.e., the process by which mechanical signals are converted to biochemical cascades. Cell-matrix adhesions are mechanically coupled to the nucleus through the cytoskeleton. This modulated and tightly integrated network mediates the transmission of mechanochemical signals from the extracellular matrix to the nucleus. Various experimental and computational techniques have been utilized to understand the basic mechanisms of mechanotransduction, yet many aspects have remained elusive. Recently, in silico experiments have made important contributions to the field of mechanobiology. Herein, computational modeling efforts devoted to understanding integrin-mediated mechanotransduction pathways are reviewed, and an outlook is presented for future directions toward using suitable computational approaches and developing novel techniques for addressing important questions in the field of mechanotransduction.
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Affiliation(s)
- Hengameh Shams
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Mohammad Soheilypour
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Mohaddeseh Peyro
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Ruhollah Moussavi-Baygi
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Mohammad R K Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
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A Hybrid Computation Model to Describe the Progression of Multiple Myeloma and Its Intra-Clonal Heterogeneity. COMPUTATION 2017. [DOI: 10.3390/computation5010016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Using game theory to investigate the epigenetic control mechanisms of embryo development. Phys Life Rev 2017; 20:140-142. [DOI: 10.1016/j.plrev.2017.01.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 01/11/2017] [Indexed: 01/10/2023]
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Warsinske HC, Wheaton AK, Kim KK, Linderman JJ, Moore BB, Kirschner DE. Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis. Front Pharmacol 2016; 7:183. [PMID: 27445819 PMCID: PMC4917547 DOI: 10.3389/fphar.2016.00183] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 06/10/2016] [Indexed: 11/13/2022] Open
Abstract
Pulmonary fibrosis is pathologic remodeling of lung tissue that can result in difficulty breathing, reduced quality of life, and a poor prognosis for patients. Fibrosis occurs as a result of insult to lung tissue, though mechanisms of this response are not well-characterized. The disease is driven in part by dysregulation of fibroblast proliferation and differentiation into myofibroblast cells, as well as pro-fibrotic mediator-driven epithelial cell apoptosis. The most well-characterized pro-fibrotic mediator associated with pulmonary fibrosis is TGF-β1. Excessive synthesis of, and sensitivity to, pro-fibrotic mediators as well as insufficient production of and sensitivity to anti-fibrotic mediators has been credited with enabling fibroblast accumulation. Available treatments neither halt nor reverse lung damage. In this study we have two aims: to identify molecular and cellular scale mechanisms driving fibroblast proliferation and differentiation as well as epithelial cell survival in the context of fibrosis, and to predict therapeutic targets and strategies. We combine in vitro studies with a multi-scale hybrid agent-based computational model that describes fibroblasts and epithelial cells in co-culture. Within this model TGF-β1 represents a pro-fibrotic mediator and we include detailed dynamics of TGF-β1 receptor ligand signaling in fibroblasts. PGE2 represents an anti-fibrotic mediator. Using uncertainty and sensitivity analysis we identify TGF-β1 synthesis, TGF-β1 activation, and PGE2 synthesis among the key mechanisms contributing to fibrotic outcomes. We further demonstrate that intervention strategies combining potential therapeutics targeting both fibroblast regulation and epithelial cell survival can promote healthy tissue repair better than individual strategies. Combinations of existing drugs and compounds may provide significant improvements to the current standard of care for pulmonary fibrosis. Thus, a two-hit therapeutic intervention strategy may prove necessary to halt and reverse disease dynamics.
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Affiliation(s)
- Hayley C. Warsinske
- Department of Microbiology and Immunology, University of Michigan Medical SchoolAnn Arbor, MI, USA
| | - Amanda K. Wheaton
- Department of Internal Medicine, University of Michigan Medical SchoolAnn Arbor, MI, USA
| | - Kevin K. Kim
- Department of Internal Medicine, University of Michigan Medical SchoolAnn Arbor, MI, USA
| | | | - Bethany B. Moore
- Department of Microbiology and Immunology, University of Michigan Medical SchoolAnn Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical SchoolAnn Arbor, MI, USA
| | - Denise E. Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical SchoolAnn Arbor, MI, USA
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Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data. PLoS One 2015; 10:e0141295. [PMID: 26535589 PMCID: PMC4633145 DOI: 10.1371/journal.pone.0141295] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 10/07/2015] [Indexed: 12/04/2022] Open
Abstract
Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.
