1
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Sego TJ, Comlekoglu T, Peirce SM, Desimone DW, Glazier JA. General, open-source vertex modeling in biological applications using Tissue Forge. Sci Rep 2023; 13:17886. [PMID: 37857673 PMCID: PMC10587242 DOI: 10.1038/s41598-023-45127-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023] Open
Abstract
Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering open-source, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C, C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.
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Affiliation(s)
- T J Sego
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - Tien Comlekoglu
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- Department of Cell Biology, University of Virginia, Charlottesville, VA, USA
| | - Shayn M Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Douglas W Desimone
- Department of Cell Biology, University of Virginia, Charlottesville, VA, USA
| | - James A Glazier
- Department of Intelligent Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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2
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Comlekoglu T, Dzamba BJ, Pacheco GG, Shook DR, Sego TJ, Glazier JA, Peirce SM, DeSimone DW. Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration of Xenopus mesendoderm. bioRxiv 2023:2023.10.16.562601. [PMID: 37904937 PMCID: PMC10614848 DOI: 10.1101/2023.10.16.562601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Collectively migrating Xenopus mesendoderm cells are arranged into leader and follower rows with distinct adhesive properties and protrusive behaviors. In vivo, leading row mesendoderm cells extend polarized protrusions and migrate along a fibronectin matrix assembled by blastocoel roof cells. Traction stresses generated at the leading row result in the pulling forward of attached follower row cells. Mesendoderm explants removed from embryos provide an experimentally tractable system for characterizing collective cell movements and behaviors, yet the cellular mechanisms responsible for this mode of migration remain elusive. We introduce an agent-based computational model of migrating mesendoderm in the Cellular-Potts computational framework to investigate the relative contributions of multiple parameters specific to the behaviors of leader and follower row cells. Sensitivity analyses identify cohesotaxis, tissue geometry, and cell intercalation as key parameters affecting the migration velocity of collectively migrating cells. The model predicts that cohesotaxis and tissue geometry in combination promote cooperative migration of leader cells resulting in increased migration velocity of the collective. Radial intercalation of cells towards the substrate is an additional mechanism to increase migratory speed of the tissue. Summary Statement We present a novel Cellular-Potts model of collective cell migration to investigate the relative roles of cohesotaxis, tissue geometry, and cell intercalation on migration velocity of Xenopus mesendoderm.
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3
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Sego TJ, Sluka JP, Sauro HM, Glazier JA. Tissue Forge: Interactive biological and biophysics simulation environment. PLoS Comput Biol 2023; 19:e1010768. [PMID: 37871133 PMCID: PMC10621971 DOI: 10.1371/journal.pcbi.1010768] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 11/02/2023] [Accepted: 09/25/2023] [Indexed: 10/25/2023] Open
Abstract
Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.
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Affiliation(s)
- T. J. Sego
- Department of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - James P. Sluka
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - James A. Glazier
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
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4
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Toledo-Marín JQ, Glazier JA. Using deep LSD to build operators in GANs latent space with meaning in real space. PLoS One 2023; 18:e0287736. [PMID: 37384721 DOI: 10.1371/journal.pone.0287736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/12/2023] [Indexed: 07/01/2023] Open
Abstract
Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology.
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Affiliation(s)
- J Quetzalcóatl Toledo-Marín
- Luddy School of Informatics, Computing and Engineering, Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- TRIUMF, Vancouver, BC, Canada
| | - James A Glazier
- Luddy School of Informatics, Computing and Engineering, Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
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5
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Sego T, Comlekoglu T, Peirce SM, Desimone D, Glazier JA. General, Open-Source Vertex Modeling in Biological Applications Using Tissue Forge. Res Sq 2023:rs.3.rs-2886960. [PMID: 37214822 PMCID: PMC10197754 DOI: 10.21203/rs.3.rs-2886960/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering opensource, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C , C + + and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.
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Affiliation(s)
- T.J. Sego
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Tien Comlekoglu
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- Department of Cell Biology, University of Virginia, Charlottesville, VA, USA
| | - Shayn M. Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Douglas Desimone
- Department of Cell Biology, University of Virginia, Charlottesville, VA, USA
| | - James A. Glazier
- Department of Intelligent Engineering and Biocomplexity Institute, Indiana University,Bloomington, IN, USA
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6
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Rosenbauer J, Berghoff M, Glazier JA, Schug A. Multiscale Modeling of Spheroid Tumors: Effect of Nutrient Availability on Tumor Evolution. J Phys Chem B 2023; 127:3607-3615. [PMID: 37011021 PMCID: PMC10150357 DOI: 10.1021/acs.jpcb.2c08114] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
Recent years have revealed a large number of complex mechanisms and interactions that drive the development of malignant tumors. Tumor evolution is a framework that explains tumor development as a process driven by survival of the fittest, with tumor cells of different properties competing for limited available resources. To predict the evolutionary trajectory of a tumor, knowledge of how cellular properties influence the fitness of a subpopulation in the context of the microenvironment is required and is often inaccessible. Computational multiscale-modeling of tissues enables the observation of the full trajectory of each cell within the tumor environment. Here, we model a 3D spheroid tumor with subcellular resolution. The fitness of individual cells and the evolutionary behavior of the tumor are quantified and linked to cellular and environmental parameters. The fitness of cells is solely influenced by their position in the tumor, which in turn is influenced by the two variable parameters of our model: cell-cell adhesion and cell motility. We observe the influence of nutrient independence and static and dynamically changing nutrient availability on the evolutionary trajectories of heterogeneous tumors in a high-resolution computational model. Regardless of nutrient availability, we find a fitness advantage of low-adhesion cells, which are favorable for tumor invasion. We find that the introduction of nutrient-dependent cell division and death accelerates the evolutionary speed. The evolutionary speed can be increased by fluctuations in nutrients. We identify a distinct frequency domain in which the evolutionary speed increases significantly over a tumor with constant nutrient supply. The findings suggest that an unstable supply of nutrients can accelerate tumor evolution and, thus, the transition to malignancy.
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Affiliation(s)
- Jakob Rosenbauer
- Jülich Supercomputing Center, Forschungszentrum Jülich, Jülich, Nordrhein-Westfalen 52425, Germany
| | - Marco Berghoff
- Steinbuch Center for Computing, Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg 76021, Germany
| | - James A Glazier
- Biocomplexity Institute, Indiana University, Bloomington, Indiana 47408, United States
| | - Alexander Schug
- Jülich Supercomputing Center, Forschungszentrum Jülich, Jülich, Nordrhein-Westfalen 52425, Germany
- University of Duisburg-Essen, Duisburg, Nordrhein-Westfalen 47057, Germany
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7
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Piatkowska AM, Adhikari K, Moverley AA, Turmaine M, Glazier JA, Plachta N, Evans SE, Stern CD. Sequential changes in cellular properties accompanying amniote somite formation. J Anat 2022; 242:417-435. [PMID: 36423208 PMCID: PMC9919497 DOI: 10.1111/joa.13791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/30/2022] [Accepted: 10/28/2022] [Indexed: 11/26/2022] Open
Abstract
Somites are transient structures derived from the pre-somitic mesoderm (PSM), involving mesenchyme-to-epithelial transition (MET) where the cells change their shape and polarize. Using Scanning electron microscopy (SEM), immunocytochemistry and confocal microscopy, we study the progression of these events along the tail-to-head axis of the embryo, which mirrors the progression of somitogenesis (younger cells located more caudally). SEM revealed that PSM epithelialization is a gradual process, which begins much earlier than previously thought, starting with the dorsalmost cells, then the medial ones, and then, simultaneously, the ventral and lateral cells, before a somite fully separates from the PSM. The core (internal) cells of the PSM and somites never epithelialize, which suggests that the core cells could be 'trapped' within the somitocoele after cells at the surfaces of the PSM undergo MET. Three-dimensional imaging of the distribution of the cell polarity markers PKCζ, PAR3, ZO1, the Golgi marker GM130 and the apical marker N-cadherin reveal that the pattern of polarization is distinctive for each marker and for each surface of the PSM, but the order of these events is not the same as the progression of cell elongation. These observations challenge some assumptions underlying existing models of somite formation.
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Affiliation(s)
- Agnieszka M. Piatkowska
- Department of Cell & Developmental BiologyUniversity College London, Gower Street (Anatomy Building)LondonUK
| | - Kaustubh Adhikari
- Department of Cell & Developmental BiologyUniversity College London, Gower Street (Anatomy Building)LondonUK,Present address:
The Open UniversityMilton KeynesUK
| | - Adam A. Moverley
- Department of Cell & Developmental BiologyUniversity College London, Gower Street (Anatomy Building)LondonUK
| | - Mark Turmaine
- Department of Cell & Developmental BiologyUniversity College London, Gower Street (Anatomy Building)LondonUK
| | - James A. Glazier
- Department of Intelligent Systems EngineeringBiocomplexity InstituteBloomingtonIndianaUSA
| | - Nicolas Plachta
- Department of Cell and Developmental Biology, 9‐123 Smilow Center for Translational Research, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Susan E. Evans
- Department of Cell & Developmental BiologyUniversity College London, Gower Street (Anatomy Building)LondonUK
| | - Claudio D. Stern
- Department of Cell & Developmental BiologyUniversity College London, Gower Street (Anatomy Building)LondonUK
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8
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Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA. Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med 2022; 5:64. [PMID: 35595830 PMCID: PMC9122990 DOI: 10.1038/s41746-022-00610-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - A 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
| | - T Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - G An
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - B Shapiro
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - R S Malik-Sheriff
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, Cambridge, UK
| | - T J Sego
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - A Knapp
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - P Macklin
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - J A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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9
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de Almeida RMC, Thomas GL, Glazier JA. Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection. NAR Genom Bioinform 2022; 4:lqac020. [PMID: 35300459 PMCID: PMC8923009 DOI: 10.1093/nargab/lqac020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/10/2022] [Indexed: 11/21/2022] Open
Abstract
To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in vitro infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models and human-derived samples.
