1
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Osborne JM. An adaptive numerical method for multi-cellular simulations of tissue development and maintenance. J Theor Biol 2024; 594:111922. [PMID: 39111542 DOI: 10.1016/j.jtbi.2024.111922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 08/22/2024]
Abstract
In recent years, multi-cellular models, where cells are represented as individual interacting entities, are becoming ever popular. This has led to a proliferation of novel methods and simulation tools. The first aim of this paper is to review the numerical methods utilised by multi-cellular modelling tools and to demonstrate which numerical methods are appropriate for simulations of tissue and organ development, maintenance, and disease. The second aim is to introduce an adaptive time-stepping algorithm and to demonstrate it's efficiency and accuracy. We focus on off-lattice, mechanics based, models where cell movement is defined by a series of first order ordinary differential equations, derived by assuming over-damped motion and balancing forces. We see that many numerical methods have been used, ranging from simple Forward Euler approaches through to higher order single-step methods like Runge-Kutta 4 and multi-step methods like Adams-Bashforth 2. Through a series of exemplar multi-cellular simulations, we see that if: care is taken to have events (births deaths and re-meshing/re-arrangements) occur on common time-steps; and boundaries are imposed on all sub-steps of numerical methods or implemented using forces, then all numerical methods can converge with the correct order. We introduce an adaptive time-stepping method and demonstrate that the best compromise between L∞ error and run-time is to use Runge-Kutta 4 with an increased time-step and moderate adaptivity. We see that a judicious choice of numerical method can speed the simulation up by a factor of 10-60 from the Forward Euler methods seen in Osborne et al. (2017), and a further speed up by a factor of 4 can be achieved by using an adaptive time-step.
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Affiliation(s)
- James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Victoria, Australia.
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2
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García García OR, Ortiz R, Moreno-Barbosa E, D-Kondo N, Faddegon B, Ramos-Méndez J. TOPAS-Tissue: A Framework for the Simulation of the Biological Response to Ionizing Radiation at the Multi-Cellular Level. Int J Mol Sci 2024; 25:10061. [PMID: 39337547 PMCID: PMC11431975 DOI: 10.3390/ijms251810061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Revised: 08/21/2024] [Accepted: 09/17/2024] [Indexed: 09/30/2024] Open
Abstract
This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0-8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear-quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.
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Affiliation(s)
- Omar Rodrigo García García
- Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla 72000, Mexico; (O.R.G.G.); (E.M.-B.)
| | - Ramon Ortiz
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA 94115, USA; (R.O.); (N.D.-K.); (B.F.)
| | - Eduardo Moreno-Barbosa
- Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla 72000, Mexico; (O.R.G.G.); (E.M.-B.)
| | - Naoki D-Kondo
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA 94115, USA; (R.O.); (N.D.-K.); (B.F.)
| | - Bruce Faddegon
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA 94115, USA; (R.O.); (N.D.-K.); (B.F.)
| | - Jose Ramos-Méndez
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA 94115, USA; (R.O.); (N.D.-K.); (B.F.)
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3
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Matzko RO, Konur S. BioNexusSentinel: a visual tool for bioregulatory network and cytohistological RNA-seq genetic expression profiling within the context of multicellular simulation research using ChatGPT-augmented software engineering. BIOINFORMATICS ADVANCES 2024; 4:vbae046. [PMID: 38571784 PMCID: PMC10990683 DOI: 10.1093/bioadv/vbae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/22/2024] [Accepted: 03/18/2024] [Indexed: 04/05/2024]
Abstract
Summary Motivated by the need to parameterize ongoing multicellular simulation research, this paper documents the culmination of a ChatGPT augmented software engineering cycle resulting in an integrated visual platform for efficient cytohistological RNA-seq and bioregulatory network exploration. As contrasted to other systems and synthetic biology tools, BioNexusSentinel was developed de novo to uniquely combine these features. Reactome served as the primary source of remotely accessible biological models, accessible using BioNexusSentinel's novel search engine and REST API requests. The innovative, feature-rich gene expression profiler component was developed to enhance the exploratory experience for the researcher, culminating in the cytohistological RNA-seq explorer based on Human Protein Atlas data. A novel cytohistological classifier would be integrated via pre-processed analysis of the RNA-seq data via R statistical language, providing for useful analytical functionality and good performance for the end-user. Implications of the work span prospects for model orthogonality evaluations, gap identification in network modelling, prototyped automatic kinetics parameterization, and downstream simulation and cellular biological state analysis. This unique computational biology software engineering collaboration with generative natural language processing artificial intelligence was shown to enhance worker productivity, with evident benefits in terms of accelerating coding and machine-human intelligence transfer. Availability and implementation BioNexusSentinel project releases, with corresponding data and installation instructions, are available at https://github.com/RichardMatzko/BioNexusSentinel.