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Lin KW, Liao A, Qutub AA. Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth. PLoS Comput Biol 2015; 11:e1004169. [PMID: 25884993 PMCID: PMC4401766 DOI: 10.1371/journal.pcbi.1004169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 02/02/2015] [Indexed: 12/21/2022] Open
Abstract
Tremendous strides have been made in improving patients’ survival from cancer with one glaring exception: brain cancer. Glioblastoma is the most common, aggressive and highly malignant type of primary brain tumor. The average overall survival remains less than 1 year. Notably, cancer patients with obesity and diabetes have worse outcomes and accelerated progression of glioblastoma. The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway. However, while the process of invasive glioblastoma progression has been extensively studied macroscopically, it has not yet been well characterized with regards to intracellular insulin signaling. In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling. Results of the model suggest a novel observation: feedback from IGFBP2 to HIF1α is integral to the sustained growth of glioblastoma. Our study suggests that downstream signaling from IGFI to HIF1α, which has been the target of many insulin signaling drugs in clinical trials, plays a smaller role in overall tumor growth. These predictions strongly suggest redirecting the focus of glioma drug candidates on controlling the feedback between IGFBP2 and HIF1α. Current treatment for glioblastoma patients is limited to nonspecific methods: surgery followed by a combination of radio- and chemotherapy. With these methods, glioma patient survival is less than one year post-diagnosis. Targeting specific protein signaling pathways offers potentially more potent therapies. One promising potential target is the insulin signaling pathway, which is known to contribute to glioblastoma progression. However, drugs targeting this pathway have shown mixed results in clinical trials, and the detailed mechanisms of how the insulin signaling pathway promotes glioblastoma growth remain to be elucidated. Here, we developed a computational model of insulin signaling in glioblastoma in order to study this pathway’s role in tumor progression. Using the model, we systematically test contributions of different insulin signaling protein interactions on glioblastoma growth. Our model highlights a key driver for the growth of glioblastoma: IGFBP2-HIF1α feedback. This interaction provides a target that could open the door for new therapies in glioma and other solid tumors.
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Affiliation(s)
- Ka Wai Lin
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - Angela Liao
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - Amina A. Qutub
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
- * E-mail:
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Cockrell RC, Christley S, Chang E, An G. Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC). PLoS One 2015; 10:e0122192. [PMID: 25806784 PMCID: PMC4373890 DOI: 10.1371/journal.pone.0122192] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 02/11/2015] [Indexed: 02/06/2023] Open
Abstract
Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.
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Affiliation(s)
| | - Scott Christley
- Department of Surgery, University of Chicago, Chicago, IL, United States of America
| | - Eugene Chang
- Department of Medicine, University of Chicago, Chicago, IL, United States of America
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, IL, United States of America
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Powathil GG, Swat M, Chaplain MA. Systems oncology: Towards patient-specific treatment regimes informed by multiscale mathematical modelling. Semin Cancer Biol 2015; 30:13-20. [DOI: 10.1016/j.semcancer.2014.02.003] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Accepted: 02/06/2014] [Indexed: 10/25/2022]
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40
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Modeling intrinsic heterogeneity and growth of cancer cells. J Theor Biol 2014; 367:262-277. [PMID: 25457229 DOI: 10.1016/j.jtbi.2014.11.017] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Revised: 10/20/2014] [Accepted: 11/14/2014] [Indexed: 02/02/2023]
Abstract
Intratumoral heterogeneity has been found to be a major cause of drug resistance. Cell-to-cell variation increases as a result of cancer-related alterations, which are acquired by stochastic events and further induced by environmental signals. However, most cellular mechanisms include natural fluctuations that are closely regulated, and thus lead to asynchronization of the cells, which causes intrinsic heterogeneity in a given population. Here, we derive two novel mathematical models, a stochastic agent-based model and an integro-differential equation model, each of which describes the growth of cancer cells as a dynamic transition between proliferative and quiescent states. These models are designed to predict variations in growth as a function of the intrinsic heterogeneity emerging from the durations of the cell-cycle and apoptosis, and also include cellular density dependencies. By examining the role all parameters play in the evolution of intrinsic tumor heterogeneity, and the sensitivity of the population growth to parameter values, we show that the cell-cycle length has the most significant effect on the growth dynamics. In addition, we demonstrate that the agent-based model can be approximated well by the more computationally efficient integro-differential equations when the number of cells is large. This essential step in cancer growth modeling will allow us to revisit the mechanisms of multidrug resistance by examining spatiotemporal differences of cell growth while administering a drug among the different sub-populations in a single tumor, as well as the evolution of those mechanisms as a function of the resistance level.