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Affiliation(s)
- Rita M C de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Instituto Nacional de Ciência e Tecnologia: Sistemas Complexos, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Programa de Pós-Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | - Gilberto L Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - James A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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10
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Karr J, Malik-Sheriff RS, Osborne J, Gonzalez-Parra G, Forgoston E, Bowness R, Liu Y, Thompson R, Garira W, Barhak J, Rice J, Torres M, Dobrovolny HM, Tang T, Waites W, Glazier JA, Faeder JR, Kulesza A. Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility. Front Syst Biol 2022; 2:822606. [PMID: 36909847 PMCID: PMC10002468 DOI: 10.3389/fsysb.2022.822606] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.
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Affiliation(s)
- Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Rahuman S. Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - James Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia
| | | | - Eric Forgoston
- Department of Applied Mathematics and Statistics, Montclair State University, Montclair, NJ, United States
| | - Ruth Bowness
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Robin Thompson
- Mathematics Institute and the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Winston Garira
- Department of Mathematics and Applied Mathematics, Modelling Health and Environmental Linkages Research Group, University of Venda, Limpopo, South Africa
| | - Jacob Barhak
- Jacob Barhak Analytics, Austin, TX, United States
| | - John Rice
- Independent Retired Working Group Volunteer, Virginia Beach, VA, United States
| | - Marcella Torres
- Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, United States
| | - Hana M. Dobrovolny
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, United States
| | - Tingting Tang
- Department of Mathematics and Statistics in San Diego State University (SDSU) and SDSU Imperial Valley, Calexico, CA, United States
| | - William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States
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de Almeida RM, Giardini GS, Vainstein M, Glazier JA, Thomas GL. Exact solution for the Anisotropic Ornstein-Uhlenbeck process. Physica A 2022; 587:126526. [PMID: 36937094 PMCID: PMC10022481 DOI: 10.1016/j.physa.2021.126526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Active-Matter models commonly consider particles with overdamped dynamics subject to a force (speed) with constant modulus and random direction. Some models also include random noise in particle displacement (a Wiener process), resulting in diffusive motion at short time scales. On the other hand, Ornstein-Uhlenbeck processes apply Langevin dynamics to the particles' velocity and predict motion that is not diffusive at short time scales. Experiments show that migrating cells have gradually varying speeds at intermediate and long time scales, with short-time diffusive behavior. While Ornstein-Uhlenbeck processes can describe the moderate-and long-time speed variation, Active-Matter models for over-damped particles can explain the short-time diffusive behavior. Isotropic models cannot explain both regimes, because short-time diffusion renders instantaneous velocity ill-defined, and prevents the use of dynamical equations that require velocity time-derivatives. On the other hand, both models correctly describe some of the different temporal regimes seen in migrating biological cells and must, in the appropriate limit, yield the same observable predictions. Here we propose and solve analytically an Anisotropic Ornstein-Uhlenbeck process for polarized particles, with Langevin dynamics governing the particle's movement in the polarization direction and a Wiener process governing displacement in the orthogonal direction. Our characterization provides a theoretically robust way to compare movement in dimensionless simulations to movement in experiments in which measurements have meaningful space and time units. We also propose an approach to deal with inevitable finite-precision effects in experiments and simulations.
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Affiliation(s)
- Rita M.C. de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Program de Pós Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | | | - Mendeli Vainstein
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - James A. Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
| | - Gilberto L. Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
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12
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Sego TJ, Mochan ED, Ermentrout GB, Glazier JA. A multiscale multicellular spatiotemporal model of local influenza infection and immune response. J Theor Biol 2022; 532:110918. [PMID: 34592264 PMCID: PMC8478073 DOI: 10.1016/j.jtbi.2021.110918] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 12/11/2022]
Abstract
Respiratory viral infections pose a
serious public health concern, from mild seasonal influenza to pandemics
like those of SARS-CoV-2. Spatiotemporal dynamics of viral infection
impact nearly all aspects of the progression of a viral infection, like
the dependence of viral replication rates on the type of cell and
pathogen, the strength of the immune response and localization of
infection. Mathematical modeling is often used to describe respiratory
viral infections and the immune response to them using ordinary
differential equation (ODE) models. However, ODE models neglect
spatially-resolved biophysical mechanisms like lesion shape and the
details of viral transport, and so cannot model spatial effects of a
viral infection and immune response. In this work, we develop a
multiscale, multicellular spatiotemporal model of influenza infection and
immune response by combining non-spatial ODE modeling and spatial,
cell-based modeling. We employ cellularization, a recently developed
method for generating spatial, cell-based, stochastic models from
non-spatial ODE models, to generate much of our model from a calibrated
ODE model that describes infection, death and recovery of susceptible
cells and innate and adaptive responses during influenza infection, and
develop models of cell migration and other mechanisms not explicitly
described by the ODE model. We determine new model parameters to generate
agreement between the spatial and original ODE models under certain
conditions, where simulation replicas using our model serve as
microconfigurations of the ODE model, and compare results between the
models to investigate the nature of viral exposure and impact of
heterogeneous infection on the time-evolution of the viral infection. We
found that using spatially homogeneous initial exposure conditions
consistently with those employed during calibration of the ODE model
generates far less severe infection, and that local exposure to virus
must be multiple orders of magnitude greater than a uniformly applied
exposure to all available susceptible cells. This strongly suggests a
prominent role of localization of exposure in influenza A infection. We
propose that the particularities of the microenvironment to which a virus
is introduced plays a dominant role in disease onset and progression, and
that spatially resolved models like ours may be important to better
understand and more reliably predict future health states based on
susceptibility of potential lesion sites using spatially resolved patient
data of the state of an infection. We can readily integrate the immune
response components of our model into other modeling and simulation
frameworks of viral infection dynamics that do detailed modeling of other
mechanisms like viral internalization and intracellular viral replication
dynamics, which are not explicitly represented in the ODE model. We can
also combine our model with available experimental data and modeling of
exposure scenarios and spatiotemporal aspects of mechanisms like
mucociliary clearance that are only implicitly described by the ODE
model, which would significantly improve the ability of our model to
present spatially resolved predictions about the progression of influenza
infection and immune response.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
| | - Ericka D Mochan
- Department of Analytical, Physical, and Social Sciences, Carlow University, Pittsburgh, PA, USA
| | - G Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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13
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Tikka P, Mercker M, Skovorodkin I, Saarela U, Vainio S, Ronkainen VP, Sluka JP, Glazier JA, Marciniak-Czochra A, Schaefer F. Computational modelling of nephron progenitor cell movement and aggregation during kidney organogenesis. Math Biosci 2021; 344:108759. [PMID: 34883105 DOI: 10.1016/j.mbs.2021.108759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 10/19/2022]
Abstract
During early kidney organogenesis, nephron progenitor (NP) cells move from the tip to the corner region of the ureteric bud (UB) branches in order to form the pretubular aggregate, the early structure giving rise to nephron formation. NP cells derive from metanephric mesenchymal cells and physically interact with them during the movement. Chemotaxis and cell-cell adhesion differences are believed to drive the cell patterning during this critical period of organogenesis. However, the effect of these forces to the cell patterns and their respective movements are known in limited details. We applied a Cellular Potts Model to explore how these forces and organizations contribute to directed cell movement and aggregation. Model parameters were estimated based on fitting to experimental data obtained in ex vivo kidney explant and dissociation-reaggregation organoid culture studies. Our simulations indicated that optimal enrichment and aggregation of NP cells in the UB corner niche requires chemoattractant secretion from both the UB epithelial cells and the NP cells themselves, as well as differences in cell-cell adhesion energies. Furthermore, NP cells were observed, both experimentally and by modelling, to move at higher speed in the UB corner as compared to the tip region where they originated. The existence of different cell speed domains along the UB was confirmed using self-organizing map analysis. In summary, we saw faster NP cell movements near aggregation. The applicability of Cellular Potts Model approach to simulate cell movement and patterning was found to be good during for this early nephrogenesis process. Further refinement of the model should allow us to recapitulate the effects of developmental changes of cell phenotypes and molecular crosstalk during further organ development.
