1
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Gallagher K, Creswell R, Lambert B, Robinson M, Lok Lei C, Mirams GR, Gavaghan DJ. Ten simple rules for training scientists to make better software. PLoS Comput Biol 2024; 20:e1012410. [PMID: 39264985 PMCID: PMC11392269 DOI: 10.1371/journal.pcbi.1012410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2024] Open
Affiliation(s)
- Kit Gallagher
- Doctoral Training Centre, University of Oxford, Oxford, United Kingdom
| | - Richard Creswell
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ben Lambert
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Chon Lok Lei
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macau, China
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - David J Gavaghan
- Doctoral Training Centre, University of Oxford, Oxford, United Kingdom
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2
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Colebank MJ, Oomen PA, Witzenburg CM, Grosberg A, Beard DA, Husmeier D, Olufsen MS, Chesler NC. Guidelines for mechanistic modeling and analysis in cardiovascular research. Am J Physiol Heart Circ Physiol 2024; 327:H473-H503. [PMID: 38904851 DOI: 10.1152/ajpheart.00766.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 06/07/2024] [Accepted: 06/16/2024] [Indexed: 06/22/2024]
Abstract
Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.
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Affiliation(s)
- Mitchel J Colebank
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Pim A Oomen
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Colleen M Witzenburg
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Anna Grosberg
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Daniel A Beard
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, United States
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
| | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, North Carolina, United States
| | - Naomi C Chesler
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
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3
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Sarkar T, Krajnc M. Graph topological transformations in space-filling cell aggregates. PLoS Comput Biol 2024; 20:e1012089. [PMID: 38743660 PMCID: PMC11093388 DOI: 10.1371/journal.pcbi.1012089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/19/2024] [Indexed: 05/16/2024] Open
Abstract
Cell rearrangements are fundamental mechanisms driving large-scale deformations of living tissues. In three-dimensional (3D) space-filling cell aggregates, cells rearrange through local topological transitions of the network of cell-cell interfaces, which is most conveniently described by the vertex model. Since these transitions are not yet mathematically properly formulated, the 3D vertex model is generally difficult to implement. The few existing implementations rely on highly customized and complex software-engineering solutions, which cannot be transparently delineated and are thus mostly non-reproducible. To solve this outstanding problem, we propose a reformulation of the vertex model. Our approach, called Graph Vertex Model (GVM), is based on storing the topology of the cell network into a knowledge graph with a particular data structure that allows performing cell-rearrangement events by simple graph transformations. Importantly, when these same transformations are applied to a two-dimensional (2D) polygonal cell aggregate, they reduce to a well-known T1 transition, thereby generalizing cell-rearrangements in 2D and 3D space-filling packings. This result suggests that the GVM's graph data structure may be the most natural representation of cell aggregates and tissues. We also develop a Python package that implements GVM, relying on a graph-database-management framework Neo4j. We use this package to characterize an order-disorder transition in 3D cell aggregates, driven by active noise and we find aggregates undergoing efficient ordering close to the transition point. In all, our work showcases knowledge graphs as particularly suitable data models for structured storage, analysis, and manipulation of tissue data.
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Affiliation(s)
- Tanmoy Sarkar
- Department of Theoretical Physics, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Matej Krajnc
- Department of Theoretical Physics, Jožef Stefan Institute, Ljubljana, Slovenia
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4
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Colyer B, Bak M, Basanta D, Noble R. A seven-step guide to spatial, agent-based modelling of tumour evolution. Evol Appl 2024; 17:e13687. [PMID: 38707992 PMCID: PMC11064804 DOI: 10.1111/eva.13687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 05/07/2024] Open
Abstract
Spatial agent-based models are frequently used to investigate the evolution of solid tumours subject to localized cell-cell interactions and microenvironmental heterogeneity. As spatial genomic, transcriptomic and proteomic technologies gain traction, spatial computational models are predicted to become ever more necessary for making sense of complex clinical and experimental data sets, for predicting clinical outcomes, and for optimizing treatment strategies. Here we present a non-technical step by step guide to developing such a model from first principles. Stressing the importance of tailoring the model structure to that of the biological system, we describe methods of increasing complexity, from the basic Eden growth model up to off-lattice simulations with diffusible factors. We examine choices that unavoidably arise in model design, such as implementation, parameterization, visualization and reproducibility. Each topic is illustrated with examples drawn from recent research studies and state of the art modelling platforms. We emphasize the benefits of simpler models that aim to match the complexity of the phenomena of interest, rather than that of the entire biological system. Our guide is aimed at both aspiring modellers and other biologists and oncologists who wish to understand the assumptions and limitations of the models on which major cancer studies now so often depend.
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Affiliation(s)
- Blair Colyer
- Department of MathematicsCity, University of LondonLondonUK
| | - Maciej Bak
- Department of MathematicsCity, University of LondonLondonUK
| | - David Basanta
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFloridaUSA
| | - Robert Noble
- Department of MathematicsCity, University of LondonLondonUK
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5
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Ko JM, Reginato W, Wolff A, Lobo D. Mechanistic regulation of planarian shape during growth and degrowth. Development 2024; 151:dev202353. [PMID: 38619319 PMCID: PMC11128284 DOI: 10.1242/dev.202353] [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: 09/15/2023] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Adult planarians can grow when fed and degrow (shrink) when starved while maintaining their whole-body shape. It is unknown how the morphogens patterning the planarian axes are coordinated during feeding and starvation or how they modulate the necessary differential tissue growth or degrowth. Here, we investigate the dynamics of planarian shape together with a theoretical study of the mechanisms regulating whole-body proportions and shape. We found that the planarian body proportions scale isometrically following similar linear rates during growth and degrowth, but that fed worms are significantly wider than starved worms. By combining a descriptive model of planarian shape and size with a mechanistic model of anterior-posterior and medio-lateral signaling calibrated with a novel parameter optimization methodology, we theoretically demonstrate that the feedback loop between these positional information signals and the shape they control can regulate the planarian whole-body shape during growth. Furthermore, the computational model produced the correct shape and size dynamics during degrowth as a result of a predicted increase in apoptosis rate and pole signal during starvation. These results offer mechanistic insights into the dynamic regulation of whole-body morphologies.
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Affiliation(s)
- Jason M. Ko
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Waverly Reginato
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Andrew Wolff
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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6
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Simpson MJ, Murphy KM, McCue SW, Buenzli PR. Discrete and continuous mathematical models of sharp-fronted collective cell migration and invasion. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240126. [PMID: 39076824 PMCID: PMC11286127 DOI: 10.1098/rsos.240126] [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: 01/22/2024] [Accepted: 02/22/2024] [Indexed: 07/31/2024]
Abstract
Mathematical models describing the spatial spreading and invasion of populations of biological cells are often developed in a continuum modelling framework using reaction-diffusion equations. While continuum models based on linear diffusion are routinely employed and known to capture key experimental observations, linear diffusion fails to predict well-defined sharp fronts that are often observed experimentally. This observation has motivated the use of nonlinear degenerate diffusion; however, these nonlinear models and the associated parameters lack a clear biological motivation and interpretation. Here, we take a different approach by developing a stochastic discrete lattice-based model incorporating biologically inspired mechanisms and then deriving the reaction-diffusion continuum limit. Inspired by experimental observations, agents in the simulation deposit extracellular material, which we call a substrate, locally onto the lattice, and the motility of agents is taken to be proportional to the substrate density. Discrete simulations that mimic a two-dimensional circular barrier assay illustrate how the discrete model supports both smooth and sharp-fronted density profiles depending on the rate of substrate deposition. Coarse-graining the discrete model leads to a novel partial differential equation (PDE) model whose solution accurately approximates averaged data from the discrete model. The new discrete model and PDE approximation provide a simple, biologically motivated framework for modelling the spreading, growth and invasion of cell populations with well-defined sharp fronts. Open-source Julia code to replicate all results in this work is available on GitHub.