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Affiliation(s)
- Richard Oliver Matzko
- School of Computer Science, AI and Electronics, University of Bradford, Bradford BD7 1HR, United Kingdom
| | - Savas Konur
- School of Computer Science, AI and Electronics, University of Bradford, Bradford BD7 1HR, United Kingdom
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4
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Cockx BJR, Foster T, Clegg RJ, Alden K, Arya S, Stekel DJ, Smets BF, Kreft JU. Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0. PLoS Comput Biol 2024; 20:e1011303. [PMID: 38422165 PMCID: PMC10947719 DOI: 10.1371/journal.pcbi.1011303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 03/18/2024] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.
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Affiliation(s)
- Bastiaan J. R. Cockx
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Tim Foster
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Robert J. Clegg
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Kieran Alden
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Sankalp Arya
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Dov J. Stekel
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Barth F. Smets
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Jan-Ulrich Kreft
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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5
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Johnson JA, Stein-O’Brien GL, Booth M, Heiland R, Kurtoglu F, Bergman DR, Bucher E, Deshpande A, Forjaz A, Getz M, Godet I, Lyman M, Metzcar J, Mitchell J, Raddatz A, Rocha H, Solorzano J, Sundus A, Wang Y, Gilkes D, Kagohara LT, Kiemen AL, Thompson ED, Wirtz D, Wu PH, Zaidi N, Zheng L, Zimmerman JW, Jaffee EM, Hwan Chang Y, Coussens LM, Gray JW, Heiser LM, Fertig EJ, Macklin P. Digitize your Biology! Modeling multicellular systems through interpretable cell behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.557982. [PMID: 37745323 PMCID: PMC10516032 DOI: 10.1101/2023.09.17.557982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.
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Affiliation(s)
- Jeanette A.I. Johnson
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Genevieve L. Stein-O’Brien
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins University. Baltimore, MD USA
| | - Max Booth
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Randy Heiland
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Furkan Kurtoglu
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Daniel R. Bergman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elmar Bucher
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - André Forjaz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Michael Getz
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Ines Godet
- Memorial Sloan Kettering Cancer Center. New York, NY USA
| | - Melissa Lyman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
- Department of Informatics, Indiana University. Bloomington, IN USA
| | - Jacob Mitchell
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Human Genetics, Johns Hopkins University. Baltimore, MD USA
| | - Andrew Raddatz
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University. Atlanta, GA USA
| | - Heber Rocha
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Jacobo Solorzano
- Centre de Recherches en Cancerologie de Toulouse. Toulouse, France
| | - Aneequa Sundus
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Danielle Gilkes
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Luciane T. Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Ashley L. Kiemen
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
| | | | - Denis Wirtz
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
- Department of Materials Science and Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Pei-Hsun Wu
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Neeha Zaidi
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Lei Zheng
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Jacquelyn W. Zimmerman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elizabeth M. Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Lisa M. Coussens
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University. Portland, OR USA
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Laura M. Heiser
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Elana J. Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
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6
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Du J, Zhou Y, Jin L, Sheng K. Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system. PLoS One 2023; 18:e0288721. [PMID: 37463167 DOI: 10.1371/journal.pone.0288721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop Gell (GPU Cell), a fast and memory-efficient open-source GPU-based hybrid computational modeling platform for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement.