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Affiliation(s)
- Pauli Tikka
- Division of Pediatric Nephrology. Heidelberg University Center for Pediatrics and Adolescent Medicine, Heidelberg, Germany.
| | - Moritz Mercker
- Institute of Applied Mathematics (IAM) and Interdisciplinary Center of Scientific Computing (IWR), Mathematikon, Heidelberg University, Germany
| | - Ilya Skovorodkin
- Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Ulla Saarela
- Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Seppo Vainio
- Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Veli-Pekka Ronkainen
- Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - James P Sluka
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, Indiana, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, Indiana, USA
| | - Anna Marciniak-Czochra
- Institute of Applied Mathematics (IAM) and Interdisciplinary Center of Scientific Computing (IWR), Mathematikon, Heidelberg University, Germany
| | - Franz Schaefer
- Division of Pediatric Nephrology. Heidelberg University Center for Pediatrics and Adolescent Medicine, Heidelberg, Germany
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14
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Getz M, Wang Y, An G, Asthana M, Becker A, Cockrell C, Collier N, Craig M, Davis CL, Faeder JR, Ford Versypt AN, Mapder T, Gianlupi JF, Glazier JA, Hamis S, Heiland R, Hillen T, Hou D, Islam MA, Jenner AL, Kurtoglu F, Larkin CI, Liu B, Macfarlane F, Maygrundter P, Morel PA, Narayanan A, Ozik J, Pienaar E, Rangamani P, Saglam AS, Shoemaker JE, Smith AM, Weaver JJA, Macklin P. Iterative community-driven development of a SARS-CoV-2 tissue simulator. bioRxiv 2021:2020.04.02.019075. [PMID: 32511322 PMCID: PMC7239052 DOI: 10.1101/2020.04.02.019075] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.
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15
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Aponte-Serrano JO, Weaver JJA, Sego TJ, Glazier JA, Shoemaker JE. Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics. PLoS Comput Biol 2021; 17:e1008874. [PMID: 34695114 PMCID: PMC8608315 DOI: 10.1371/journal.pcbi.1008874] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 11/22/2021] [Accepted: 09/27/2021] [Indexed: 02/07/2023] Open
Abstract
Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. The model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of plaque growth arrest on diffusion lengths highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro measurement of these diffusion coefficients. Sensitivity analyses under conditions leading to continuous or arrested plaque growth found that plaque growth is more sensitive to variations of most parameters and more likely to have identifiable model parameters when conditions lead to plaque arrest. This result suggests that cytokine assay measurements may be most informative under conditions leading to arrested plaque growth. The model is easy to extend to include SARS-CoV-2-specific mechanisms or to use as a component in models linking epithelial cell signaling to systemic immune models.
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Affiliation(s)
- Josua O. Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Jordan J. A. Weaver
- Department of Chemical & Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - T. J. Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - James A. Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Jason E. Shoemaker
- Department of Chemical & Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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16
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Sego TJ, Aponte-Serrano JO, Gianlupi JF, Glazier JA. Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection. BMC Biol 2021; 19:196. [PMID: 34496857 PMCID: PMC8424622 DOI: 10.1186/s12915-021-01115-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 08/02/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND The biophysics of an organism span multiple scales from subcellular to organismal and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them and highlights the need for simpler methods able to model the spatial features of biological systems. RESULTS In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models, the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models. CONCLUSION We developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
| | - Josua O Aponte-Serrano
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Juliano F Gianlupi
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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17
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Toledo-Marín JQ, Fox G, Sluka JP, Glazier JA. Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations. Front Physiol 2021; 12:667828. [PMID: 34248661 PMCID: PMC8264663 DOI: 10.3389/fphys.2021.667828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 05/25/2021] [Indexed: 11/13/2022] Open
Abstract
In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.
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Affiliation(s)
- J Quetzalcóatl Toledo-Marín
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States.,Luddy School of Informatics, Computing and Engineering, Bloomington, IN, United States
| | - Geoffrey Fox
- Luddy School of Informatics, Computing and Engineering, Bloomington, IN, United States.,Digital Science Center, Bloomington, IN, United States
| | - James P Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States.,Luddy School of Informatics, Computing and Engineering, Bloomington, IN, United States
| | - James A Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States.,Luddy School of Informatics, Computing and Engineering, Bloomington, IN, United States
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18
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Affiliation(s)
| | - James P Sluka
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
| | - James A Glazier
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
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19
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de Almeida RMC, Thomas GL, Glazier JA. Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection. bioRxiv 2021. [PMID: 32587961 PMCID: PMC7310616 DOI: 10.1101/2020.06.16.155267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in vitro infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. At the individual-gene level, EGR3 was significantly upregulated in infected cells. Similar activation in T-cells and fibroblasts in infected lung could explain the T-cell anergy and eventual fibrosis seen in SARS-CoV-1 infection. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models, and human-derived samples.
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Affiliation(s)
- Rita M C de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.,Instituto Nacional de Ciência e Tecnologia: Sistemas Complexos, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.,Programa de Pós-Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | - Gilberto L Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - James A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
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20
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Liu R, Higley KA, Swat MH, Chaplain MAJ, Powathil GG, Glazier JA. Development of a coupled simulation toolkit for computational radiation biology based on Geant4 and CompuCell3D. Phys Med Biol 2021; 66:045026. [PMID: 33339019 DOI: 10.1088/1361-6560/abd4f9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Understanding and designing clinical radiation therapy is one of the most important areas of state-of-the-art oncological treatment regimens. Decades of research have gone into developing sophisticated treatment devices and optimization protocols for schedules and dosages. In this paper, we presented a comprehensive computational platform that facilitates building of the sophisticated multi-cell-based model of how radiation affects the biology of living tissue. We designed and implemented a coupled simulation method, including a radiation transport model, and a cell biology model, to simulate the tumor response after irradiation. The radiation transport simulation was implemented through Geant4 which is an open-source Monte Carlo simulation platform that provides many flexibilities for users, as well as low energy DNA damage simulation physics, Geant4-DNA. The cell biology simulation was implemented using CompuCell3D (CC3D) which is a cell biology simulation platform. In order to couple Geant4 solver with CC3D, we developed a 'bridging' module, RADCELL, that extracts tumor cellular geometry of the CC3D simulation (including specification of the individual cells) and ported it to the Geant4 for radiation transport simulation. The cell dose and cell DNA damage distribution in multicellular system were obtained using Geant4. The tumor response was simulated using cell-based tissue models based on CC3D, and the cell dose and cell DNA damage information were fed back through RADCELL to CC3D for updating the cell properties. By merging two powerful and widely used modeling platforms, CC3D and Geant4, we delivered a novel tool that can give us the ability to simulate the dynamics of biological tissue in the presence of ionizing radiation, which provides a framework for quantifying the biological consequences of radiation therapy. In this introductory methods paper, we described our modeling platform in detail and showed how it can be applied to study the application of radiotherapy to a vascularized tumor.
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Affiliation(s)
- Ruirui Liu
- School of Nuclear Science and Engineering, Oregon State University, 100 Radiation Center, Corvallis, OR 97331, United States of America.,Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, 63110, United States of America
| | - Kathryn A Higley
- School of Nuclear Science and Engineering, Oregon State University, 100 Radiation Center, Corvallis, OR 97331, United States of America
| | - Maciej H Swat
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Mark A J Chaplain
- School of Mathematics and Statistics, Mathematical Institute, University of St Andrews, St Andrews KY16 9SS, Fife, United Kingdom
| | - Gibin G Powathil
- Department of Mathematics, College of Science, Swansea University, Swansea, SA2 8PP, United Kingdom
| | - James A Glazier
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
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21
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Adhyapok P, Fu X, Sluka JP, Clendenon SG, Sluka VD, Wang Z, Dunn K, Klaunig JE, Glazier JA. A computational model of liver tissue damage and repair. PLoS One 2020; 15:e0243451. [PMID: 33347443 PMCID: PMC7752149 DOI: 10.1371/journal.pone.0243451] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/22/2020] [Indexed: 01/09/2023] Open
Abstract
Drug induced liver injury (DILI) and cell death can result from oxidative stress in hepatocytes. An initial pattern of centrilobular damage in the APAP model of DILI is amplified by communication from stressed cells and immune system activation. While hepatocyte proliferation counters cell loss, high doses are still lethal to the tissue. To understand the progression of disease from the initial damage to tissue recovery or death, we computationally model the competing biological processes of hepatocyte proliferation, necrosis and injury propagation. We parametrize timescales of proliferation (α), conversion of healthy to stressed cells (β) and further sensitization of stressed cells towards necrotic pathways (γ) and model them on a Cellular Automaton (CA) based grid of lattice sites. 1D simulations show that a small α/β (fast proliferation), combined with a large γ/β (slow death) have the lowest probabilities of tissue survival. At large α/β, tissue fate can be described by a critical γ/β* ratio alone; this value is dependent on the initial amount of damage and proportional to the tissue size N. Additionally, the 1D model predicts a minimum healthy population size below which damage is irreversible. Finally, we compare 1D and 2D phase spaces and discuss outcomes of bistability where either survival or death is possible, and of coexistence where simulated tissue never completely recovers or dies but persists as a mixture of healthy, stressed and necrotic cells. In conclusion, our model sheds light on the evolution of tissue damage or recovery and predicts potential for divergent fates given different rates of proliferation, necrosis, and injury propagation.