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Pingle SC, Sultana Z, Pastorino S, Jiang P, Mukthavaram R, Chao Y, Bharati IS, Nomura N, Makale M, Abbasi T, Kapoor S, Kumar A, Usmani S, Agrawal A, Vali S, Kesari S. In silico modeling predicts drug sensitivity of patient-derived cancer cells. J Transl Med 2014; 12:128. [PMID: 24884660 PMCID: PMC4030016 DOI: 10.1186/1479-5876-12-128] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Accepted: 04/23/2014] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. METHODS Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. RESULTS Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. CONCLUSIONS These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Santosh Kesari
- Translational Neuro-Oncology Laboratories, Moores Cancer Center, UC San Diego, La Jolla, CA 92093, USA.
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Simeoni M, De Nicolao G, Magni P, Rocchetti M, Poggesi I. Modeling of human tumor xenografts and dose rationale in oncology. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e365-72. [PMID: 24050133 DOI: 10.1016/j.ddtec.2012.07.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.
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43
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Investigation of inflammation and tissue patterning in the gut using a Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT). PLoS Comput Biol 2014; 10:e1003507. [PMID: 24675765 PMCID: PMC3967920 DOI: 10.1371/journal.pcbi.1003507] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Accepted: 01/10/2014] [Indexed: 01/22/2023] Open
Abstract
The mucosa of the intestinal tract represents a finely tuned system where tissue structure strongly influences, and is turn influenced by, its function as both an absorptive surface and a defensive barrier. Mucosal architecture and histology plays a key role in the diagnosis, characterization and pathophysiology of a host of gastrointestinal diseases. Inflammation is a significant factor in the pathogenesis in many gastrointestinal diseases, and is perhaps the most clinically significant control factor governing the maintenance of the mucosal architecture by morphogenic pathways. We propose that appropriate characterization of the role of inflammation as a controller of enteric mucosal tissue patterning requires understanding the underlying cellular and molecular dynamics that determine the epithelial crypt-villus architecture across a range of conditions from health to disease. Towards this end we have developed the Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT) to dynamically represent existing knowledge of the behavior of enteric epithelial tissue as influenced by inflammation with the ability to generate a variety of pathophysiological processes within a common platform and from a common knowledge base. In addition to reproducing healthy ileal mucosal dynamics as well as a series of morphogen knock-out/inhibition experiments, SEGMEnT provides insight into a range of clinically relevant cellular-molecular mechanisms, such as a putative role for Phosphotase and tensin homolog/phosphoinositide 3-kinase (PTEN/PI3K) as a key point of crosstalk between inflammation and morphogenesis, the protective role of enterocyte sloughing in enteric ischemia-reperfusion and chronic low level inflammation as a driver for colonic metaplasia. These results suggest that SEGMEnT can serve as an integrating platform for the study of inflammation in gastrointestinal disease.
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Masoudi-Nejad A, Meshkin A. WITHDRAWN: Cancer modeling: The holonic agent-based approach. Semin Cancer Biol 2014:S1044-579X(14)00032-7. [PMID: 24598694 DOI: 10.1016/j.semcancer.2014.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 02/19/2014] [Accepted: 02/20/2014] [Indexed: 11/22/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Alireza Meshkin
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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45
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How to build a multiscale model in biology. Acta Biotheor 2013; 61:291-303. [PMID: 24061792 DOI: 10.1007/s10441-013-9199-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2012] [Accepted: 08/20/2013] [Indexed: 10/26/2022]
Abstract
Biological processes span several scales in space, from the single molecules to organisms and ecosystems. Multiscale modelling approaches in biology are useful to take into account the complex interactions between different organisation levels in those systems. We review several single- and multiscale models, from the most simple to the complex ones, and discuss their properties from a multiscale point of view. Approaches based on master equations for stochastic processes, individual-based models, hybrid continuous-discrete models and structured PDE models are presented.