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Affiliation(s)
- Matthew J. Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Keeley M. Murphy
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Scott W. McCue
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Pascal R. Buenzli
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
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7
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Kolokotroni E, Abler D, Ghosh A, Tzamali E, Grogan J, Georgiadi E, Büchler P, Radhakrishnan R, Byrne H, Sakkalis V, Nikiforaki K, Karatzanis I, McFarlane NJB, Kaba D, Dong F, Bohle RM, Meese E, Graf N, Stamatakos G. A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin. J Pers Med 2024; 14:475. [PMID: 38793058 PMCID: PMC11122096 DOI: 10.3390/jpm14050475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| | - Daniel Abler
- Department of Oncology, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland;
- Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Eleftheria Tzamali
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - James Grogan
- Irish Centre for High End Computing, University of Galway, H91 TK33 Galway, Ireland;
| | - Eleni Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
- Biomedical Engineering Department, University of West Attica, 12243 Egaleo, Greece
| | | | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK;
| | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | | | - Djibril Kaba
- Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;
| | - Feng Dong
- Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK;
| | - Rainer M. Bohle
- Department of Pathology, Saarland University, 66421 Homburg, Germany;
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany;
| | - Norbert Graf
- Department of Paediatric Oncology and Haematology, Saarland University, 66421 Homburg, Germany;
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
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8
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Kumar N, Rangel Ambriz J, Tsai K, Mim MS, Flores-Flores M, Chen W, Zartman JJ, Alber M. Balancing competing effects of tissue growth and cytoskeletal regulation during Drosophila wing disc development. Nat Commun 2024; 15:2477. [PMID: 38509115 PMCID: PMC10954670 DOI: 10.1038/s41467-024-46698-7] [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: 09/28/2022] [Accepted: 03/06/2024] [Indexed: 03/22/2024] Open
Abstract
How a developing organ robustly coordinates the cellular mechanics and growth to reach a final size and shape remains poorly understood. Through iterations between experiments and model simulations that include a mechanistic description of interkinetic nuclear migration, we show that the local curvature, height, and nuclear positioning of cells in the Drosophila wing imaginal disc are defined by the concurrent patterning of actomyosin contractility, cell-ECM adhesion, ECM stiffness, and interfacial membrane tension. We show that increasing cell proliferation via different growth-promoting pathways results in two distinct phenotypes. Triggering proliferation through insulin signaling increases basal curvature, but an increase in growth through Dpp signaling and Myc causes tissue flattening. These distinct phenotypic outcomes arise from differences in how each growth pathway regulates the cellular cytoskeleton, including contractility and cell-ECM adhesion. The coupled regulation of proliferation and cytoskeletal regulators is a general strategy to meet the multiple context-dependent criteria defining tissue morphogenesis.
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Affiliation(s)
- Nilay Kumar
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Jennifer Rangel Ambriz
- Department of Mathematics, University of California, Riverside, CA, USA
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, USA
| | - Kevin Tsai
- Department of Mathematics, University of California, Riverside, CA, USA
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, USA
| | - Mayesha Sahir Mim
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Marycruz Flores-Flores
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Weitao Chen
- Department of Mathematics, University of California, Riverside, CA, USA
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, USA
| | - Jeremiah J Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA.
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
| | - Mark Alber
- Department of Mathematics, University of California, Riverside, CA, USA.
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, USA.
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9
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Cumming T, Levayer R. Toward a predictive understanding of epithelial cell death. Semin Cell Dev Biol 2024; 156:44-57. [PMID: 37400292 DOI: 10.1016/j.semcdb.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/15/2023] [Accepted: 06/22/2023] [Indexed: 07/05/2023]
Abstract
Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.
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Affiliation(s)
- Tom Cumming
- Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, 25 rue du Dr. Roux, 75015 Paris, France; Sorbonne Université, Collège Doctoral, F75005 Paris, France
| | - Romain Levayer
- Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, 25 rue du Dr. Roux, 75015 Paris, France.
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10
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Ma T, Liu X, Su H, Shi Q, He Y, Wu F, Gao C, Li K, Liang Z, Zhang D, Zhang X, Hu K, Li S, Wang L, Wang M, Yue S, Hong W, Chen X, Zhang J, Zheng L, Deng X, Wang P, Fan Y. Coupling of Perinuclear Actin Cap and Nuclear Mechanics in Regulating Flow-Induced Yap Spatiotemporal Nucleocytoplasmic Transport. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305867. [PMID: 38161226 PMCID: PMC10953556 DOI: 10.1002/advs.202305867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/10/2023] [Indexed: 01/03/2024]
Abstract
Mechanical forces, including flow shear stress, govern fundamental cellular processes by modulating nucleocytoplasmic transport of transcription factors like Yes-associated Protein (YAP). However, the underlying mechanical mechanism remains elusive. In this study, it is reported that unidirectional flow induces biphasic YAP transport with initial nuclear import, followed by nuclear export as actin cap formation and nuclear stiffening. Conversely, pathological oscillatory flow induces slight actin cap formation, nuclear softening, and sustained YAP nuclear localization. To elucidate the disparately YAP spatiotemporal distribution, a 3D mechanochemical model is developed, which integrates flow sensing, cytoskeleton organization, nucleus mechanotransduction, and YAP transport. The results unveiled that despite the significant localized nuclear stress imposed by the actin cap, its inherent stiffness counteracts the dispersed contractile stress exerted by conventional fibers on the nuclear membrane. Moreover, alterations in nuclear stiffness synergistically regulate nuclear deformation, thereby governing YAP transport. Furthermore, by expanding the single-cell model to a collective vertex framework, it is revealed that the irregularities in actin cap formation within individual cells have the potential to induce topological defects and spatially heterogeneous YAP distribution in the cellular monolayer. This work unveils a unified mechanism of flow-induced nucleocytoplasmic transport, providing a linkage between transcription factor localization and mechanical stimulation.
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Affiliation(s)
- Tianxiang Ma
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Xiao Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Haoran Su
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Qiusheng Shi
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Yuan He
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Fan Wu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Chenxing Gao
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Kexin Li
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Zhuqing Liang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Dongrui Zhang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Xing Zhang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Ke Hu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Shangyu Li
- Biomedical Pioneering Innovation Center (BIOPIC)Peking UniversityBeijing100871China
- Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China
| | - Li Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Min Wang
- Department of Gynecology and ObstetricsStrategic Support Force Medical CenterBeijing100101China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Weili Hong
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Jing Zhang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Lisha Zheng
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Xiaoyan Deng
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Pu Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
| | - Yubo Fan
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijing100083China
- School of Engineering MedicineBeihang UniversityBeijing100083China
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11
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Crossley RM, Johnson S, Tsingos E, Bell Z, Berardi M, Botticelli M, Braat QJS, Metzcar J, Ruscone M, Yin Y, Shuttleworth R. Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist. Front Cell Dev Biol 2024; 12:1354132. [PMID: 38495620 PMCID: PMC10940354 DOI: 10.3389/fcell.2024.1354132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/12/2024] [Indexed: 03/19/2024] Open
Abstract
The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.
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Affiliation(s)
- Rebecca M. Crossley
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Samuel Johnson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Erika Tsingos
- Computational Developmental Biology Group, Institute of Biodynamics and Biocomplexity, Utrecht University, Utrecht, Netherlands
| | - Zoe Bell
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Massimiliano Berardi
- LaserLab, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Optics11 life, Amsterdam, Netherlands
| | | | - Quirine J. S. Braat
- Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, Netherlands
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
- Department of Informatics, Indiana University, Bloomington, IN, United States
| | | | - Yuan Yin
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
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12
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Cockx BJR, Foster T, Clegg RJ, Alden K, Arya S, Stekel DJ, Smets BF, Kreft JU. Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0. PLoS Comput Biol 2024; 20:e1011303. [PMID: 38422165 PMCID: PMC10947719 DOI: 10.1371/journal.pcbi.1011303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 03/18/2024] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.
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Affiliation(s)
- Bastiaan J. R. Cockx
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Tim Foster
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Robert J. Clegg
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Kieran Alden
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Sankalp Arya
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Dov J. Stekel
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Barth F. Smets
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Jan-Ulrich Kreft
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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13
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Pak TF, Pitt-Francis J, Baker RE. A mathematical framework for the emergence of winners and losers in cell competition. J Theor Biol 2024; 577:111666. [PMID: 37956955 DOI: 10.1016/j.jtbi.2023.111666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 09/27/2023] [Accepted: 11/06/2023] [Indexed: 11/21/2023]
Abstract
Cell competition is a process in multicellular organisms where cells interact with their neighbours to determine a "winner" or "loser" status. The loser cells are eliminated through programmed cell death, leaving only the winner cells to populate the tissue. Cell competition is context-dependent; the same cell type can win or lose depending on the cell type it is competing against. Hence, winner/loser status is an emergent property. A key question in cell competition is: how do cells acquire their winner/loser status? In this paper, we propose a mathematical framework for studying the emergence of winner/loser status based on a set of quantitative criteria that distinguishes competitive from non-competitive outcomes. We apply this framework in a cell-based modelling context, to both highlight the crucial role of active cell death in cell competition and identify the factors that drive cell competition.
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Affiliation(s)
- Thomas F Pak
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
| | - Joe Pitt-Francis
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK
| | - Ruth E Baker
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
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14
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Bifulco SF, Boyle PM. Computational Modeling and Simulation of the Fibrotic Human Atria. Methods Mol Biol 2024; 2735:105-115. [PMID: 38038845 DOI: 10.1007/978-1-0716-3527-8_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Patient-specific modeling of atrial electrical activity enables the execution of simulations that can provide mechanistic insights and provide novel solutions to vexing clinical problems. The geometry and fibrotic remodeling of the heart can be reconstructed from clinical-grade medical scans and used to inform personalized models with detail incorporated at the cell- and tissue-scale to represent changes in image-identified diseased regions. Here, we provide a rubric for the reconstruction of realistic atrial models from pre-segmented 3D renderings of the left atrium with fibrotic tissue regions delineated, which are the output from clinical-grade systems for quantifying fibrosis. We then provide a roadmap for using those models to carry out patient-specific characterization of the fibrotic substrate in terms of its potential to harbor reentrant drivers via cardiac electrophysiology simulations.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA.