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Affiliation(s)
- Priyom Adhyapok
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Physics, Indiana University, Bloomington, IN, United States of America
- * E-mail:
| | - Xiao Fu
- The Francis Crick Institute, London, United Kingdom
| | - James P. Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
| | - Sherry G. Clendenon
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
| | - Victoria D. Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
| | - Zemin Wang
- School of Public Health, Indiana University, Bloomington, IN, United States of America
| | - Kenneth Dunn
- School of Medicine, Indiana University, Indianapolis, IN, United States of America
| | - James E. Klaunig
- School of Public Health, Indiana University, Bloomington, IN, United States of America
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
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22
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Sego TJ, Aponte-Serrano JO, Ferrari Gianlupi J, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness. PLoS Comput Biol 2020; 16:e1008451. [PMID: 33347439 PMCID: PMC7785254 DOI: 10.1371/journal.pcbi.1008451] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/05/2021] [Accepted: 10/20/2020] [Indexed: 12/23/2022] Open
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T. J. Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Josua O. Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Samuel R. Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Cognitive Science Program, Indiana University, Bloomington, Indiana, United States of America
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität, Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - James M. Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Ellen M. Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Richard K. Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, Georgia, United States of America
| | - James A. Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
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23
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Sego TJ, Aponte-Serrano JO, Gianlupi JF, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness: A multiscale model of viral infection in epithelial tissues. bioRxiv 2020:2020.04.27.064139. [PMID: 32511367 PMCID: PMC7263495 DOI: 10.1101/2020.04.27.064139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Josua O Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Samuel R Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Richard K Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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24
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Sego TJ, Glazier JA, Tovar A. Unification of aggregate growth models by emergence from cellular and intracellular mechanisms. R Soc Open Sci 2020; 7:192148. [PMID: 32968501 PMCID: PMC7481681 DOI: 10.1098/rsos.192148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 07/03/2020] [Indexed: 05/04/2023]
Abstract
Multicellular aggregate growth is regulated by nutrient availability and removal of metabolites, but the specifics of growth dynamics are dependent on cell type and environment. Classical models of growth are based on differential equations. While in some cases these classical models match experimental observations, they can only predict growth of a limited number of cell types and so can only be selectively applied. Currently, no classical model provides a general mathematical representation of growth for any cell type and environment. This discrepancy limits their range of applications, which a general modelling framework can enhance. In this work, a hybrid cellular Potts model is used to explain the discrepancy between classical models as emergent behaviours from the same mathematical system. Intracellular processes are described using probability distributions of local chemical conditions for proliferation and death and simulated. By fitting simulation results to a generalization of the classical models, their emergence is demonstrated. Parameter variations elucidate how aggregate growth may behave like one classical growth model or another. Three classical growth model fits were tested, and emergence of the Gompertz equation was demonstrated. Effects of shape changes are demonstrated, which are significant for final aggregate size and growth rate, and occur stochastically.
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Affiliation(s)
- T. J. Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - James A. Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Andres Tovar
- Department of Mechanical and Energy Engineering, Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA
- Author for correspondence: Andres Tovar e-mail:
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25
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Fortuna I, Perrone GC, Krug MS, Susin E, Belmonte JM, Thomas GL, Glazier JA, de Almeida RMC. CompuCell3D Simulations Reproduce Mesenchymal Cell Migration on Flat Substrates. Biophys J 2020; 118:2801-2815. [PMID: 32407685 PMCID: PMC7264849 DOI: 10.1016/j.bpj.2020.04.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 04/20/2020] [Accepted: 04/20/2020] [Indexed: 11/16/2022] Open
Abstract
Mesenchymal cell crawling is a critical process in normal development, in tissue function, and in many diseases. Quantitatively predictive numerical simulations of cell crawling thus have multiple scientific, medical, and technological applications. However, we still lack a low-computational-cost approach to simulate mesenchymal three-dimensional (3D) cell crawling. Here, we develop a computationally tractable 3D model (implemented as a simulation in the CompuCell3D simulation environment) of mesenchymal cells crawling on a two-dimensional substrate. The Fürth equation, the usual characterization of mean-squared displacement (MSD) curves for migrating cells, describes a motion in which, for increasing time intervals, cell movement transitions from a ballistic to a diffusive regime. Recent experiments have shown that for very short time intervals, cells exhibit an additional fast diffusive regime. Our simulations' MSD curves reproduce the three experimentally observed temporal regimes, with fast diffusion for short time intervals, slow diffusion for long time intervals, and intermediate time -interval-ballistic motion. The resulting parameterization of the trajectories for both experiments and simulations allows the definition of time- and length scales that translate between computational and laboratory units. Rescaling by these scales allows direct quantitative comparisons among MSD curves and between velocity autocorrelation functions from experiments and simulations. Although our simulations replicate experimentally observed spontaneous symmetry breaking, short-timescale diffusive motion, and spontaneous cell-motion reorientation, their computational cost is low, allowing their use in multiscale virtual-tissue simulations. Comparisons between experimental and simulated cell motion support the hypothesis that short-time actomyosin dynamics affects longer-time cell motility. The success of the base cell-migration simulation model suggests its future application in more complex situations, including chemotaxis, migration through complex 3D matrices, and collective cell motion.
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Affiliation(s)
- Ismael Fortuna
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Gabriel C Perrone
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Monique S Krug
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Eduarda Susin
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Julio M Belmonte
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana; Department of Physics, North Carolina State University, Raleigh, North Carolina
| | - Gilberto L Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
| | - James A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana
| | - Rita M C de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Program de Pós Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.
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26
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Dunn KW, Martinez MM, Wang Z, Mang HE, Clendenon SG, Sluka JP, Glazier JA, Klaunig JE. Mitochondrial depolarization and repolarization in the early stages of acetaminophen hepatotoxicity in mice. Toxicology 2020; 439:152464. [PMID: 32315716 DOI: 10.1016/j.tox.2020.152464] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/25/2020] [Accepted: 04/14/2020] [Indexed: 02/07/2023]
Abstract
Mitochondrial injury and depolarization are primary events in acetaminophen hepatotoxicity. Previous studies have shown that restoration of mitochondrial function in surviving hepatocytes, which is critical to recovery, is at least partially accomplished via biogenesis of new mitochondria. However, other studies indicate that mitochondria also have the potential to spontaneously repolarize. Although repolarization was previously observed only at a sub-hepatotoxic dose of acetaminophen, we postulated that mitochondrial repolarization in hepatocytes outside the centrilobular regions of necrosis might contribute to recovery of mitochondrial function following acetaminophen-induced injury. Our studies utilized longitudinal intravital microscopy of millimeter-scale regions of the mouse liver to characterize the spatio-temporal relationship between mitochondrial polarization and necrosis early in acetaminophen-induced liver injury. Treatment of male C57BL/6J mice with a single intraperitoneal 250 mg/kg dose of acetaminophen resulted in hepatotoxicity that was apparent histologically within 2 h of treatment, leading to 20 and 60-fold increases in serum aspartate aminotransferase and alanine aminotransferase, respectively, within 6 h. Intravital microscopy of the livers of mice injected with rhodamine123, TexasRed-dextran, propidium iodide and Hoechst 33342 detected centrilobular foci of necrosis within extended regions of mitochondrial depolarization within 2 h of acetaminophen treatment. Although regions of necrosis were more apparent 6 h after acetaminophen treatment, the vast majority of hepatocytes with depolarized mitochondria did not progress to necrosis, but rather recovered mitochondrial polarization within 6 h. Recovery of mitochondrial function following acetaminophen hepatotoxicity thus involves not only biogenesis of new mitochondria, but also repolarization of existing mitochondria. These studies also revealed a spatial distribution of necrosis and mitochondrial depolarization whose single-cell granularity is inconsistent with the hypothesis that communication between neighboring cells plays an important role in the propagation of necrosis during the early stages of APAP hepatotoxicity. Small islands of healthy, intact cells were frequently found surrounded by necrotic cells, and small islands of necrotic cells were frequently found surrounded by healthy, intact cells. Time-series studies demonstrated that these "islands", consisting in some cases of single cells, are persistent; over a period of hours, injury does not spread from individual necrotic cells to their neighbors.
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Affiliation(s)
- Kenneth W Dunn
- Department of Medicine, Indiana University, Indianapolis, IN, USA.
| | | | - Zemin Wang
- School of Public Health, Indiana University, Bloomington, IN, USA
| | - Henry E Mang
- Department of Medicine, Indiana University, Indianapolis, IN, USA
| | - Sherry G Clendenon
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - James P Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - James A Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - James E Klaunig
- School of Public Health, Indiana University, Bloomington, IN, USA
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27
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Clendenon SG, Fu X, Von Hoene RA, Clendenon JL, Sluka JP, Winfree S, Mang H, Martinez M, Filson A, Klaunig JE, Glazier JA, Dunn KW. Spatial Temporal Analysis of Fieldwise Flow in Microvasculature. J Vis Exp 2019. [PMID: 31789313 DOI: 10.3791/60493] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Changes in blood flow velocity and distribution are vital in maintaining tissue and organ perfusion in response to varying cellular needs. Further, appearance of defects in microcirculation can be a primary indicator in the development of multiple pathologies. Advances in optical imaging have made intravital microscopy (IVM) a practical approach, permitting imaging at the cellular and subcellular level in live animals at high-speed over time. Yet, despite the importance of maintaining adequate tissue perfusion, spatial and temporal variability in capillary flow is seldom documented. In the standard approach, a small number of capillary segments are chosen for imaging over a limited time. To comprehensively quantify capillary flow in an unbiased way we developed Spatial Temporal Analysis of Fieldwise Flow (STAFF), a macro for FIJI open-source image analysis software. Using high-speed image sequences of full fields of blood flow within capillaries, STAFF produces images that represent motion over time called kymographs for every time interval for every vascular segment. From the kymographs STAFF calculates velocities from the distance that red blood cells move over time, and outputs the velocity data as a sequence of color-coded spatial maps for visualization and tabular output for quantitative analyses. In normal mouse livers, STAFF analyses quantified profound differences in flow velocity between pericentral and periportal regions within lobules. Even more unexpected are the differences in flow velocity seen between sinusoids that are side by side and fluctuations seen within individual vascular segments over seconds. STAFF is a powerful new tool capable of providing novel insights by enabling measurement of the complex spatiotemporal dynamics of capillary flow.