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Affiliation(s)
- Jiayi Du
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Yu Zhou
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Lihua Jin
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Ke Sheng
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California, United States of America
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7
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Muratore IB, Garnier S. Ontogeny of collective behaviour. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220065. [PMID: 36802780 PMCID: PMC9939274 DOI: 10.1098/rstb.2022.0065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/21/2022] [Indexed: 02/21/2023] Open
Abstract
During their lifetime, superorganisms, like unitary organisms, undergo transformations that change the machinery of their collective behaviour. Here, we suggest that these transformations are largely understudied and propose that more systematic research into the ontogeny of collective behaviours is needed if we hope to better understand the link between proximate behavioural mechanisms and the development of collective adaptive functions. In particular, certain social insects engage in self-assemblage, forming dynamic and physically connected architectures with striking similarities to developing multicellular organisms, making them good model systems for ontogenetic studies of collective behaviour. However, exhaustive time series and three-dimensional data are required to thoroughly characterize the different life stages of the collective structures and the transitions between these stages. The well-established fields of embryology and developmental biology offer practical tools and theoretical frameworks that could speed up the acquisition of new knowledge about the formation, development, maturity and dissolution of social insect self-assemblages and, by extension, other superorganismal behaviours. We hope that this review will encourage an expansion of the ontogenetic perspective in the field of collective behaviour and, in particular, in self-assemblage research, which has far-reaching applications in robotics, computer science and regenerative medicine. This article is part of a discussion meeting issue 'Collective behaviour through time'.
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Affiliation(s)
| | - Simon Garnier
- Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
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8
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Echlin M, Aguilar B, Shmulevich I. Characterizing the Impact of Communication on Cellular and Collective Behavior Using a Three-Dimensional Multiscale Cellular Model. ENTROPY (BASEL, SWITZERLAND) 2023; 25:319. [PMID: 36832685 PMCID: PMC9955575 DOI: 10.3390/e25020319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/29/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Communication between cells enables the coordination that drives structural and functional complexity in biological systems. Both single and multicellular organisms have evolved diverse communication systems for a range of purposes, including synchronization of behavior, division of labor, and spatial organization. Synthetic systems are also increasingly being engineered to utilize cell-cell communication. While research has elucidated the form and function of cell-cell communication in many biological systems, our knowledge is still limited by the confounding effects of other biological phenomena at play and the bias of the evolutionary background. In this work, our goal is to push forward the context-free understanding of what impact cell-cell communication can have on cellular and population behavior to more fully understand the extent to which cell-cell communication systems can be utilized, modified, and engineered. We use an in silico model of 3D multiscale cellular populations, with dynamic intracellular networks interacting via diffusible signals. We focus on two key communication parameters: the effective interaction distance at which cells are able to interact and the receptor activation threshold. We found that cell-cell communication can be divided into six different forms along the parameter axes, three asocial and three social. We also show that cellular behavior, tissue composition, and tissue diversity are all highly sensitive to both the general form and specific parameters of communication even when the cellular network has not been biased towards that behavior.
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Affiliation(s)
- Moriah Echlin
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Boris Aguilar
- Institute for Systems Biology, Seattle, WA 98109, USA
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9
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Novel Ground-Up 3D Multicellular Simulators for Synthetic Biology CAD Integrating Stochastic Gillespie Simulations Benchmarked with Topologically Variable SBML Models. Genes (Basel) 2023; 14:genes14010154. [PMID: 36672895 PMCID: PMC9859520 DOI: 10.3390/genes14010154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023] Open
Abstract
The elevation of Synthetic Biology from single cells to multicellular simulations would be a significant scale-up. The spatiotemporal behavior of cellular populations has the potential to be prototyped in silico for computer assisted design through ergonomic interfaces. Such a platform would have great practical potential across medicine, industry, research, education and accessible archiving in bioinformatics. Existing Synthetic Biology CAD systems are considered limited regarding population level behavior, and this work explored the in silico challenges posed from biological and computational perspectives. Retaining the connection to Synthetic Biology CAD, an extension of the Infobiotics Workbench Suite was considered, with potential for the integration of genetic regulatory models and/or chemical reaction networks through Next Generation Stochastic Simulator (NGSS) Gillespie algorithms. These were executed using SBML models generated by in-house SBML-Constructor over numerous topologies and benchmarked in association with multicellular simulation layers. Regarding multicellularity, two ground-up multicellular solutions were developed, including the use of Unreal Engine 4 contrasted with CPU multithreading and Blender visualization, resulting in a comparison of real-time versus batch-processed simulations. In conclusion, high-performance computing and client-server architectures could be considered for future works, along with the inclusion of numerous biologically and physically informed features, whilst still pursuing ergonomic solutions.