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15
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Geroski T, Gkaintes O, Vulović A, Ukaj N, Barrasa-Fano J, Perez-Boerema F, Milićević B, Atanasijević A, Živković J, Živić A, Roumpi M, Exarchos T, Hellmich C, Scheiner S, Van Oosterwyck H, Jakovljević D, Ivanović M, Filipović N. SGABU computational platform for multiscale modeling: Bridging the gap between education and research. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107935. [PMID: 38006682 DOI: 10.1016/j.cmpb.2023.107935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/06/2023] [Accepted: 11/18/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND AND OBJECTIVE In accordance with the latest aspirations in the field of bioengineering, there is a need to create a web accessible, but powerful cloud computational platform that combines datasets and multiscale models related to bone modeling, cancer, cardiovascular diseases and tissue engineering. The SGABU platform may become a powerful information system for research and education that can integrate data, extract information, and facilitate knowledge exchange with the goal of creating and developing appropriate computing pipelines to provide accurate and comprehensive biological information from the molecular to organ level. METHODS The datasets integrated into the platform are obtained from experimental and/or clinical studies and are mainly in tabular or image file format, including metadata. The implementation of multiscale models, is an ambitious effort of the platform to capture phenomena at different length scales, described using partial and ordinary differential equations, which are solved numerically on complex geometries with the use of the finite element method. The majority of the SGABU platform's simulation pipelines are provided as Common Workflow Language (CWL) workflows. Each of them requires creating a CWL implementation on the backend and a user-friendly interface using standard web technologies. Platform is available at https://sgabu-test.unic.kg.ac.rs/login. RESULTS The main dashboard of the SGABU platform is divided into sections for each field of research, each one of which includes a subsection of datasets and multiscale models. The datasets can be presented in a simple form as tabular data, or using technologies such as Plotly.js for 2D plot interactivity, Kitware Paraview Glance for 3D view. Regarding the models, the usage of Docker containerization for packing the individual tools and CWL orchestration for describing inputs with validation forms and outputs with tabular views for output visualization, interactive diagrams, 3D views and animations. CONCLUSIONS In practice, the structure of SGABU platform means that any of the integrated workflows can work equally well on any other bioengineering platform. The key advantage of the SGABU platform over similar efforts is its versatility offered with the use of modern, modular, and extensible technology for various levels of architecture.
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Affiliation(s)
- Tijana Geroski
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia; Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia.
| | | | - Aleksandra Vulović
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia; Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
| | - Niketa Ukaj
- Vienna University of Technology, Vienna, Austria
| | - Jorge Barrasa-Fano
- Biomechanics section, Department of Mechanical Engineering, KU Leuven, Belgium
| | | | - Bogdan Milićević
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia; Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
| | | | - Jelena Živković
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
| | - Andreja Živić
- Faculty of Science, University of Kragujevac, Kragujevac, Serbia
| | | | - Themis Exarchos
- University of Ioannina, Ioannina, Greece; Ionian University, Corfu, Greece
| | | | | | - Hans Van Oosterwyck
- Biomechanics section, Department of Mechanical Engineering, KU Leuven, Belgium
| | | | - Miloš Ivanović
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia; University of Kragujevac Computing Center, Kragujevac, Serbia; Faculty of Science, University of Kragujevac, Kragujevac, Serbia
| | - Nenad Filipović
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia; Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
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16
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Khan AK, Muñoz-Castro G, Muñoz JJ. Single and two-cells shape analysis from energy functionals for three-dimensional vertex models. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3766. [PMID: 37551449 DOI: 10.1002/cnm.3766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/09/2023]
Abstract
Vertex models have been extensively used for simulating the evolution of multicellular systems, and have given rise to important global properties concerning their macroscopic rheology or jamming transitions. These models are based on the definition of an energy functional, which fully determines the cellular response and conclusions. While two-dimensional vertex models have been widely employed, three-dimensional models are far more scarce, mainly due to the large amount of configurations that they may adopt and the complex geometrical transitions they undergo. We here investigate the shape of single and two-cells configurations as a function of the energy terms, and we study the dependence of the final shape on the model parameters: namely the exponent of the term penalising cell-cell adhesion and surface contractility. In single cell analysis, we deduce analytically the radius and limit values of the contractility for linear and quadratic surface energy terms, in 2D and 3D. In two-cells systems, symmetrical and asymmetrical, we deduce the evolution of the aspect ratio and the relative radius. While in functionals with linear surface terms yield the same aspect ratio in 2D and 3D, the configurations when using quadratic surface terms are distinct. We relate our results with well-known solutions from capillarity theory, and verify our analytical findings with a three-dimensional vertex model.
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Affiliation(s)
- Ahmad K Khan
- Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Guillem Muñoz-Castro
- Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Jose J Muñoz
- Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
- Laboratori de Càlcul Numèric (LaCàN), Universitat Politècnica de Catalunya, Barcelona, Spain
- Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Barcelona, Spain
- Institut de Matemàtiques de la UPC-BarcelonaTech (IMTech), Barcelona, Spain
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17
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Torre M, Morganti S, Pasqualini FS, Reali A. Current progress toward isogeometric modeling of the heart biophysics. BIOPHYSICS REVIEWS 2023; 4:041301. [PMID: 38510845 PMCID: PMC10903424 DOI: 10.1063/5.0152690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 10/24/2023] [Indexed: 03/22/2024]
Abstract
In this paper, we review a powerful methodology to solve complex numerical simulations, known as isogeometric analysis, with a focus on applications to the biophysical modeling of the heart. We focus on the hemodynamics, modeling of the valves, cardiac tissue mechanics, and on the simulation of medical devices and treatments. For every topic, we provide an overview of the methods employed to solve the specific numerical issue entailed by the simulation. We try to cover the complete process, starting from the creation of the geometrical model up to the analysis and post-processing, highlighting the advantages and disadvantages of the methodology.
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Affiliation(s)
- Michele Torre
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Simone Morganti
- Department of Electrical, Computer, and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy
| | - Francesco S. Pasqualini
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
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18
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Johnson JA, Stein-O’Brien GL, Booth M, Heiland R, Kurtoglu F, Bergman DR, Bucher E, Deshpande A, Forjaz A, Getz M, Godet I, Lyman M, Metzcar J, Mitchell J, Raddatz A, Rocha H, Solorzano J, Sundus A, Wang Y, Gilkes D, Kagohara LT, Kiemen AL, Thompson ED, Wirtz D, Wu PH, Zaidi N, Zheng L, Zimmerman JW, Jaffee EM, Hwan Chang Y, Coussens LM, Gray JW, Heiser LM, Fertig EJ, Macklin P. Digitize your Biology! Modeling multicellular systems through interpretable cell behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.557982. [PMID: 37745323 PMCID: PMC10516032 DOI: 10.1101/2023.09.17.557982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.
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Affiliation(s)
- Jeanette A.I. Johnson
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Genevieve L. Stein-O’Brien
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins University. Baltimore, MD USA
| | - Max Booth
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Randy Heiland
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Furkan Kurtoglu
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Daniel R. Bergman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elmar Bucher
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - André Forjaz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Michael Getz
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Ines Godet
- Memorial Sloan Kettering Cancer Center. New York, NY USA
| | - Melissa Lyman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
- Department of Informatics, Indiana University. Bloomington, IN USA
| | - Jacob Mitchell
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Human Genetics, Johns Hopkins University. Baltimore, MD USA
| | - Andrew Raddatz
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University. Atlanta, GA USA
| | - Heber Rocha
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Jacobo Solorzano
- Centre de Recherches en Cancerologie de Toulouse. Toulouse, France
| | - Aneequa Sundus
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Danielle Gilkes
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Luciane T. Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Ashley L. Kiemen
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
| | | | - Denis Wirtz
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
- Department of Materials Science and Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Pei-Hsun Wu
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Neeha Zaidi
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Lei Zheng
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Jacquelyn W. Zimmerman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elizabeth M. Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Lisa M. Coussens
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University. Portland, OR USA
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Laura M. Heiser
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Elana J. Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
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19
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Alamoudi E, Schälte Y, Müller R, Starruß J, Bundgaard N, Graw F, Brusch L, Hasenauer J. FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes. Bioinformatics 2023; 39:btad674. [PMID: 37947308 PMCID: PMC10666203 DOI: 10.1093/bioinformatics/btad674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023] Open
Abstract
MOTIVATION Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. RESULTS Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AVAILABILITY AND IMPLEMENTATION FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.