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Affiliation(s)
- Sherry G Clendenon
- Biocomplexity Institute, Indiana University; Department of Intelligent Systems Engineering, Indiana University
| | - Xiao Fu
- Biocomplexity Institute, Indiana University; Department of Physics, Indiana University
| | | | | | - James P Sluka
- Biocomplexity Institute, Indiana University; Department of Intelligent Systems Engineering, Indiana University
| | | | - Henry Mang
- Department of Medicine, Indiana University
| | | | | | | | - James A Glazier
- Biocomplexity Institute, Indiana University; Department of Intelligent Systems Engineering, Indiana University
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28
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Rockne RC, Hawkins-Daarud A, Swanson KR, Sluka JP, Glazier JA, Macklin P, Hormuth DA, Jarrett AM, Lima EABF, Tinsley Oden J, Biros G, Yankeelov TE, Curtius K, Al Bakir I, Wodarz D, Komarova N, Aparicio L, Bordyuh M, Rabadan R, Finley SD, Enderling H, Caudell J, Moros EG, Anderson ARA, Gatenby RA, Kaznatcheev A, Jeavons P, Krishnan N, Pelesko J, Wadhwa RR, Yoon N, Nichol D, Marusyk A, Hinczewski M, Scott JG. The 2019 mathematical oncology roadmap. Phys Biol 2019; 16:041005. [PMID: 30991381 PMCID: PMC6655440 DOI: 10.1088/1478-3975/ab1a09] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.
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Affiliation(s)
- Russell C Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA 91010, United States of America. Author to whom any correspondence should be addressed
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29
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Clendenon SG, Fu X, Von Hoene RA, Clendenon JL, Sluka JP, Winfree S, Mang H, Martinez M, Filson AJ, Klaunig JE, Glazier JA, Dunn KW. A simple automated method for continuous fieldwise measurement of microvascular hemodynamics. Microvasc Res 2018; 123:7-13. [PMID: 30502365 DOI: 10.1016/j.mvr.2018.11.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 11/27/2018] [Accepted: 11/27/2018] [Indexed: 01/25/2023]
Abstract
Microvascular perfusion dynamics are vital to physiological function and are frequently dysregulated in injury and disease. Typically studies measure microvascular flow in a few selected vascular segments over limited time, failing to capture spatial and temporal variability. To quantify microvascular flow in a more complete and unbiased way we developed STAFF (Spatial Temporal Analysis of Fieldwise Flow), a macro for FIJI open-source image analysis software. Using high-speed microvascular flow movies, STAFF generates kymographs for every time interval for every vascular segment, calculates flow velocities from red blood cell shadow angles, and outputs the data as color-coded velocity map movies and spreadsheets. In untreated mice, analyses demonstrated profound variation even between adjacent sinusoids over seconds. In acetaminophen-treated mice we detected flow reduction localized to pericentral regions. STAFF is a powerful new tool capable of providing novel insights by enabling measurement of the complex spatiotemporal dynamics of microvascular flow.
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Affiliation(s)
- Sherry G Clendenon
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Xiao Fu
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Physics, Indiana University, Bloomington, IN, USA
| | - Robert A Von Hoene
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | | | - James P Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Seth Winfree
- Department of Medicine, Indiana University, Indianapolis, IN, USA
| | - Henry Mang
- Department of Medicine, Indiana University, Indianapolis, IN, USA
| | | | - Adele J Filson
- Department of Medicine, Indiana University, Indianapolis, IN, USA
| | - James E Klaunig
- School of Public Health, Indiana University, Bloomington, IN, USA
| | - James A Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Kenneth W Dunn
- Department of Medicine, Indiana University, Indianapolis, IN, USA.
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30
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Fu X, Sluka JP, Clendenon SG, Dunn KW, Wang Z, Klaunig JE, Glazier JA. Modeling of xenobiotic transport and metabolism in virtual hepatic lobule models. PLoS One 2018; 13:e0198060. [PMID: 30212461 PMCID: PMC6136710 DOI: 10.1371/journal.pone.0198060] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 08/23/2018] [Indexed: 12/29/2022] Open
Abstract
Computational models of normal liver function and xenobiotic induced liver damage are increasingly being used to interpret in vitro and in vivo data and as an approach to the de novo prediction of the liver’s response to xenobiotics. The microdosimetry (dose at the level of individual cells) of xenobiotics vary spatially within the liver because of both compound-independent and compound-dependent factors. In this paper, we build model liver lobules to investigate the interplay between vascular structure, blood flow and cellular transport that lead to regional variations in microdosimetry. We then compared simulation results obtained using this complex spatial model with a simpler linear pipe model of a sinusoid and a very simple single box model. We found that variations in diffusive transport, transporter-mediated transport and metabolism, coupled with complex liver sinusoid architecture and blood flow distribution, led to three essential patterns of xenobiotic exposure within the virtual liver lobule: (1) lobular-wise uniform, (2) radially varying and (3) both radially and azimuthally varying. We propose to use these essential patterns of exposure as a reference for selection of model representations when a computational study involves modeling detailed hepatic responses to xenobiotics.
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Affiliation(s)
- Xiao Fu
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Physics, Indiana University, Bloomington, IN, United States of America
| | - James P. Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
- * E-mail:
| | - Sherry G. Clendenon
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
| | - Kenneth W. Dunn
- School of Medicine, Indiana University, Indianapolis, IN, United States of America
| | - Zemin Wang
- School of Public Health, Indiana University, Bloomington, IN, United States of America
| | - James E. Klaunig
- School of Public Health, Indiana University, Bloomington, IN, United States of America
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
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31
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Abstract
Virus capsids are polymeric protein shells that protect the viral cargo. About half of known virus families have icosahedral capsids that self-assemble from tens to thousands of subunits. Capsid disassembly is critical to the lifecycles of many viruses yet is poorly understood. Here, we apply a graph and percolation theory to examine the effect of removing capsid subunits on capsid stability and fragmentation. Based on the structure of the icosahedral capsid of hepatitis B virus (HBV), we constructed a graph of rhombic subunits arranged with icosahedral symmetry. Though our approach neglects dependence on energetics, time, and molecular detail, it quantitatively predicts a percolation phase transition consistent with recent in vitro studies of HBV capsid dissociation. While the stability of the capsid graph followed a gradual quadratic decay, the rhombic tiling abruptly fragmented when we removed more than 25% of the 120 subunits, near the percolation threshold observed experimentally. This threshold may also affect results of capsid assembly, which also experimentally produces a preponderance of 90 mer intermediates, as the intermediate steps in these reactions are reversible and can thus resemble dissociation. Application of percolation theory to understanding capsid association and dissociation may prove a general approach to relating virus biology to the underlying biophysics of the virus particle.
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Affiliation(s)
- Nicholas E Brunk
- Department of Molecular and Cellular Biochemistry, Indiana University, Bloomington, IN 47405, United States of America. Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, United States of America
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32
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Somogyi E, Hagar A, Glazier JA. TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY. Proc Winter Simul Conf 2017; 2016:1230-1240. [PMID: 29282379 DOI: 10.1109/wsc.2016.7822179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Living tissues are dynamic, heterogeneous compositions of objects, including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes. Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology (CCOPM) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models.
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Affiliation(s)
- Endre Somogyi
- Dept. of Computer Science, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
| | - Amit Hagar
- Dept. of History & Philosophy of Science & Medicine, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
| | - James A Glazier
- Dept. of Intelligent Systems Engineering, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
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33
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Chen KY, Srinivasan T, Tung KL, Belmonte JM, Wang L, Murthy PKL, Choi J, Rakhilin N, King S, Varanko AK, Witherspoon M, Nishimura N, Glazier JA, Lipkin SM, Bu P, Shen X. A Notch positive feedback in the intestinal stem cell niche is essential for stem cell self-renewal. Mol Syst Biol 2017; 13:927. [PMID: 28455349 PMCID: PMC5408779 DOI: 10.15252/msb.20167324] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The intestinal epithelium is the fastest regenerative tissue in the body, fueled by fast‐cycling stem cells. The number and identity of these dividing and migrating stem cells are maintained by a mosaic pattern at the base of the crypt. How the underlying regulatory scheme manages this dynamic stem cell niche is not entirely clear. We stimulated intestinal organoids with Notch ligands and inhibitors and discovered that intestinal stem cells employ a positive feedback mechanism via direct Notch binding to the second intron of the Notch1 gene. Inactivation of the positive feedback by CRISPR/Cas9 mutation of the binding sequence alters the mosaic stem cell niche pattern and hinders regeneration in organoids. Dynamical system analysis and agent‐based multiscale stochastic modeling suggest that the positive feedback enhances the robustness of Notch‐mediated niche patterning. This study highlights the importance of feedback mechanisms in spatiotemporal control of the stem cell niche.