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10
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Jayasinghe MK, Lee CY, Tran TTT, Tan R, Chew SM, Yeo BZJ, Loh WX, Pirisinu M, Le MTN. The Role of in silico Research in Developing Nanoparticle-Based Therapeutics. Front Digit Health 2022; 4:838590. [PMID: 35373184 PMCID: PMC8965754 DOI: 10.3389/fdgth.2022.838590] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 02/16/2022] [Indexed: 12/12/2022] Open
Abstract
Nanoparticles (NPs) hold great potential as therapeutics, particularly in the realm of drug delivery. They are effective at functional cargo delivery and offer a great degree of amenability that can be used to offset toxic side effects or to target drugs to specific regions in the body. However, there are many challenges associated with the development of NP-based drug formulations that hamper their successful clinical translation. Arguably, the most significant barrier in the way of efficacious NP-based drug delivery systems is the tedious and time-consuming nature of NP formulation—a process that needs to account for downstream effects, such as the onset of potential toxicity or immunogenicity, in vivo biodistribution and overall pharmacokinetic profiles, all while maintaining desirable therapeutic outcomes. Computational and AI-based approaches have shown promise in alleviating some of these restrictions. Via predictive modeling and deep learning, in silico approaches have shown the ability to accurately model NP-membrane interactions and cellular uptake based on minimal data, such as the physicochemical characteristics of a given NP. More importantly, machine learning allows computational models to predict how specific changes could be made to the physicochemical characteristics of a NP to improve functional aspects, such as drug retention or endocytosis. On a larger scale, they are also able to predict the in vivo pharmacokinetics of NP-encapsulated drugs, predicting aspects such as circulatory half-life, toxicity, and biodistribution. However, the convergence of nanomedicine and computational approaches is still in its infancy and limited in its applicability. The interactions between NPs, the encapsulated drug and the body form an intricate network of interactions that cannot be modeled with absolute certainty. Despite this, rapid advancements in the area promise to deliver increasingly powerful tools capable of accelerating the development of advanced nanoscale therapeutics. Here, we describe computational approaches that have been utilized in the field of nanomedicine, focusing on approaches for NP design and engineering.
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Affiliation(s)
- Migara Kavishka Jayasinghe
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chang Yu Lee
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Trinh T T Tran
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Vingroup Science and Technology Scholarship Program, Vin University, Hanoi, Vietnam
| | - Rachel Tan
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Sarah Min Chew
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Brendon Zhi Jie Yeo
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Wen Xiu Loh
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Marco Pirisinu
- Jotbody (HK) Pte Limited, Hong Kong, Hong Kong SAR, China
| | - Minh T N Le
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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11
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Mathias S, Coulier A, Hellander A. CBMOS: a GPU-enabled Python framework for the numerical study of center-based models. BMC Bioinformatics 2022; 23:55. [PMID: 35100968 PMCID: PMC8805507 DOI: 10.1186/s12859-022-04575-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 01/11/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Cell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. Without concrete studies to differentiate between solid model conclusions and numerical artifacts, modelers are at risk of being misled by their experiments' results. Most cell-based modeling frameworks offer a feature-rich environment, providing a wide range of biological components, but are less suitable for numerical studies. There is thus a need for software specifically targeted at this use case. RESULTS We present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS' focus is on facilitating numerical study of center-based models by providing access to multiple ordinary differential equation solvers and force functions through a flexible, user-friendly interface and by enabling rapid testing through graphics processing unit (GPU) acceleration. We show-case its potential by illustrating two common workflows: (1) comparison of the numerical properties of two solvers within a Jupyter notebook and (2) measuring average wall times of both solvers on a high performance computing cluster. More specifically, we confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases. CONCLUSIONS CBMOS is a flexible, easy-to-use Python implementation of the center-based model, exposing both basic model assumptions and numerical components to the user. It is available on GitHub and PyPI under an MIT license. CBMOS allows for fast prototyping on a central processing unit for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, it will substantially lower the time investment for any modeler to check the crucial assumption that model conclusions are independent of numerical issues.