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Affiliation(s)
- Emad Alamoudi
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
| | - Yannik Schälte
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany
| | - Robert Müller
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Jörn Starruß
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Nils Bundgaard
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg 69120, Germany
| | - Frederik Graw
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg 69120, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg 69120, Germany
- Department of Medicine 5, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen 91054, Germany
| | - Lutz Brusch
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden 01062, Germany
| | - Jan Hasenauer
- Life and Medical Sciences Institute, University of Bonn, Bonn 53113, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany
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20
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Africa PC, Piersanti R, Regazzoni F, Bucelli M, Salvador M, Fedele M, Pagani S, Dede' L, Quarteroni A. lifex-ep: a robust and efficient software for cardiac electrophysiology simulations. BMC Bioinformatics 2023; 24:389. [PMID: 37828428 PMCID: PMC10571323 DOI: 10.1186/s12859-023-05513-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. RESULTS This work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. CONCLUSIONS [Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations.
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Affiliation(s)
- Pasquale Claudio Africa
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- mathLab, Mathematics Area, SISSA International School for Advanced Studies, Trieste, Italy
| | - Roberto Piersanti
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy.
| | | | - Michele Bucelli
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Matteo Salvador
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA
| | - Marco Fedele
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Stefano Pagani
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Luca Dede'
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Alfio Quarteroni
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Professor emeritus, Switzerland
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21
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Germano DPJ, Zanca A, Johnston ST, Flegg JA, Osborne JM. Free and Interfacial Boundaries in Individual-Based Models of Multicellular Biological systems. Bull Math Biol 2023; 85:111. [PMID: 37805982 PMCID: PMC10560655 DOI: 10.1007/s11538-023-01214-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023]
Abstract
Coordination of cell behaviour is key to a myriad of biological processes including tissue morphogenesis, wound healing, and tumour growth. As such, individual-based computational models, which explicitly describe inter-cellular interactions, are commonly used to model collective cell dynamics. However, when using individual-based models, it is unclear how descriptions of cell boundaries affect overall population dynamics. In order to investigate this we define three cell boundary descriptions of varying complexities for each of three widely used off-lattice individual-based models: overlapping spheres, Voronoi tessellation, and vertex models. We apply our models to multiple biological scenarios to investigate how cell boundary description can influence tissue-scale behaviour. We find that the Voronoi tessellation model is most sensitive to changes in the cell boundary description with basic models being inappropriate in many cases. The timescale of tissue evolution when using an overlapping spheres model is coupled to the boundary description. The vertex model is demonstrated to be the most stable to changes in boundary description, though still exhibits timescale sensitivity. When using individual-based computational models one should carefully consider how cell boundaries are defined. To inform future work, we provide an exploration of common individual-based models and cell boundary descriptions in frequently studied biological scenarios and discuss their benefits and disadvantages.
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Affiliation(s)
- Domenic P. J. Germano
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - Adriana Zanca
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - Stuart T. Johnston
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - Jennifer A. Flegg
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
| | - James M. Osborne
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010 Australia
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22
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Montes-Olivas S, Legge D, Lund A, Fletcher AG, Williams AC, Marucci L, Homer M. In-silico and in-vitro morphometric analysis of intestinal organoids. PLoS Comput Biol 2023; 19:e1011386. [PMID: 37578984 PMCID: PMC10473498 DOI: 10.1371/journal.pcbi.1011386] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/01/2023] [Accepted: 07/25/2023] [Indexed: 08/16/2023] Open
Abstract
Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average number of budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.
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Affiliation(s)
- Sandra Montes-Olivas
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Danny Legge
- Colorectal Tumour Biology Group, School of Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol, United Kingdom
| | - Abbie Lund
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Alexander G. Fletcher
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
- Bateson Centre, University of Sheffield, Sheffield, United Kingdom
| | - Ann C. Williams
- Colorectal Tumour Biology Group, School of Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol, United Kingdom
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
- BrisSynBio, Bristol, United Kingdom
| | - Martin Homer
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
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23
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Du J, Zhou Y, Jin L, Sheng K. Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system. PLoS One 2023; 18:e0288721. [PMID: 37463167 DOI: 10.1371/journal.pone.0288721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop Gell (GPU Cell), a fast and memory-efficient open-source GPU-based hybrid computational modeling platform for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement.
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Affiliation(s)
- Jiayi Du
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Yu Zhou
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Lihua Jin
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Ke Sheng
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California, United States of America
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24
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Galappaththige S, Pathmanathan P, Gray RA. A computational modeling framework for pre-clinical evaluation of cardiac mapping systems. Front Physiol 2023; 14:1074527. [PMID: 37485068 PMCID: PMC10358980 DOI: 10.3389/fphys.2023.1074527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 05/31/2023] [Indexed: 07/25/2023] Open
Abstract
There are a variety of difficulties in evaluating clinical cardiac mapping systems, most notably the inability to record the transmembrane potential throughout the entire heart during patient procedures which prevents the comparison to a relevant "gold standard". Cardiac mapping systems are comprised of hardware and software elements including sophisticated mathematical algorithms, both of which continue to undergo rapid innovation. The purpose of this study is to develop a computational modeling framework to evaluate the performance of cardiac mapping systems. The framework enables rigorous evaluation of a mapping system's ability to localize and characterize (i.e., focal or reentrant) arrhythmogenic sources in the heart. The main component of our tool is a library of computer simulations of various dynamic patterns throughout the entire heart in which the type and location of the arrhythmogenic sources are known. Our framework allows for performance evaluation for various electrode configurations, heart geometries, arrhythmias, and electrogram noise levels and involves blind comparison of mapping systems against a "silver standard" comprised of computer simulations in which the precise transmembrane potential patterns throughout the heart are known. A feasibility study was performed using simulations of patterns in the human left atria and three hypothetical virtual catheter electrode arrays. Activation times (AcT) and patterns (AcP) were computed for three virtual electrode arrays: two basket arrays with good and poor contact and one high-resolution grid with uniform spacing. The average root mean squared difference of AcTs of electrograms and those of the nearest endocardial action potential was less than 1 ms and therefore appears to be a poor performance metric. In an effort to standardize performance evaluation of mapping systems a novel performance metric is introduced based on the number of AcPs identified correctly and those considered spurious as well as misclassifications of arrhythmia type; spatial and temporal localization accuracy of correctly identified patterns was also quantified. This approach provides a rigorous quantitative analysis of cardiac mapping system performance. Proof of concept of this computational evaluation framework suggests that it could help safeguard that mapping systems perform as expected as well as provide estimates of system accuracy.
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25
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Selvamani P, Chelakkot R, Nandi A, Inamdar MM. Emergence of Spatial Scales and Macroscopic Tissue Dynamics in Active Epithelial Monolayers. Cells Tissues Organs 2023; 213:269-282. [PMID: 37044075 DOI: 10.1159/000528501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 11/22/2022] [Indexed: 04/14/2023] Open
Abstract
Migrating cells in tissues are often known to exhibit collective swirling movements. In this paper, we develop an active vertex model with polarity dynamics based on contact inhibition of locomotion (CIL). We show that under this dynamics, the cells form steady-state vortices in velocity, polarity, and cell stress with length scales that depend on polarity alignment rate (ζ), self-motility (v0), and cell-cell bond tension (λ). When the ratio λ/v0 becomes larger, the tissue reaches a near jamming state because of the inability of the cells to exchange their neighbors, and the length scale associated with tissue kinematics increases. A deeper examination of this jammed state provides insights into the mechanism of sustained swirl formation under CIL rule that is governed by the feedback between cell polarities and deformations. To gain additional understanding of how active forcing governed by CIL dynamics leads to large-scale tissue dynamics, we systematically coarse-grain cell stress, polarity, and motility and show that the tissue remains polar even on larger length scales. Overall, we explore the origin of swirling patterns during collective cell migration and obtain a connection between cell-level dynamics and large-scale cellular flow patterns observed in epithelial monolayers.
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Affiliation(s)
- Padmalochini Selvamani
- Center for Research in Nanotechnology and Science, Indian Institute of Technology Bombay, Mumbai, India
| | | | - Amitabha Nandi
- Department of Physics, Indian Institute of Technology Bombay, Mumbai, India
| | - Mandar M Inamdar
- Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India
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26
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Luque LM, Carlevaro CM, Llamoza Torres CJ, Lomba E. Physics-based tissue simulator to model multicellular systems: A study of liver regeneration and hepatocellular carcinoma recurrence. PLoS Comput Biol 2023; 19:e1010920. [PMID: 36877741 PMCID: PMC10019748 DOI: 10.1371/journal.pcbi.1010920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/16/2023] [Accepted: 02/03/2023] [Indexed: 03/07/2023] Open
Abstract
We present a multiagent-based model that captures the interactions between different types of cells with their microenvironment, and enables the analysis of the emergent global behavior during tissue regeneration and tumor development. Using this model, we are able to reproduce the temporal dynamics of regular healthy cells and cancer cells, as well as the evolution of their three-dimensional spatial distributions. By tuning the system with the characteristics of the individual patients, our model reproduces a variety of spatial patterns of tissue regeneration and tumor growth, resembling those found in clinical imaging or biopsies. In order to calibrate and validate our model we study the process of liver regeneration after surgical hepatectomy in different degrees. In the clinical context, our model is able to predict the recurrence of a hepatocellular carcinoma after a 70% partial hepatectomy. The outcomes of our simulations are in agreement with experimental and clinical observations. By fitting the model parameters to specific patient factors, it might well become a useful platform for hypotheses testing in treatments protocols.