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Affiliation(s)
- Kai-Yuan Chen
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.,Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Tara Srinivasan
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Kuei-Ling Tung
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
| | - Julio M Belmonte
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, USA
| | - Lihua Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.,Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
| | | | - Jiahn Choi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Nikolai Rakhilin
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.,Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Sarah King
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | | | - Mavee Witherspoon
- School of Mechanical Aerospace Engineering, Cornell University, Ithaca, NY, USA
| | - Nozomi Nishimura
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - James A Glazier
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, USA
| | - Steven M Lipkin
- Departments of Medicine, Genetic Medicine and Surgery, Weill Cornell Medical College, New York, NY, USA
| | - Pengcheng Bu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA .,Key Laboratory of RNA Biology, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Xiling Shen
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA .,Department of Biomedical Engineering, Duke University, Durham, NC, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
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Somogyi E, Glazier JA. A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES. Symp Theory Model Simul 2017; 2017:14. [PMID: 29303160 PMCID: PMC5749416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment.
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Affiliation(s)
- Endre Somogyi
- Department of Computer Science, Indiana University, Bloomington, IN 47405
| | - James A Glazier
- Department of Intelligent Systems Engineering, Biocomplexity Institute, Indiana University, Bloomington, IN 47405
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de Almeida RMC, Clendenon SG, Richards WG, Boedigheimer M, Damore M, Rossetti S, Harris PC, Herbert BS, Xu WM, Wandinger-Ness A, Ward HH, Glazier JA, Bacallao RL. Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD. Hum Genomics 2016; 10:37. [PMID: 27871310 PMCID: PMC5117508 DOI: 10.1186/s40246-016-0095-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Accepted: 11/04/2016] [Indexed: 12/18/2022] Open
Abstract
Background Autosomal dominant polycystic kidney disease (ADPKD) causes progressive loss of renal function in adults as a consequence of the accumulation of cysts. ADPKD is the most common genetic cause of end-stage renal disease. Mutations in polycystin-1 occur in 87% of cases of ADPKD and mutations in polycystin-2 are found in 12% of ADPKD patients. The complexity of ADPKD has hampered efforts to identify the mechanisms underlying its pathogenesis. No current FDA (Federal Drug Administration)-approved therapies ameliorate ADPKD progression. Results We used the de Almeida laboratory’s sensitive new transcriptogram method for whole-genome gene expression data analysis to analyze microarray data from cell lines developed from cell isolates of normal kidney and of both non-cystic nephrons and cysts from the kidney of a patient with ADPKD. We compared results obtained using standard Ingenuity Volcano plot analysis, Gene Set Enrichment Analysis (GSEA) and transcriptogram analysis. Transcriptogram analysis confirmed the findings of Ingenuity, GSEA, and published analysis of ADPKD kidney data and also identified multiple new expression changes in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to cell growth, cell death, genetic information processing, nucleotide metabolism, signal transduction, immune response, response to stimulus, cellular processes, ion homeostasis and transport and cofactors, vitamins, amino acids, energy, carbohydrates, drugs, lipids, and glycans. Transcriptogram analysis also provides significance metrics which allow us to prioritize further study of these pathways. Conclusions Transcriptogram analysis identifies novel pathways altered in ADPKD, providing new avenues to identify both ADPKD’s mechanisms of pathogenesis and pharmaceutical targets to ameliorate the progression of the disease. Electronic supplementary material The online version of this article (doi:10.1186/s40246-016-0095-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rita M C de Almeida
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, 47405, USA.,Instituto de Física and Instituto Nacional de Ciência e Tecnologia, Universidade Federal do Rio Grande do Sul, 91501-970, Porto Alegre, RS, Brazil
| | - Sherry G Clendenon
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 47405, USA
| | | | | | - Michael Damore
- AMGEN Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320-1799, USA
| | - Sandro Rossetti
- Division of Nephrology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Peter C Harris
- Division of Nephrology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Britney-Shea Herbert
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Wei Min Xu
- Division of Nephrology, Department of Medicine, Richard Roudebush VAMC and Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Angela Wandinger-Ness
- Department of Pathology MSC08-4640 and Cancer Research and Treatment Center, University of New Mexico Health Sciences Center, Albuquerque, NM, 87131, USA
| | - Heather H Ward
- Division of Nephrology, Department of Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, 87131, USA
| | - James A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 47405, USA
| | - Robert L Bacallao
- Division of Nephrology, Department of Medicine, Richard Roudebush VAMC and Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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36
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Gast TJ, Fu X, Gens JS, Glazier JA. A Computational Model of Peripheral Photocoagulation for the Prevention of Progressive Diabetic Capillary Occlusion. J Diabetes Res 2016; 2016:2508381. [PMID: 27847828 PMCID: PMC5099465 DOI: 10.1155/2016/2508381] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 08/02/2016] [Accepted: 09/29/2016] [Indexed: 01/17/2023] Open
Abstract
We developed a computational model of the propagation of retinal ischemia in diabetic retinopathy and analyzed the consequences of various patterns and sizes of burns in peripheral retinal photocoagulation. The model addresses retinal ischemia as a phenomenon of adverse local feedback in which once a capillary is occluded there is an elevated probability of occlusion of adjacent capillaries resulting in enlarging areas of retinal ischemia as is commonly seen clinically. Retinal burns of different sizes and patterns, treated as local oxygen sources, are predicted to have different effects on the propagation of retinal ischemia. The patterns of retinal burns are optimized with regard to minimization of the sum of the photocoagulated retina and computer predicted ischemic retina. Our simulations show that certain patterns of retinal burns are effective at preventing the spatial spread of ischemia by creating oxygenated boundaries across which the ischemia does not propagate. This model makes no statement about current PRP treatment of avascular peripheral retina and notes that the usual spot sizes used in PRP will not prevent ischemic propagation in still vascularized retinal areas. The model seems to show that a properly patterned laser treatment of still vascularized peripheral retina may be able to prevent or at least constrain the propagation of diabetic retinal ischemia in those retinal areas with intact capillaries.
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Affiliation(s)
- Thomas J. Gast
- School of Optometry, Indiana University, Bloomington, IN 47405, USA
| | - Xiao Fu
- The Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
- Physics Department, Indiana University, Bloomington, IN 47405, USA
| | - John Scott Gens
- The Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
- Physics Department, Indiana University, Bloomington, IN 47405, USA
| | - James A. Glazier
- The Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
- Physics Department, Indiana University, Bloomington, IN 47405, USA
- School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
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37
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Somogyi E, Sluka JP, Glazier JA. Formalizing Knowledge in Multi-Scale Agent-Based Simulations. Model Driven Eng Lang Syst 2016; 16:115-122. [PMID: 29338063 PMCID: PMC5761068 DOI: 10.1145/2976767.2976790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused.
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Affiliation(s)
- Endre Somogyi
- Department of Computer Science, Biocomplexity Institute Indiana University Bloomington, IN 47405
| | - James P Sluka
- Department of Intelligent Systems Engineering Biocomplexity Institute Indiana University Bloomington, IN 47405
| | - James A Glazier
- Department of Intelligent Systems Engineering Biocomplexity Institute Indiana University Bloomington, IN 47405
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Abstract
In convergent-extension (CE), a planar-polarized epithelial tissue elongates (extends) in-plane in one direction while shortening (converging) in the perpendicular in-plane direction, with the cells both elongating and intercalating along the converging axis. CE occurs during the development of most multicellular organisms. Current CE models assume cell or tissue asymmetry, but neglect the preferential filopodial activity along the convergent axis observed in many tissues. We propose a cell-based CE model based on asymmetric filopodial tension forces between cells and investigate how cell-level filopodial interactions drive tissue-level CE. The final tissue geometry depends on the balance between external rounding forces and cell-intercalation traction. Filopodial-tension CE is robust to relatively high levels of planar cell polarity misalignment and to the presence of non-active cells. Addition of a simple mechanical feedback between cells fully rescues and even improves CE of tissues with high levels of polarity misalignments. Our model extends easily to three dimensions, with either one converging and two extending axes, or two converging and one extending axes, producing distinct tissue morphologies, as observed in vivo. The development of an embryo from a fertilized egg to an adult organism requires not only cell proliferation and differentiation, but also numerous types of tissue restructuring. The development of a relatively round initial embryo into one elongated along its rostral-caudal axis involves coordinated tissue elongation and cell reorganization in one or more groups of cells or tissues. Counterintuitively, in many organisms, cells in elongating tissues elongate and increase their protrusive activity in the direction perpendicular to the axis of elongation (convergent extension). Experimental and theoretical studies have not determined how this cell-level oriented protrusive activity leads to observed tissue-level changes in morphology. We propose a filopodial-tension model that shows how tension from oriented cell protrusions leads to observed patterns of tissue CE.