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Affiliation(s)
- Sonja Mathias
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Adrien Coulier
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Andreas Hellander
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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12
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Bogdanowski A, Banitz T, Muhsal LK, Kost C, Frank K. McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems. PLoS Comput Biol 2022; 18:e1009777. [PMID: 35073313 PMCID: PMC8830788 DOI: 10.1371/journal.pcbi.1009777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/10/2022] [Accepted: 12/20/2021] [Indexed: 01/30/2023] Open
Abstract
Individual-based modeling is widely applied to investigate the ecological mechanisms driving microbial community dynamics. In such models, the population or community dynamics emerge from the behavior and interplay of individual entities, which are simulated according to a predefined set of rules. If the rules that govern the behavior of individuals are based on generic and mechanistically sound principles, the models are referred to as next-generation individual-based models. These models perform particularly well in recapitulating actual ecological dynamics. However, implementation of such models is time-consuming and requires proficiency in programming or in using specific software, which likely hinders a broader application of this powerful method. Here we present McComedy, a modeling tool designed to facilitate the development of next-generation individual-based models of microbial consumer-resource systems. This tool allows flexibly combining pre-implemented building blocks that represent physical and biological processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems is demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. With this article, we provide a versatile tool for developing next-generation individual-based models that can foster understanding of microbial ecology in both research and education. Microorganisms such as bacteria and fungi can be found in virtually any natural environment. To better understand the ecology of these microorganisms–which is important for several research fields including medicine, biotechnology, and conservation biology–researchers often use computer models to simulate and predict the behavior of microbial communities. Commonly, a particular technique called individual-based modeling is used to generate structurally realistic models of these communities by explicitly simulating each individual microorganism. Here we developed a tool called McComedy that helps researchers applying individual-based modeling efficiently without having to program low-level processes, thus allowing them to focus on their actual research questions. To test whether McComedy is not only convenient to use but also generates meaningful models, we used it to reproduce previously reported findings of two other research groups. Given that our results could well recapitulate and furthermore extend the original findings, we are confident that McComedy is a powerful and versatile tool that can help to address fundamental questions in microbial ecology.
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Affiliation(s)
- André Bogdanowski
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
| | - Thomas Banitz
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
| | - Linea Katharina Muhsal
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
| | - Christian Kost
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
| | - Karin Frank
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
- Osnabrück University, Institute for Environmental Systems Research, Osnabrück, Germany
- iDiv – German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, Germany
- * E-mail:
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13
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Fletcher AG, Osborne JM. Seven challenges in the multiscale modeling of multicellular tissues. WIREs Mech Dis 2022; 14:e1527. [PMID: 35023326 DOI: 10.1002/wsbm.1527] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/23/2020] [Accepted: 03/25/2021] [Indexed: 11/11/2022]
Abstract
The growth and dynamics of multicellular tissues involve tightly regulated and coordinated morphogenetic cell behaviors, such as shape changes, movement, and division, which are governed by subcellular machinery and involve coupling through short- and long-range signals. A key challenge in the fields of developmental biology, tissue engineering and regenerative medicine is to understand how relationships between scales produce emergent tissue-scale behaviors. Recent advances in molecular biology, live-imaging and ex vivo techniques have revolutionized our ability to study these processes experimentally. To fully leverage these techniques and obtain a more comprehensive understanding of the causal relationships underlying tissue dynamics, computational modeling approaches are increasingly spanning multiple spatial and temporal scales, and are coupling cell shape, growth, mechanics, and signaling. Yet such models remain challenging: modeling at each scale requires different areas of technical skills, while integration across scales necessitates the solution to novel mathematical and computational problems. This review aims to summarize recent progress in multiscale modeling of multicellular tissues and to highlight ongoing challenges associated with the construction, implementation, interrogation, and validation of such models. This article is categorized under: Reproductive System Diseases > Computational Models Metabolic Diseases > Computational Models Cancer > Computational Models.