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Affiliation(s)
- Luciana Melina Luque
- Instituto de Física de Líquidos y Sistemas Biológicos - CONICET. La Plata, Argentina
- * E-mail: (LML); (CMC)
| | - Carlos Manuel Carlevaro
- Instituto de Física de Líquidos y Sistemas Biológicos - CONICET. La Plata, Argentina
- Departamento de Ingeniería Mecánica, Universidad Tecnológica Nacional, Facultad Regional La Plata, La Plata, Argentina
- * E-mail: (LML); (CMC)
| | | | - Enrique Lomba
- Instituto de Química Física Rocasolano - CSIC. Madrid, España
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27
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Bull JA, Byrne HM. Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions. PLoS Comput Biol 2023; 19:e1010994. [PMID: 36972297 PMCID: PMC10079237 DOI: 10.1371/journal.pcbi.1010994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 04/06/2023] [Accepted: 03/04/2023] [Indexed: 03/29/2023] Open
Abstract
We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the 'three Es of cancer immunoediting': Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a 'human readable' statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct 'PCF signature' that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples.
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Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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28
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Gonçalves IG, García-Aznar JM. Hybrid computational models of multicellular tumour growth considering glucose metabolism. Comput Struct Biotechnol J 2023; 21:1262-1271. [PMID: 36814723 PMCID: PMC9939553 DOI: 10.1016/j.csbj.2023.01.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.
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Key Words
- ABM, agent-based model
- ATP, adenosine triphosphate
- CA, cellular automata
- CPM, cellular Potts model
- ECM, extracellular matrix
- FBA, Flux Balance Analysis
- FDG-PET, [18F]-fluorodeoxyglucose-positron emission tomography
- MCTS, multicellular tumour spheroids
- ODEs, ordinary differential equations
- PDEs, partial differential equations
- SBML, Systems Biology Markup Language
- Warburg effect
- agent-based models
- glucose metabolism
- hybrid modelling
- multicellular simulations
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Affiliation(s)
- Inês G Gonçalves
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Aragon, Spain
| | - José Manuel García-Aznar
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Aragon, Spain
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29
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Novel Ground-Up 3D Multicellular Simulators for Synthetic Biology CAD Integrating Stochastic Gillespie Simulations Benchmarked with Topologically Variable SBML Models. Genes (Basel) 2023; 14:genes14010154. [PMID: 36672895 PMCID: PMC9859520 DOI: 10.3390/genes14010154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023] Open
Abstract
The elevation of Synthetic Biology from single cells to multicellular simulations would be a significant scale-up. The spatiotemporal behavior of cellular populations has the potential to be prototyped in silico for computer assisted design through ergonomic interfaces. Such a platform would have great practical potential across medicine, industry, research, education and accessible archiving in bioinformatics. Existing Synthetic Biology CAD systems are considered limited regarding population level behavior, and this work explored the in silico challenges posed from biological and computational perspectives. Retaining the connection to Synthetic Biology CAD, an extension of the Infobiotics Workbench Suite was considered, with potential for the integration of genetic regulatory models and/or chemical reaction networks through Next Generation Stochastic Simulator (NGSS) Gillespie algorithms. These were executed using SBML models generated by in-house SBML-Constructor over numerous topologies and benchmarked in association with multicellular simulation layers. Regarding multicellularity, two ground-up multicellular solutions were developed, including the use of Unreal Engine 4 contrasted with CPU multithreading and Blender visualization, resulting in a comparison of real-time versus batch-processed simulations. In conclusion, high-performance computing and client-server architectures could be considered for future works, along with the inclusion of numerous biologically and physically informed features, whilst still pursuing ergonomic solutions.
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30
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Fuji K, Tanida S, Sano M, Nonomura M, Riveline D, Honda H, Hiraiwa T. Computational approaches for simulating luminogenesis. Semin Cell Dev Biol 2022; 131:173-185. [PMID: 35773151 DOI: 10.1016/j.semcdb.2022.05.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 12/14/2022]
Abstract
Lumens, liquid-filled cavities surrounded by polarized tissue cells, are elementary units involved in the morphogenesis of organs. Theoretical modeling and computations, which can integrate various factors involved in biophysics of morphogenesis of cell assembly and lumens, may play significant roles to elucidate the mechanisms in formation of such complex tissue with lumens. However, up to present, it has not been documented well what computational approaches or frameworks can be applied for this purpose and how we can choose the appropriate approach for each problem. In this review, we report some typical lumen morphologies and basic mechanisms for the development of lumens, focusing on three keywords - mechanics, hydraulics and geometry - while outlining pros and cons of the current main computational strategies. We also describe brief guidance of readouts, i.e., what we should measure in experiments to make the comparison with the model's assumptions and predictions.
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Affiliation(s)
- Kana Fuji
- Universal Biology Institute, Graduate School of Science, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Sakurako Tanida
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan
| | - Masaki Sano
- Institute of Natural Sciences, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Makiko Nonomura
- Department of Mathematical Information Engineering, College of Industrial Technology, Nihon University, 1-2-1 Izumicho, Narashino-shi, Chiba 275-8575, Japan
| | - Daniel Riveline
- Laboratory of Cell Physics IGBMC, CNRS, INSERM and Université de Strasbourg, Strasbourg, France
| | - Hisao Honda
- Division of Cell Physiology, Department of Physiology and Cell Biology, Graduate School of Medicine Kobe University, Kobe, Hyogo, Japan
| | - Tetsuya Hiraiwa
- Mechanobiology Institute, Singapore, National University of Singapore, 117411, Singapore.
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31
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Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System. Bioengineering (Basel) 2022; 9:bioengineering9100543. [PMID: 36290510 PMCID: PMC9598639 DOI: 10.3390/bioengineering9100543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/20/2022] [Accepted: 10/07/2022] [Indexed: 12/03/2022] Open
Abstract
The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin–Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.
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32
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Galappaththige S, Gray RA, Costa CM, Niederer S, Pathmanathan P. Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar. PLoS Comput Biol 2022; 18:e1010541. [PMID: 36215228 PMCID: PMC9550052 DOI: 10.1371/journal.pcbi.1010541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/02/2022] [Indexed: 11/07/2022] Open
Abstract
Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and “every-patient” error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility. Patient-specific models are computational models that have been personalized using data from a patient. After decades of research, recent computational, data science and healthcare advances have opened the door to the fulfilment of the enormous potential of such models, from truly personalized medicine to efficient and cost-effective testing of new medical products. However, reliability (credibility) of patient-specific models is key to their success, and there are currently no general guidelines for evaluating credibility of patient-specific models. Here, we consider how frameworks and model evaluation activities that have been developed for generic (not patient-specific) computational models, can be extended to patient specific models. We achieve this through a detailed analysis of the activities required to evaluate cardiac electrophysiological models, chosen as an exemplar field due to its maturity and the complexity of such models. This is the first paper on the topic of reliability of patient-specific models and will help pave the way to reliable and trusted patient-specific modeling across healthcare applications.
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Affiliation(s)
- Suran Galappaththige
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Richard A. Gray
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Caroline Mendonca Costa
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
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Senthilkumar I, Howley E, McEvoy E. Thermodynamically-motivated chemo-mechanical models and multicellular simulation to provide new insight into active cell and tumour remodelling. Exp Cell Res 2022; 419:113317. [PMID: 36028058 DOI: 10.1016/j.yexcr.2022.113317] [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: 02/01/2022] [Revised: 07/19/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022]
Abstract
Computational models can shape our understanding of cell and tissue remodelling, from cell spreading, to active force generation, adhesion, and growth. In this mini-review, we discuss recent progress in modelling of chemo-mechanical cell behaviour and the evolution of multicellular systems. In particular, we highlight recent advances in (i) free-energy based single cell models that can provide new fundamental insight into cell spreading, cancer cell invasion, stem cell differentiation, and remodelling in disease, and (ii) mechanical agent-based models to simulate large numbers of discrete interacting cells in proliferative tumours. We describe how new biological understanding has emerged from such theoretical models, and the trade-offs and constraints associated with current approaches. Ultimately, we aim to make a case for why theory should be integrated with an experimental workflow to optimise new in-vitro studies, to predict feedback between cells and their microenvironment, and to deepen understanding of active cell behaviour.
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Affiliation(s)
- Irish Senthilkumar
- School of Computer Science, College of Science and Engineering, National University of Ireland Galway, Ireland; Biomedical Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland
| | - Enda Howley
- School of Computer Science, College of Science and Engineering, National University of Ireland Galway, Ireland
| | - Eoin McEvoy
- Biomedical Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland.