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46
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Wang J, Zhang L, Jing C, Ye G, Wu H, Miao H, Wu Y, Zhou X. Multi-scale agent-based modeling on melanoma and its related angiogenesis analysis. Theor Biol Med Model 2013; 10:41. [PMID: 23800293 PMCID: PMC3694033 DOI: 10.1186/1742-4682-10-41] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2013] [Accepted: 06/18/2013] [Indexed: 12/26/2022] Open
Abstract
Background Recently, melanoma has become the most malignant and commonly occurring skin cancer. Melanoma is not only the major source (75%) of deaths related to skin cancer, but also it is hard to be treated by the conventional drugs. Recent research indicated that angiogenesis is an important factor for tumor initiation, expansion, and response to therapy. Thus, we proposed a novel multi-scale agent-based computational model that integrates the angiogenesis into tumor growth to study the response of melanoma cancer under combined drug treatment. Results Our multi-scale agent-based model can simulate the melanoma tumor growth with angiogenesis under combined drug treatment. The significant synergistic effects between drug Dox and drug Sunitinib demonstrated the clinical potential to interrupt the communication between melanoma cells and its related vasculatures. Also, the sensitivity analysis of the model revealed that diffusivity related to the micro-vasculatures around tumor tissues closely correlated with the spread, oscillation and destruction of the tumor. Conclusions Simulation results showed that the 3D model can represent key features of melanoma growth, angiogenesis, and its related micro-environment. The model can help cancer researchers understand the melanoma developmental mechanism. Drug synergism analysis suggested that interrupting the communications between melanoma cells and the related vasculatures can significantly increase the drug efficacy against tumor cells.
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Affiliation(s)
- Jun Wang
- College of Computer and Information Science, Southwest University, Chongqing 400715, China
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47
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Csikász-Nagy A, Escudero LM, Guillaud M, Sedwards S, Baum B, Cavaliere M. Cooperation and competition in the dynamics of tissue architecture during homeostasis and tumorigenesis. Semin Cancer Biol 2013; 23:293-8. [PMID: 23751796 DOI: 10.1016/j.semcancer.2013.05.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2013] [Revised: 05/28/2013] [Accepted: 05/30/2013] [Indexed: 01/26/2023]
Abstract
The construction of a network of cell-to-cell contacts makes it possible to characterize the patterns and spatial organization of tissues. Such networks are highly dynamic, depending on the changes of the tissue architecture caused by cell division, death and migration. Local competitive and cooperative cell-to-cell interactions influence the choices cells make. We review the literature on quantitative data of epithelial tissue topology and present a dynamical network model that can be used to explore the evolutionary dynamics of a two dimensional tissue architecture with arbitrary cell-to-cell interactions. In particular, we show that various forms of experimentally observed types of interactions can be modelled using game theory. We discuss a model of cooperative and non-cooperative cell-to-cell communication that can capture the interplay between cellular competition and tissue dynamics. We conclude with an outlook on the possible uses of this approach in modelling tumorigenesis and tissue homeostasis.
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Affiliation(s)
- Attila Csikász-Nagy
- Department of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige 38010, Italy.
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An integrated computational/experimental model of lymphoma growth. PLoS Comput Biol 2013; 9:e1003008. [PMID: 23555235 PMCID: PMC3610621 DOI: 10.1371/journal.pcbi.1003008] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Accepted: 02/13/2013] [Indexed: 12/27/2022] Open
Abstract
Non-Hodgkin's lymphoma is a disseminated, highly malignant cancer, with resistance to drug treatment based on molecular- and tissue-scale characteristics that are intricately linked. A critical element of molecular resistance has been traced to the loss of functionality in proteins such as the tumor suppressor p53. We investigate the tissue-scale physiologic effects of this loss by integrating in vivo and immunohistological data with computational modeling to study the spatiotemporal physical dynamics of lymphoma growth. We compare between drug-sensitive Eμ-myc Arf-/- and drug-resistant Eμ-myc p53-/- lymphoma cell tumors grown in live mice. Initial values for the model parameters are obtained in part by extracting values from the cellular-scale from whole-tumor histological staining of the tumor-infiltrated inguinal lymph node in vivo. We compare model-predicted tumor growth with that observed from intravital microscopy and macroscopic imaging in vivo, finding that the model is able to accurately predict lymphoma growth. A critical physical mechanism underlying drug-resistant phenotypes may be that the Eμ-myc p53-/- cells seem to pack more closely within the tumor than the Eμ-myc Arf-/- cells, thus possibly exacerbating diffusion gradients of oxygen, leading to cell quiescence and hence resistance to cell-cycle specific drugs. Tighter cell packing could also maintain steeper gradients of drug and lead to insufficient toxicity. The transport phenomena within the lymphoma may thus contribute in nontrivial, complex ways to the difference in drug sensitivity between Eμ-myc Arf-/- and Eμ-myc p53-/- tumors, beyond what might be solely expected from loss of functionality at the molecular scale. We conclude that computational modeling tightly integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma and provides a platform to generate confirmable predictions of tumor growth. Non-Hodgkin's lymphoma is a cancer that develops from white blood cells called lymphocytes in the immune system, whose role is to fight disease throughout the body. This cancer can spread throughout the whole body and be very lethal – in the US, one third of patients will die from this disease within five years of diagnosis. Chemotherapy is a usual treatment for lymphoma, but the cancer can become highly resistant to it. One reason is that a critical gene called p53 can become mutated and help the cancer to survive. In this work we investigate how cells with this mutation affect the cancer growth by performing experiments in mice and using a computer model. By inputting the model parameters based on data from the experiments, we are able to accurately predict the growth of the tumor as compared to tumor measurements in living mice. We conclude that computational modeling integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma, and provides a platform to generate confirmable predictions of tumor growth.