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Affiliation(s)
- Alexander G Fletcher
- School of Mathematics and Statistics, University of Sheffield, Sheffield, UK.,Bateson Centre, University of Sheffield, Sheffield, UK
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
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14
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Breitwieser L, Hesam A, de Montigny J, Vavourakis V, Iosif A, Jennings J, Kaiser M, Manca M, Di Meglio A, Al-Ars Z, Rademakers F, Mutlu O, Bauer R. BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics 2022; 38:453-460. [PMID: 34529036 PMCID: PMC8723141 DOI: 10.1093/bioinformatics/btab649] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 09/02/2021] [Accepted: 09/13/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. RESULTS We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. AVAILABILITY AND IMPLEMENTATION BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information. SUPPLEMENTARY INFORMATION Available at https://doi.org/10.5281/zenodo.5121618.
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Affiliation(s)
- Lukas Breitwieser
- CERN openlab, IT Department, CERN, Geneva 1211, Switzerland.,Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland
| | - Ahmad Hesam
- CERN openlab, IT Department, CERN, Geneva 1211, Switzerland.,Department of Quantum & Computer Engineering, Delft University of Technology, Delft 2628CD, The Netherlands
| | | | - Vasileios Vavourakis
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia 2109, Cyprus.,Department of Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Alexandros Iosif
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia 2109, Cyprus
| | - Jack Jennings
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.,Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.,Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK
| | - Marco Manca
- SCimPulse Foundation, Geleen 6162 BC, The Netherlands
| | | | - Zaid Al-Ars
- Department of Quantum & Computer Engineering, Delft University of Technology, Delft 2628CD, The Netherlands
| | | | - Onur Mutlu
- Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland.,Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich 8092, Switzerland
| | - Roman Bauer
- Department of Computer Science, University of Surrey, Guildford GU2 7XH, UK
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15
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Lai X, Taskén HA, Mo T, Funke SW, Frigessi A, Rognes ME, Köhn-Luque A. A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3542. [PMID: 34716985 DOI: 10.1002/cnm.3542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/24/2021] [Indexed: 06/13/2023]
Abstract
Mathematical modeling and simulation is a promising approach to personalized cancer medicine. Yet, the complexity, heterogeneity and multi-scale nature of cancer pose significant computational challenges. Coupling discrete cell-based models with continuous models using hybrid cellular automata (CA) is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales. However, when clinically relevant cancer portions are taken into account, such models become computationally very expensive. While efficient parallelization techniques for continuous models exist, their coupling with discrete models, particularly CA, necessitates more elaborate solutions. Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communication between processes that share CA neighborhood values while also allowing for reproducibility during stochastic updates. We demonstrated the potential of our solution on a complex hybrid cellular automaton model of breast cancer treated with combination chemotherapy. On a single-core processor, we obtained nearly linear scaling with an increasing problem size, whereas weak parallel scaling showed moderate growth in solving time relative to increase in problem size. Finally, we applied the algorithm to a problem that is 500 times larger than previous work, allowing us to run personalized therapy simulations based on heterogeneous cell density and tumor perfusion conditions estimated from magnetic resonance imaging data on an unprecedented scale.