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Miller C, Crampin E, Osborne JM. Multiscale modelling of desquamation in the interfollicular epidermis. PLoS Comput Biol 2022; 18:e1010368. [PMID: 36037236 PMCID: PMC9462764 DOI: 10.1371/journal.pcbi.1010368] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/09/2022] [Accepted: 07/08/2022] [Indexed: 11/19/2022] Open
Abstract
Maintenance of epidermal thickness is critical to the barrier function of the skin. Decreased tissue thickness, specifically in the stratum corneum (the outermost layer of the tissue), causes discomfort and inflammation, and is related to several severe diseases of the tissue. In order to maintain both stratum corneum thickness and overall tissue thickness it is necessary for the system to balance cell proliferation and cell loss. Cell proliferation in the epidermis occurs in the basal layer and causes constant upwards movement in the tissue. Cell loss occurs when dead cells at the top of the tissue are lost to the environment through a process called desquamation. Desquamation is thought to occur through a gradual reduction in adhesion between cells, due to the cleaving of adhesion proteins by enzymes, in the stratum corneum. In this paper we will investigate combining a (mass action) subcellular model of desquamation with a three dimensional (cell centre based) multicellular model of the interfollicular epidermis to better understand maintenance of epidermal thickness. Specifically, our aim is to determine if a hypothesised biological model for the degradation of cell-cell adhesion, from the literature, is sufficient to maintain a steady state tissue thickness. These investigations show the model is able to provide a consistent rate of cell loss in the multicellular model. This loss balances proliferation, and hence maintains a homeostatic tissue thickness. Moreover, we find that multiple proliferative cell populations in the basal layer can be represented by a single proliferative cell population, simplifying investigations with this model. The model is used to investigate a disorder (Netherton Syndrome) which disrupts desquamation. The model shows how biochemical changes can cause disruptions to the tissue, resulting in a reduced tissue thickness and consequently diminishing the protective role of the tissue. A hypothetical treatment result is also investigated: we compare the cases of a partially effective homogeneous treatment (where all cells partially recover) and a totally effective heterogeneous treatment (in which a proportion of the cells totally recover) with the aim to determine the difference in the response of the tissue to these different scenarios. Results show an increased benefit to corneum thickness from the heterogeneous treatment over the homogeneous treatment.
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Affiliation(s)
- Claire Miller
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- Computational Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Edmund Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- School of Medicine, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
| | - James M. Osborne
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
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Niarakis A, Waltemath D, Glazier J, Schreiber F, Keating SM, Nickerson D, Chaouiya C, Siegel A, Noël V, Hermjakob H, Helikar T, Soliman S, Calzone L. Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology. Brief Bioinform 2022; 23:bbac212. [PMID: 35671510 PMCID: PMC9294410 DOI: 10.1093/bib/bbac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/20/2022] [Accepted: 05/06/2022] [Indexed: 11/14/2022] Open
Abstract
Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.
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Affiliation(s)
- Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Dagmar Waltemath
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - James Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Anne Siegel
- Univ Rennes, CNRS, Inria - IRISA lab. Rennes
| | - Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Henning Hermjakob
- EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
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36
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Angaroni F, Guidi A, Ascolani G, d'Onofrio A, Antoniotti M, Graudenzi A. J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments. BMC Bioinformatics 2022; 23:269. [PMID: 35804300 PMCID: PMC9270769 DOI: 10.1186/s12859-022-04779-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/09/2022] [Indexed: 11/15/2022] Open
Abstract
Background The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. Result We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. Conclusion J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl.
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Affiliation(s)
- Fabrizio Angaroni
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.
| | - Alessandro Guidi
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy
| | - Gianluca Ascolani
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy
| | - Alberto d'Onofrio
- Department of Mathematics and Geosciences, Univ. of Trieste, Trieste, Italy
| | - Marco Antoniotti
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.,Bicocca Bioinformatics, Biostatistics and Bioimaging Centre (B4), Milan, Italy
| | - Alex Graudenzi
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.,Bicocca Bioinformatics, Biostatistics and Bioimaging Centre (B4), Milan, Italy.,Inst. of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy
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37
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Avci R, Wickens JD, Sangi M, Athavale ON, Di Natale MR, Furness JB, Du P, Cheng LK. A Computational Model of Biophysical Properties of the Rat Stomach Informed by Comprehensive Analysis of Muscle Anatomy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4954-4957. [PMID: 36085865 DOI: 10.1109/embc48229.2022.9871314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
An anatomically based 3D computational model of the rat stomach was developed using experimental muscle thickness measurements and muscle fiber orientations for the longitudinal muscle (LM) and circular muscle (CM) layers. First, 15 data points corresponding to the measurements were registered on the dorsal and ventral faces of the serosal surface of an averaged 3D rat stomach model. A thickness field representing the varying wall thickness was fitted to the surface and nodal points were projected outwards (for the LM layer) and inwards (for the CM layer) to create 2 new surfaces. In addition, a computational volume mesh was created and fiber orientation in each tetrahedral element was computed using a Laplace-Dirichlet rule-based algorithm and a simulation was performed to validate the model. The stomach model successfully represented the experimental measurements with a thickness in the range of 11.7-52.9 µm and 40.6-276.5 µm in the LM and CM layers, respectively, while the variation across the stomach was in agreement with the reported values. Similarly, the generated fiber orientations matched with the investigated fiber data and successfully resembled the observed properties such as the hairpin-like structure formed by the LM fibers in the fundus. Bioelectrical simulation using the developed model was successfully converged and reflected the properties of normal antegrade activity. In conclusion, a 3D computational model of the rat stomach was successfully developed and tested for in-silico studies. The model will be used in future studies to assess parameters in electrical therapies and to investigate the structure-function relationship in gastric motility. Clinical Relevance - Electrical stimulation is an emerging therapy for functional motility disorders. The 3D model of rat stomach developed in this study could provide accurate assessment of the efficacy of a vast range of stimulation parameters via in-silico studies and could aid in the adaptation of electrical therapies to clinical settings.
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Johnson CGM, Fletcher AG, Soyer OS. ChemChaste: Simulating spatially inhomogeneous biochemical reaction-diffusion systems for modeling cell-environment feedbacks. Gigascience 2022; 11:giac051. [PMID: 35715874 PMCID: PMC9205757 DOI: 10.1093/gigascience/giac051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 03/31/2022] [Accepted: 05/30/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Spatial organization plays an important role in the function of many biological systems, from cell fate specification in animal development to multistep metabolic conversions in microbial communities. The study of such systems benefits from the use of spatially explicit computational models that combine a discrete description of cells with a continuum description of one or more chemicals diffusing within a surrounding bulk medium. These models allow the in silico testing and refinement of mechanistic hypotheses. However, most existing models of this type do not account for concurrent bulk and intracellular biochemical reactions and their possible coupling. CONCLUSIONS Here, we describe ChemChaste, an extension for the open-source C++ computational biology library Chaste. ChemChaste enables the spatial simulation of both multicellular and bulk biochemistry by expanding on Chaste's existing capabilities. In particular, ChemChaste enables (i) simulation of an arbitrary number of spatially diffusing chemicals, (ii) spatially heterogeneous chemical diffusion coefficients, and (iii) inclusion of both bulk and intracellular biochemical reactions and their coupling. ChemChaste also introduces a file-based interface that allows users to define the parameters relating to these functional features without the need to interact directly with Chaste's core C++ code. We describe ChemChaste and demonstrate its functionality using a selection of chemical and biochemical exemplars, with a focus on demonstrating increased ability in modeling bulk chemical reactions and their coupling with intracellular reactions. AVAILABILITY AND IMPLEMENTATION ChemChaste version 1.0 is a free, open-source C++ library, available via GitHub at https://github.com/OSS-Lab/ChemChaste under the BSD license, on the Zenodo archive at zendodo doi, as well as on BioTools (biotools:chemchaste) and SciCrunch (RRID:SCR022208) databases.
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Affiliation(s)
- Connah G M Johnson
- Mathematics of Real-World Systems Doctoral Training Centre, University of Warwick, Coventry, CV35 9EF, UK
- School of Life Sciences, University of Warwick, Coventry, CV35 9EF, UK
| | - Alexander G Fletcher
- School of Mathematics & Statistics, University of Sheffield, Sheffield, S3 7RH, UK
- Bateson Centre, University of Sheffield, Sheffield, S10 2TN, UK
| | - Orkun S Soyer
- School of Life Sciences, University of Warwick, Coventry, CV35 9EF, UK
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Guidance by followers ensures long-range coordination of cell migration through α-catenin mechanoperception. Dev Cell 2022; 57:1529-1544.e5. [PMID: 35613615 DOI: 10.1016/j.devcel.2022.05.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 03/09/2022] [Accepted: 05/02/2022] [Indexed: 11/23/2022]
Abstract
Morphogenesis, wound healing, and some cancer metastases depend upon the migration of cell collectives that need to be guided to their destination as well as coordinated with other cell movements. During zebrafish gastrulation, the extension of the embryonic axis is led by the mesendodermal polster that migrates toward the animal pole, followed by the axial mesoderm that undergoes convergence and extension. Here, we investigate how polster cells are guided toward the animal pole. Using a combination of precise laser ablations, advanced transplants, and functional as well as in silico approaches, we establish that each polster cell is oriented by its immediate follower cells. Each cell perceives the migration of followers, through E-cadherin/α-catenin mechanotransduction, and aligns with them. Therefore, directional information propagates from cell to cell over the whole tissue. Such guidance of migrating cells by followers ensures long-range coordination of movements and developmental robustness.