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Bhattacharya S, Shoda LKM, Zhang Q, Woods CG, Howell BA, Siler SQ, Woodhead JL, Yang Y, McMullen P, Watkins PB, Andersen ME. Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches. Front Physiol 2012; 3:462. [PMID: 23248599 PMCID: PMC3522076 DOI: 10.3389/fphys.2012.00462] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 11/21/2012] [Indexed: 12/22/2022] Open
Abstract
We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.
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Affiliation(s)
- Sudin Bhattacharya
- Institute for Chemical Safety Sciences, The Hamner Institutes for Health Sciences Research Triangle Park, NC, USA
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50
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Carlier A, Geris L, Bentley K, Carmeliet G, Carmeliet P, Van Oosterwyck H. MOSAIC: a multiscale model of osteogenesis and sprouting angiogenesis with lateral inhibition of endothelial cells. PLoS Comput Biol 2012; 8:e1002724. [PMID: 23071433 PMCID: PMC3469420 DOI: 10.1371/journal.pcbi.1002724] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2012] [Accepted: 08/18/2012] [Indexed: 01/15/2023] Open
Abstract
The healing of a fracture depends largely on the development of a new blood vessel network (angiogenesis) in the callus. During angiogenesis tip cells lead the developing sprout in response to extracellular signals, amongst which vascular endothelial growth factor (VEGF) is critical. In order to ensure a correct development of the vasculature, the balance between stalk and tip cell phenotypes must be tightly controlled, which is primarily achieved by the Dll4-Notch1 signaling pathway. This study presents a novel multiscale model of osteogenesis and sprouting angiogenesis, incorporating lateral inhibition of endothelial cells (further denoted MOSAIC model) through Dll4-Notch1 signaling, and applies it to fracture healing. The MOSAIC model correctly predicted the bone regeneration process and recapitulated many experimentally observed aspects of tip cell selection: the salt and pepper pattern seen for cell fates, an increased tip cell density due to the loss of Dll4 and an excessive number of tip cells in high VEGF environments. When VEGF concentration was even further increased, the MOSAIC model predicted the absence of a vascular network and fracture healing, thereby leading to a non-union, which is a direct consequence of the mutual inhibition of neighboring cells through Dll4-Notch1 signaling. This result was not retrieved for a more phenomenological model that only considers extracellular signals for tip cell migration, which illustrates the importance of implementing the actual signaling pathway rather than phenomenological rules. Finally, the MOSAIC model demonstrated the importance of a proper criterion for tip cell selection and the need for experimental data to further explore this. In conclusion, this study demonstrates that the MOSAIC model creates enhanced capabilities for investigating the influence of molecular mechanisms on angiogenesis and its relation to bone formation in a more mechanistic way and across different time and spatial scales.
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Affiliation(s)
- Aurélie Carlier
- Biomechanics Section, KU Leuven, Leuven, Belgium
- Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, O&N 1, Leuven, Belgium
- Biomechanics Research Unit, University of Liege, Liege, Belgium
| | - Liesbet Geris
- Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, O&N 1, Leuven, Belgium
- Biomechanics Research Unit, University of Liege, Liege, Belgium
| | - Katie Bentley
- Vascular Biology Lab, Cancer Research UK, London, United Kingdom
| | - Geert Carmeliet
- Clinical and Experimental Endocrinology, KU Leuven, O&N 1, Leuven, Belgium
| | - Peter Carmeliet
- Laboratory of Angiogenesis and Neurovascular link, Vesalius Research Center, VIB, Leuven, Belgium
- Laboratory of Angiogenesis and Neurovascular link, Vesalius Research Center, University of Leuven, Leuven, Belgium
| | - Hans Van Oosterwyck
- Biomechanics Section, KU Leuven, Leuven, Belgium
- Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, O&N 1, Leuven, Belgium
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