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Affiliation(s)
- Julio M Belmonte
- Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana, United States of America
| | - Maciej H Swat
- Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana, United States of America
| | - James A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, Indiana, United States of America
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Abstract
An explanatory computational model is developed of the contiguous areas of retinal capillary loss which play a large role in diabetic maculapathy and diabetic retinal neovascularization. Strictly random leukocyte mediated capillary occlusion cannot explain the occurrence of large contiguous areas of retinal ischemia. Therefore occlusion of an individual capillary must increase the probability of occlusion of surrounding capillaries. A retinal perifoveal vascular sector as well as a peripheral retinal capillary network and a deleted hexagonal capillary network are modelled using Compucell3D. The perifoveal modelling produces a pattern of spreading capillary loss with associated macular edema. In the peripheral network, spreading ischemia results from the progressive loss of the ladder capillaries which connect peripheral arterioles and venules. System blood flow was elevated in the macular model before a later reduction in flow in cases with progression of capillary occlusions. Simulations differing only in initial vascular network structures but with identical dynamics for oxygen, growth factors and vascular occlusions, replicate key clinical observations of ischemia and macular edema in the posterior pole and ischemia in the retinal periphery. The simulation results also seem consistent with quantitative data on macular blood flow and qualitative data on venous oxygenation. One computational model applied to distinct capillary networks in different retinal regions yielded results comparable to clinical observations in those regions.
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Affiliation(s)
- Xiao Fu
- The Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
- Department of Physics, Indiana University, Bloomington, Indiana, United States of America
| | - John Scott Gens
- The Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
- Department of Physics, Indiana University, Bloomington, Indiana, United States of America
| | - James A. Glazier
- The Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
- Department of Physics, Indiana University, Bloomington, Indiana, United States of America
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Stephen A. Burns
- School of Optometry, Indiana University, Bloomington, Indiana, United States of America
| | - Thomas J. Gast
- School of Optometry, Indiana University, Bloomington, Indiana, United States of America
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40
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Belmonte JM, Clendenon SG, Oliveira GM, Swat MH, Greene EV, Jeyaraman S, Glazier JA, Bacallao RL. Virtual-tissue computer simulations define the roles of cell adhesion and proliferation in the onset of kidney cystic disease. Mol Biol Cell 2016; 27:3673-3685. [PMID: 27193300 PMCID: PMC5221598 DOI: 10.1091/mbc.e16-01-0059] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Accepted: 05/10/2016] [Indexed: 12/15/2022] Open
Abstract
Virtual-tissue modeling is used to model emergent cyst growth in polycystic kidney disease. Model predictions, confirmed experimentally, show that decreased cell adhesion is necessary to produce stalk cysts, and loss of contact inhibition causes saccular cysts. Virtual-tissue modeling can be used to fully explore cell- and tissue-based behaviors. In autosomal dominant polycystic kidney disease (ADPKD), cysts accumulate and progressively impair renal function. Mutations in PKD1 and PKD2 genes are causally linked to ADPKD, but how these mutations drive cell behaviors that underlie ADPKD pathogenesis is unknown. Human ADPKD cysts frequently express cadherin-8 (cad8), and expression of cad8 ectopically in vitro suffices to initiate cystogenesis. To explore cell behavioral mechanisms of cad8-driven cyst initiation, we developed a virtual-tissue computer model. Our simulations predicted that either reduced cell–cell adhesion or reduced contact inhibition of proliferation triggers cyst induction. To reproduce the full range of cyst morphologies observed in vivo, changes in both cell adhesion and proliferation are required. However, only loss-of-adhesion simulations produced morphologies matching in vitro cad8-induced cysts. Conversely, the saccular cysts described by others arise predominantly by decreased contact inhibition, that is, increased proliferation. In vitro experiments confirmed that cell–cell adhesion was reduced and proliferation was increased by ectopic cad8 expression. We conclude that adhesion loss due to cadherin type switching in ADPKD suffices to drive cystogenesis. Thus, control of cadherin type switching provides a new target for therapeutic intervention.
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Affiliation(s)
- Julio M Belmonte
- Biocomplexity Institute, Physics Department, Indiana University, Bloomington, IN 47405
| | - Sherry G Clendenon
- Biocomplexity Institute, Physics Department, Indiana University, Bloomington, IN 47405
| | - Guilherme M Oliveira
- Biocomplexity Institute, Physics Department, Indiana University, Bloomington, IN 47405
| | - Maciej H Swat
- Biocomplexity Institute, Physics Department, Indiana University, Bloomington, IN 47405
| | - Evan V Greene
- Division of Nephrology, Richard L. Roudebush VA Medical Center, and Indiana University School of Medicine, Indianapolis, IN 46202
| | - Srividhya Jeyaraman
- Biocomplexity Institute, Physics Department, Indiana University, Bloomington, IN 47405
| | - James A Glazier
- Biocomplexity Institute, Physics Department, Indiana University, Bloomington, IN 47405
| | - Robert L Bacallao
- Division of Nephrology, Richard L. Roudebush VA Medical Center, and Indiana University School of Medicine, Indianapolis, IN 46202
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41
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Swat MH, Thomas GL, Shirinifard A, Clendenon SG, Glazier JA. Emergent Stratification in Solid Tumors Selects for Reduced Cohesion of Tumor Cells: A Multi-Cell, Virtual-Tissue Model of Tumor Evolution Using CompuCell3D. PLoS One 2015; 10:e0127972. [PMID: 26083246 PMCID: PMC4470639 DOI: 10.1371/journal.pone.0127972] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2014] [Accepted: 04/21/2015] [Indexed: 12/30/2022] Open
Abstract
Tumor cells and structure both evolve due to heritable variation of cell behaviors and selection over periods of weeks to years (somatic evolution). Micro-environmental factors exert selection pressures on tumor-cell behaviors, which influence both the rate and direction of evolution of specific behaviors, especially the development of tumor-cell aggression and resistance to chemotherapies. In this paper, we present, step-by-step, the development of a multi-cell, virtual-tissue model of tumor somatic evolution, simulated using the open-source CompuCell3D modeling environment. Our model includes essential cell behaviors, microenvironmental components and their interactions. Our model provides a platform for exploring selection pressures leading to the evolution of tumor-cell aggression, showing that emergent stratification into regions with different cell survival rates drives the evolution of less cohesive cells with lower levels of cadherins and higher levels of integrins. Such reduced cohesivity is a key hallmark in the progression of many types of solid tumors.
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Affiliation(s)
- Maciej H. Swat
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, Indiana, USA
- * E-mail:
| | - Gilberto L. Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Abbas Shirinifard
- St. Jude Children’s Research Hospital, Department of Information Sciences, Division of Clinical Informatics, Memphis, USA
| | - Sherry G. Clendenon
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, Indiana, USA
| | - James A. Glazier
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, Indiana, USA
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Somogyi ET, Bouteiller JM, Glazier JA, König M, Medley JK, Swat MH, Sauro HM. libRoadRunner: a high performance SBML simulation and analysis library. Bioinformatics 2015; 31:3315-21. [PMID: 26085503 PMCID: PMC4607739 DOI: 10.1093/bioinformatics/btv363] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Accepted: 06/05/2015] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION This article presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations. RESULTS libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python interface. Its Python Application Programming Interface (API) is similar to the APIs of MATLAB ( WWWMATHWORKSCOM: ) and SciPy ( HTTP//WWWSCIPYORG/: ), making it fast and easy to learn. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) including several SBML extensions (composition and distributions). It offers multiple deterministic and stochastic integrators, as well as tools for steady-state analysis, stability analysis and structural analysis of the stoichiometric matrix. AVAILABILITY AND IMPLEMENTATION libRoadRunner binary distributions are available for Mac OS X, Linux and Windows. The library is licensed under Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi. http://www.libroadrunner.org provides online documentation, full build instructions, binaries and a git source repository. CONTACTS hsauro@u.washington.edu or somogyie@indiana.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Endre T Somogyi
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, 47405, USA
| | - Jean-Marie Bouteiller
- Biomedical Engineering Department, University of Southern California, Los Angeles, CA, 90089, USA
| | - James A Glazier
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, 47405, USA
| | - Matthias König
- Department of Computational Systems Biochemistry, University Medicine Charité Berlin, 10117, Berlin, Germany and
| | - J Kyle Medley
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Maciej H Swat
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, 47405, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
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Thomas GL, Belmonte JM, Graner F, Glazier JA, de Almeida RM. 3D simulations of wet foam coarsening evidence a self similar growth regime. Colloids Surf A Physicochem Eng Asp 2015; 473:109-114. [PMID: 27630449 PMCID: PMC5019577 DOI: 10.1016/j.colsurfa.2015.02.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In wet liquid foams, slow diffusion of gas through bubble walls changes bubble pressure, volume and wall curvature. Large bubbles grow at the expenses of smaller ones. The smaller the bubble, the faster it shrinks. As the number of bubbles in a given volume decreases in time, the average bubble size increases: i.e. the foam coarsens. During coarsening, bubbles also move relative to each other, changing bubble topology and shape, while liquid moves within the regions separating the bubbles. Analyzing the combined effects of these mechanisms requires examining a volume with enough bubbles to provide appropriate statistics throughout coarsening. Using a Cellular Potts model, we simulate these mechanisms during the evolution of three-dimensional foams with wetnesses of ϕ = 0.00, 0.05 and 0.20. We represent the liquid phase as an ensemble of many small fluid particles, which allows us to monitor liquid flow in the region between bubbles. The simulations begin with 2 × 105 bubbles for ϕ = 0.00 and 1.25 × 105 bubbles for ϕ = 0.05 and 0.20, allowing us to track the distribution functions for bubble size, topology and growth rate over two and a half decades of volume change. All simulations eventually reach a self-similar growth regime, with the distribution functions time independent and the number of bubbles decreasing with time as a power law whose exponent depends on the wetness.