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40
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Jayasinghe MK, Lee CY, Tran TTT, Tan R, Chew SM, Yeo BZJ, Loh WX, Pirisinu M, Le MTN. The Role of in silico Research in Developing Nanoparticle-Based Therapeutics. Front Digit Health 2022; 4:838590. [PMID: 35373184 PMCID: PMC8965754 DOI: 10.3389/fdgth.2022.838590] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 02/16/2022] [Indexed: 12/12/2022] Open
Abstract
Nanoparticles (NPs) hold great potential as therapeutics, particularly in the realm of drug delivery. They are effective at functional cargo delivery and offer a great degree of amenability that can be used to offset toxic side effects or to target drugs to specific regions in the body. However, there are many challenges associated with the development of NP-based drug formulations that hamper their successful clinical translation. Arguably, the most significant barrier in the way of efficacious NP-based drug delivery systems is the tedious and time-consuming nature of NP formulation—a process that needs to account for downstream effects, such as the onset of potential toxicity or immunogenicity, in vivo biodistribution and overall pharmacokinetic profiles, all while maintaining desirable therapeutic outcomes. Computational and AI-based approaches have shown promise in alleviating some of these restrictions. Via predictive modeling and deep learning, in silico approaches have shown the ability to accurately model NP-membrane interactions and cellular uptake based on minimal data, such as the physicochemical characteristics of a given NP. More importantly, machine learning allows computational models to predict how specific changes could be made to the physicochemical characteristics of a NP to improve functional aspects, such as drug retention or endocytosis. On a larger scale, they are also able to predict the in vivo pharmacokinetics of NP-encapsulated drugs, predicting aspects such as circulatory half-life, toxicity, and biodistribution. However, the convergence of nanomedicine and computational approaches is still in its infancy and limited in its applicability. The interactions between NPs, the encapsulated drug and the body form an intricate network of interactions that cannot be modeled with absolute certainty. Despite this, rapid advancements in the area promise to deliver increasingly powerful tools capable of accelerating the development of advanced nanoscale therapeutics. Here, we describe computational approaches that have been utilized in the field of nanomedicine, focusing on approaches for NP design and engineering.
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Affiliation(s)
- Migara Kavishka Jayasinghe
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chang Yu Lee
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Trinh T T Tran
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Vingroup Science and Technology Scholarship Program, Vin University, Hanoi, Vietnam
| | - Rachel Tan
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Sarah Min Chew
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Brendon Zhi Jie Yeo
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Wen Xiu Loh
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Marco Pirisinu
- Jotbody (HK) Pte Limited, Hong Kong, Hong Kong SAR, China
| | - Minh T N Le
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Accurate in silico simulation of the rabbit Purkinje fiber electrophysiological assay to facilitate early pharmaceutical cardiosafety assessment: Dream or reality? J Pharmacol Toxicol Methods 2022; 115:107172. [DOI: 10.1016/j.vascn.2022.107172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/31/2022] [Accepted: 04/08/2022] [Indexed: 11/24/2022]
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42
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Donath S, Angerstein L, Gentemann L, Müller D, Seidler AE, Jesinghaus C, Bleich A, Heisterkamp A, Buettner M, Kalies S. Investigation of Colonic Regeneration via Precise Damage Application Using Femtosecond Laser-Based Nanosurgery. Cells 2022; 11:1143. [PMID: 35406708 PMCID: PMC8998079 DOI: 10.3390/cells11071143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/21/2022] [Accepted: 03/23/2022] [Indexed: 11/23/2022] Open
Abstract
Organoids represent the cellular composition of natural tissue. So called colonoids, organoids derived from colon tissue, are a good model for understanding regeneration. However, next to the cellular composition, the surrounding matrix, the cell-cell interactions, and environmental factors have to be considered. This requires new approaches for the manipulation of a colonoid. Of key interest is the precise application of localized damage and the following cellular reaction. We have established multiphoton imaging in combination with femtosecond laser-based cellular nanosurgery in colonoids to ablate single cells in the colonoids' crypts, the proliferative zones, and the differentiated zones. We observed that half of the colonoids recovered within six hours after manipulation. An invagination of the damaged cell and closing of the structure was observed. In about a third of the cases of targeted crypt damage, it caused a stop in crypt proliferation. In the majority of colonoids ablated in the crypt, the damage led to an increase in Wnt signalling, indicated via a fluorescent lentiviral biosensor. qRT-PCR analysis showed increased expression of various proliferation and Wnt-associated genes in response to damage. Our new model of probing colonoid regeneration paves the way to better understand organoid dynamics on a single cell level.
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Affiliation(s)
- Sören Donath
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
| | - Leon Angerstein
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
| | - Lara Gentemann
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
| | - Dominik Müller
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
- REBIRTH Research Center for Translational Regenerative Medicine, 30625 Hannover, Germany
| | - Anna E. Seidler
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
| | - Christian Jesinghaus
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
| | - André Bleich
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
- REBIRTH Research Center for Translational Regenerative Medicine, 30625 Hannover, Germany
- Institute for Laboratory Animal Science, Hannover Medical School, 30625 Hannover, Germany
| | - Alexander Heisterkamp
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
| | - Manuela Buettner
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
- REBIRTH Research Center for Translational Regenerative Medicine, 30625 Hannover, Germany
- Institute for Laboratory Animal Science, Hannover Medical School, 30625 Hannover, Germany
| | - Stefan Kalies
- Institute of Quantum Optics, Leibniz University Hannover, 30167 Hannover, Germany; (L.A.); (L.G.); (D.M.); (A.E.S.); (C.J.); (A.H.); (S.K.)
- Lower Saxony Center for Biomedical Engineering, Implant Research and Development (NIFE), 30625 Hannover, Germany; (A.B.); (M.B.)
- REBIRTH Research Center for Translational Regenerative Medicine, 30625 Hannover, Germany
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43
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Integrative Computational Modeling of Cardiomyocyte Calcium Handling and Cardiac Arrhythmias: Current Status and Future Challenges. Cells 2022; 11:cells11071090. [PMID: 35406654 PMCID: PMC8997666 DOI: 10.3390/cells11071090] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 12/26/2022] Open
Abstract
Cardiomyocyte calcium-handling is the key mediator of cardiac excitation-contraction coupling. In the healthy heart, calcium controls both electrical impulse propagation and myofilament cross-bridge cycling, providing synchronous and adequate contraction of cardiac muscles. However, calcium-handling abnormalities are increasingly implicated as a cause of cardiac arrhythmias. Due to the complex, dynamic and localized interactions between calcium and other molecules within a cardiomyocyte, it remains experimentally challenging to study the exact contributions of calcium-handling abnormalities to arrhythmogenesis. Therefore, multiscale computational modeling is increasingly being used together with laboratory experiments to unravel the exact mechanisms of calcium-mediated arrhythmogenesis. This article describes various examples of how integrative computational modeling makes it possible to unravel the arrhythmogenic consequences of alterations to cardiac calcium handling at subcellular, cellular and tissue levels, and discusses the future challenges on the integration and interpretation of such computational data.
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44
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Breitwieser L, Hesam A, de Montigny J, Vavourakis V, Iosif A, Jennings J, Kaiser M, Manca M, Di Meglio A, Al-Ars Z, Rademakers F, Mutlu O, Bauer R. BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics 2022; 38:453-460. [PMID: 34529036 PMCID: PMC8723141 DOI: 10.1093/bioinformatics/btab649] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 09/02/2021] [Accepted: 09/13/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. RESULTS We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. AVAILABILITY AND IMPLEMENTATION BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information. SUPPLEMENTARY INFORMATION Available at https://doi.org/10.5281/zenodo.5121618.
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Affiliation(s)
- Lukas Breitwieser
- CERN openlab, IT Department, CERN, Geneva 1211, Switzerland.,Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland
| | - Ahmad Hesam
- CERN openlab, IT Department, CERN, Geneva 1211, Switzerland.,Department of Quantum & Computer Engineering, Delft University of Technology, Delft 2628CD, The Netherlands
| | | | - Vasileios Vavourakis
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia 2109, Cyprus.,Department of Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Alexandros Iosif
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia 2109, Cyprus
| | - Jack Jennings
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.,Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.,Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK
| | - Marco Manca
- SCimPulse Foundation, Geleen 6162 BC, The Netherlands
| | | | - Zaid Al-Ars
- Department of Quantum & Computer Engineering, Delft University of Technology, Delft 2628CD, The Netherlands
| | | | - Onur Mutlu
- Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland.,Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich 8092, Switzerland
| | - Roman Bauer
- Department of Computer Science, University of Surrey, Guildford GU2 7XH, UK
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45
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Lai X, Taskén HA, Mo T, Funke SW, Frigessi A, Rognes ME, Köhn-Luque A. A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3542. [PMID: 34716985 DOI: 10.1002/cnm.3542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/24/2021] [Indexed: 06/13/2023]
Abstract
Mathematical modeling and simulation is a promising approach to personalized cancer medicine. Yet, the complexity, heterogeneity and multi-scale nature of cancer pose significant computational challenges. Coupling discrete cell-based models with continuous models using hybrid cellular automata (CA) is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales. However, when clinically relevant cancer portions are taken into account, such models become computationally very expensive. While efficient parallelization techniques for continuous models exist, their coupling with discrete models, particularly CA, necessitates more elaborate solutions. Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communication between processes that share CA neighborhood values while also allowing for reproducibility during stochastic updates. We demonstrated the potential of our solution on a complex hybrid cellular automaton model of breast cancer treated with combination chemotherapy. On a single-core processor, we obtained nearly linear scaling with an increasing problem size, whereas weak parallel scaling showed moderate growth in solving time relative to increase in problem size. Finally, we applied the algorithm to a problem that is 500 times larger than previous work, allowing us to run personalized therapy simulations based on heterogeneous cell density and tumor perfusion conditions estimated from magnetic resonance imaging data on an unprecedented scale.
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Affiliation(s)
- Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Håkon A Taskén
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Torgeir Mo
- Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
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46
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Ceccarelli AS, Borges A, Chara O. Size matters: tissue size as a marker for a transition between reaction-diffusion regimes in spatio-temporal distribution of morphogens. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211112. [PMID: 35116146 PMCID: PMC8790355 DOI: 10.1098/rsos.211112] [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: 06/30/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
The reaction-diffusion model constitutes one of the most influential mathematical models to study distribution of morphogens in tissues. Despite its widespread use, the effect of finite tissue size on model-predicted spatio-temporal morphogen distributions has not been completely elucidated. In this study, we analytically investigated the spatio-temporal distributions of morphogens predicted by a reaction-diffusion model in a finite one-dimensional domain, as a proxy for a biological tissue, and compared it with the solution of the infinite-domain model. We explored the reduced parameter, the tissue length in units of a characteristic reaction-diffusion length, and identified two reaction-diffusion regimes separated by a crossover tissue size estimated in approximately three characteristic reaction-diffusion lengths. While above this crossover the infinite-domain model constitutes a good approximation, it breaks below this crossover, whereas the finite-domain model faithfully describes the entire parameter space. We evaluated whether the infinite-domain model renders accurate estimations of diffusion coefficients when fitted to finite spatial profiles, a procedure typically followed in fluorescence recovery after photobleaching (FRAP) experiments. We found that the infinite-domain model overestimates diffusion coefficients when the domain is smaller than the crossover tissue size. Thus, the crossover tissue size may be instrumental in selecting the suitable reaction-diffusion model to study tissue morphogenesis.
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Affiliation(s)
- Alberto S. Ceccarelli
- Systems Biology Group (SysBio), Institute of Physics of Liquids and Biological Systems (IFLySIB), National Scientific and Technical Research Council (CONICET), University of La Plata, La Plata, Argentina
| | - Augusto Borges
- Systems Biology Group (SysBio), Institute of Physics of Liquids and Biological Systems (IFLySIB), National Scientific and Technical Research Council (CONICET), University of La Plata, La Plata, Argentina
- Research Unit of Sensory Biology & Organogenesis, Helmholtz Zentrum München, Munich, Germany
- Graduate School of Quantitative Biosciences (QBM), Munich, Germany
| | - Osvaldo Chara
- Systems Biology Group (SysBio), Institute of Physics of Liquids and Biological Systems (IFLySIB), National Scientific and Technical Research Council (CONICET), University of La Plata, La Plata, Argentina
- Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden, Germany
- Instituto de Tecnología, Universidad Argentina de la Empresa (UADE), Buenos Aires, Argentina
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47
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Abstract
Extracting mechanistic knowledge from the spatial and temporal phenotypes of morphogenesis is a current challenge due to the complexity of biological regulation and their feedback loops. Furthermore, these regulatory interactions are also linked to the biophysical forces that shape a developing tissue, creating complex interactions responsible for emergent patterns and forms. Here we show how a computational systems biology approach can aid in the understanding of morphogenesis from a mechanistic perspective. This methodology integrates the modeling of tissues and whole-embryos with dynamical systems, the reverse engineering of parameters or even whole equations with machine learning, and the generation of precise computational predictions that can be tested at the bench. To implement and perform the computational steps in the methodology, we present user-friendly tools, computer code, and guidelines. The principles of this methodology are general and can be adapted to other model organisms to extract mechanistic knowledge of their morphogenesis.
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Affiliation(s)
- Jason M Ko
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
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48
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Lötstedt P. Derivation of continuum models from discrete models of mechanical forces in cell populations. J Math Biol 2021; 83:75. [PMID: 34878601 PMCID: PMC8654724 DOI: 10.1007/s00285-021-01697-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/23/2021] [Accepted: 11/16/2021] [Indexed: 11/14/2022]
Abstract
In certain discrete models of populations of biological cells, the mechanical forces between the cells are center based or vertex based on the microscopic level where each cell is individually represented. The cells are circular or spherical in a center based model and polygonal or polyhedral in a vertex based model. On a higher, macroscopic level, the time evolution of the density of the cells is described by partial differential equations (PDEs). We derive relations between the modelling on the micro and macro levels in one, two, and three dimensions by regarding the micro model as a discretization of a PDE for conservation of mass on the macro level. The forces in the micro model correspond on the macro level to a gradient of the pressure scaled by quantities depending on the cell geometry. The two levels of modelling are compared in numerical experiments in one and two dimensions.
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Affiliation(s)
- Per Lötstedt
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden.
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49
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Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth. PLoS Comput Biol 2021; 17:e1008845. [PMID: 34843457 PMCID: PMC8659698 DOI: 10.1371/journal.pcbi.1008845] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 12/09/2021] [Accepted: 11/02/2021] [Indexed: 12/31/2022] Open
Abstract
Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively. The calibration of agent-based models of tumor cell growth to experimental data remains a challenge in computational oncology. Besides the computational cost of modeling thousands of agents, the model’s intrinsic stochasticity demands numerous realizations of the simulations to accurately represent the statistical features of the model predictions. We developed a hybrid, multiscale, coarse-grain, agent-based model that captures the growth and decline of human breast carcinoma cells under different initial conditions. We determined the effects of coarse-graining the ABM on the multiscale model output and the number of repetitions necessary to capture the stochastic transitions present in the model. We identified the most influential parameters on the model prediction through a sensitivity analysis and selected which parameters can be fixed and which ones should be calibrated. Using Bayesian calibration, we show that the model can accurately represent the experimental data. The validation step indicates that our model can reliably predict the in vitro temporal data, depending on the choice of the training (calibration data) sets.
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50
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Cook B, Combes A, Little M, Osborne JM. Modelling Cellular Interactions and Dynamics During Kidney Morphogenesis. Bull Math Biol 2021; 84:8. [PMID: 34837548 DOI: 10.1007/s11538-021-00968-3] [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: 06/29/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022]
Abstract
Kidney disease and renal disorders account for a significant proportion of health complications in mid-late adulthood worldwide. Many renal deficiencies are due to improper formation of the kidneys before birth, which are caused by disorders in the developmental process that arise from genetic and/or environmental factors. Mathematical modelling can help build on experimental knowledge to increase our understanding of the complexities of kidney organogenesis. In this paper, we present a discrete cell-based model of kidney development. Specifically, we model the tip of the developing ureteric tree to investigate the behaviours of cap mesenchyme cells which are required to sustain ureteric tip growth. We find that spatial regulation of the differentiation of cap mesenchyme cells through cellular signalling is sufficient to ensure robust ureteric tip development. Additionally, we find that increased adhesion interactions between cap mesenchyme cells and the ureteric tip surface can lead to a more stable tip-cap unit. Our analysis of the various processes on this scale highlights essential components for healthy kidney growth and provides insight into mechanisms to be studied further in order to replicate the process in vitro.
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Affiliation(s)
- Blake Cook
- School of Mathematics and Statistics, University of Melbourne, Victoria, 3010, Australia.,Institute of Metabolism and Systems Research, College of Medical and Dental Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Alex Combes
- Department of Anatomy and Developmental Biology, and Stem Cells and Development Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Melissa Little
- Murdoch Children's Research Institute, Flemington Rd, Parkville, VIC, 3052, Australia.,Department of Pediatrics, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Victoria, 3010, Australia.
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