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Affiliation(s)
- Gilberto L. Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, C.P. 15051 - 91501-970 Porto Alegre, RS, Brazil
| | - Julio M. Belmonte
- European Molecular Biology Laboratory Heidelberg, Meyerhofstr. 1, 69117 Heidelberg, Germany
- Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana, 47405, United States of America
| | - François Graner
- Matière et Systèmes Complexes, Université Paris Diderot, CNRS UMR 7057, 10 rue Alice Domon et Léonie Duquet, F-75205 Paris Cedex 13, France
| | - James A. Glazier
- Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana, 47405, United States of America
| | - Rita M.C. de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, C.P. 15051 - 91501-970 Porto Alegre, RS, Brazil
- Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana, 47405, United States of America
- Instituto Nacional de Ciência e Tecnologia - Sistemas Complexos, Av. Bento Gonçalves 9500, C.P. 15051 - 91501-970 Porto Alegre, RS, Brazil
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Sluka JP, Shirinifard A, Swat M, Cosmanescu A, Heiland RW, Glazier JA. The cell behavior ontology: describing the intrinsic biological behaviors of real and model cells seen as active agents. Bioinformatics 2014; 30:2367-74. [PMID: 24755304 PMCID: PMC4133580 DOI: 10.1093/bioinformatics/btu210] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Revised: 03/18/2014] [Accepted: 04/15/2014] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Currently, there are no ontologies capable of describing both the spatial organization of groups of cells and the behaviors of those cells. The lack of a formalized method for describing the spatiality and intrinsic biological behaviors of cells makes it difficult to adequately describe cells, tissues and organs as spatial objects in living tissues, in vitro assays and in computational models of tissues. RESULTS We have developed an OWL-2 ontology to describe the intrinsic physical and biological characteristics of cells and tissues. The Cell Behavior Ontology (CBO) provides a basis for describing the spatial and observable behaviors of cells and extracellular components suitable for describing in vivo, in vitro and in silico multicell systems. Using the CBO, a modeler can create a meta-model of a simulation of a biological model and link that meta-model to experiment or simulation results. Annotation of a multicell model and its computational representation, using the CBO, makes the statement of the underlying biology explicit. The formal representation of such biological abstraction facilitates the validation, falsification, discovery, sharing and reuse of both models and experimental data. AVAILABILITY AND IMPLEMENTATION The CBO, developed using Protégé 4, is available at http://cbo.biocomplexity.indiana.edu/cbo/ and at BioPortal (http://bioportal.bioontology.org/ontologies/CBO).
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Affiliation(s)
- James P Sluka
- Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
| | - Abbas Shirinifard
- Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
| | - Maciej Swat
- Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
| | - Alin Cosmanescu
- Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
| | - Randy W Heiland
- Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
| | - James A Glazier
- Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, IN 47405, USA
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Abstract
The formation of body segments (somites) in vertebrate embryos is accompanied by molecular oscillations (segmentation clock). Interaction of this oscillator with a wave traveling along the body axis (the clock-and-wavefront model) is generally believed to control somite number, size, and axial identity. Here we show that a clock-and-wavefront mechanism is unnecessary for somite formation. Non-somite mesoderm treated with Noggin generates many somites that form simultaneously, without cyclic expression of Notch-pathway genes, yet have normal size, shape, and fate. These somites have axial identity: The Hox code is fixed independently of somite fate. However, these somites are not subdivided into rostral and caudal halves, which is necessary for neural segmentation. We propose that somites are self-organizing structures whose size and shape is controlled by local cell-cell interactions.
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Affiliation(s)
- Ana S Dias
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK
| | - Irene de Almeida
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK
| | - Julio M Belmonte
- Department of Physics, Biocomplexity Institute, University of Indiana at Bloomington, Simon Hall MSB1, 047G, 212 South Hawthorne Drive, Bloomington, IN 47405-7003, USA
| | - James A Glazier
- Department of Physics, Biocomplexity Institute, University of Indiana at Bloomington, Simon Hall MSB1, 047G, 212 South Hawthorne Drive, Bloomington, IN 47405-7003, USA
| | - Claudio D Stern
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK
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Gens JS, Srividhya J, Belmonte JM, Hester SD, Glazier JA. Multiscale modeling goes out on a limb: in silico simulations of developmental mechanisms shared between somitogenesis and the developing embryonic avian limb bud. FASEB J 2013. [DOI: 10.1096/fasebj.27.1_supplement.964.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- J. Scott Gens
- Biocomplexity InstituteDept. of PhysicsIndiana UniversityBloomingtonIN
| | | | - Julio M. Belmonte
- Biocomplexity InstituteDept. of PhysicsIndiana UniversityBloomingtonIN
| | | | - James A. Glazier
- Biocomplexity InstituteDept. of PhysicsIndiana UniversityBloomingtonIN
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Shirinifard A, McCollum CW, Bolin MB, Gustafsson JÅ, Glazier JA, Clendenon SG. 3D quantitative analyses of angiogenic sprout growth dynamics. Dev Dyn 2013; 242:518-26. [PMID: 23417958 DOI: 10.1002/dvdy.23946] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Revised: 01/16/2013] [Accepted: 02/03/2013] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Zebrafish intersegmental vessel (ISV) growth is widely used to study angiogenesis and to screen drugs and toxins that perturb angiogenesis. Most current ISV growth assays observe the presence or absence of ISVs or perturbation of ISV morphology but do not measure growth dynamics. We have developed a four-dimensional (4D, space plus time) quantitative analysis of angiogenic sprout growth dynamics for characterization of both normal and perturbed growth. RESULTS We tracked the positions of the ISV base and tip for each ISV sprout in 4D. Despite immobilization, zebrafish embryos translocated globally and non-uniformly during development. We used displacement of the ISV base and the angle between the ISV and the dorsal aorta to correct for displacement and rotation during development. From corrected tip cell coordinates, we computed average ISV trajectories. We fitted a quadratic curve to the average ISV trajectories to produce a canonical ISV trajectory for each experimental group, arsenic treated and untreated. From the canonical ISV trajectories, we computed curvature, average directed migration speed and directionality. Canonical trajectories from treated (arsenic exposed) and untreated groups differed in curvature, average directed migration speed and angle between the ISV and dorsal aorta. CONCLUSIONS 4D analysis of angiogenic sprout growth dynamics: (1) Allows quantitative assessment of ISV growth dynamics and perturbation, and (2) provides critical inputs for computational models of angiogenesis.
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Affiliation(s)
- Abbas Shirinifard
- Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana 47405-7003, USA.
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Gens JS, Jeyaraman S, Glazier JA. Mathematical modeling of wound healing using Compucell3D multicell modeling environment. FASEB J 2012. [DOI: 10.1096/fasebj.26.1_supplement.916.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- John Scott Gens
- Biocomplexity InstituteDept. of PhysicsIndiana UniversityBloomingtonIN
| | | | - James A. Glazier
- Biocomplexity InstituteDept. of PhysicsIndiana UniversityBloomingtonIN
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Abstract
The study of how cells interact to produce tissue development, homeostasis, or diseases was, until recently, almost purely experimental. Now, multi-cell computer simulation methods, ranging from relatively simple cellular automata to complex immersed-boundary and finite-element mechanistic models, allow in silico study of multi-cell phenomena at the tissue scale based on biologically observed cell behaviors and interactions such as movement, adhesion, growth, death, mitosis, secretion of chemicals, chemotaxis, etc. This tutorial introduces the lattice-based Glazier-Graner-Hogeweg (GGH) Monte Carlo multi-cell modeling and the open-source GGH-based CompuCell3D simulation environment that allows rapid and intuitive modeling and simulation of cellular and multi-cellular behaviors in the context of tissue formation and subsequent dynamics. We also present a walkthrough of four biological models and their associated simulations that demonstrate the capabilities of the GGH and CompuCell3D.
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Affiliation(s)
- Maciej H Swat
- Department of Physics, Biocomplexity Institute, Indiana University, Bloomington, Indiana, USA
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50
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Abstract
The actions of cell adhesion molecules, in particular, cadherins during embryonic development and morphogenesis more generally, regulate many aspects of cellular interactions, regulation and signaling. Often, a gradient of cadherin expression levels drives collective and relative cell motions generating macroscopic cell sorting. Computer simulations of cell sorting have focused on the interactions of cells with only a few discrete adhesion levels between cells, ignoring biologically observed continuous variations in expression levels and possible nonlinearities in molecular binding. In this paper, we present three models relating the surface density of cadherins to the net intercellular adhesion and interfacial tension for both discrete and continuous levels of cadherin expression. We then use then the Glazier-Graner-Hogeweg (GGH) model to investigate how variations in the distribution of the number of cadherins per cell and in the choice of binding model affect cell sorting. We find that an aggregate with a continuous variation in the level of a single type of cadherin molecule sorts more slowly than one with two levels. The rate of sorting increases strongly with the interfacial tension, which depends both on the maximum difference in number of cadherins per cell and on the binding model. Our approach helps connect signaling at the molecular level to tissue-level morphogenesis.
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Affiliation(s)
- Ying Zhang
- Cancer and Developmental Biology Laboratory, National Cancer Institute, Frederick, Maryland, United States of America.
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