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Affiliation(s)
- Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Håkon A Taskén
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Torgeir Mo
- Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
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16
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Griesemer M, Sindi SS. Rules of Engagement: A Guide to Developing Agent-Based Models. Methods Mol Biol 2022; 2349:367-380. [PMID: 34719003 DOI: 10.1007/978-1-0716-1585-0_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Agent-based models (ABM), also called individual-based models, first appeared several decades ago with the promise of nearly real-time simulations of active, autonomous individuals such as animals or objects. The goal of ABMs is to represent a population of individuals (agents) interacting with one another and their environment. Because of their flexible framework, ABMs have been widely applied to study systems in engineering, economics, ecology, and biology. This chapter is intended to guide the users in the development of an agent-based model by discussing conceptual issues, implementation, and pitfalls of ABMs from first principles. As a case study, we consider an ABM of the multi-scale dynamics of cellular interactions in a microbial community. We develop a lattice-free agent-based model of individual cells whose actions of growth, movement, and division are influenced by both their individual processes (cell cycle) and their contact with other cells (adhesion and contact inhibition).
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Affiliation(s)
- Marc Griesemer
- Controls and Data Systems Division, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced, Merced, CA, USA.
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17
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Lötstedt P. Derivation of continuum models from discrete models of mechanical forces in cell populations. J Math Biol 2021; 83:75. [PMID: 34878601 PMCID: PMC8654724 DOI: 10.1007/s00285-021-01697-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/23/2021] [Accepted: 11/16/2021] [Indexed: 11/14/2022]
Abstract
In certain discrete models of populations of biological cells, the mechanical forces between the cells are center based or vertex based on the microscopic level where each cell is individually represented. The cells are circular or spherical in a center based model and polygonal or polyhedral in a vertex based model. On a higher, macroscopic level, the time evolution of the density of the cells is described by partial differential equations (PDEs). We derive relations between the modelling on the micro and macro levels in one, two, and three dimensions by regarding the micro model as a discretization of a PDE for conservation of mass on the macro level. The forces in the micro model correspond on the macro level to a gradient of the pressure scaled by quantities depending on the cell geometry. The two levels of modelling are compared in numerical experiments in one and two dimensions.
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Affiliation(s)
- Per Lötstedt
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden.
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18
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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 : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.04.02.019075. [PMID: 32511322 PMCID: PMC7239052 DOI: 10.1101/2020.04.02.019075] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [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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19
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Mathias S, Coulier A, Bouchnita A, Hellander A. Impact of Force Function Formulations on the Numerical Simulation of Centre-Based Models. Bull Math Biol 2020; 82:132. [PMID: 33025278 PMCID: PMC7538447 DOI: 10.1007/s11538-020-00810-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 09/21/2020] [Indexed: 12/17/2022]
Abstract
Centre-based or cell-centre models are a framework for the computational study of multicellular systems with widespread use in cancer modelling and computational developmental biology. At the core of these models are the numerical method used to update cell positions and the force functions that encode the pairwise mechanical interactions of cells. For the latter, there are multiple choices that could potentially affect both the biological behaviour captured, and the robustness and efficiency of simulation. For example, available open-source software implementations of centre-based models rely on different force functions for their default behaviour and it is not straightforward for a modeller to know if these are interchangeable. Our study addresses this problem and contributes to the understanding of the potential and limitations of three popular force functions from a numerical perspective. We show empirically that choosing the force parameters such that the relaxation time for two cells after cell division is consistent between different force functions results in good agreement of the population radius of a two-dimensional monolayer relaxing mechanically after intense cell proliferation. Furthermore, we report that numerical stability is not sufficient to prevent unphysical cell trajectories following cell division, and consequently, that too large time steps can cause geometrical differences at the population level.
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Affiliation(s)
- Sonja Mathias
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Adrien Coulier
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Anass Bouchnita
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- Present Address: Ecole Centrale Casablanca, Bouskoura, Morocco
| | - Andreas Hellander
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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20
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Agmon E, Spangler RK. A Multi-Scale Approach to Modeling E. coli Chemotaxis. ENTROPY 2020; 22:e22101101. [PMID: 33286869 PMCID: PMC7597207 DOI: 10.3390/e22101101] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/25/2020] [Accepted: 09/25/2020] [Indexed: 12/25/2022]
Abstract
The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium-an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium's approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.
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21
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Bull JA, Mech F, Quaiser T, Waters SL, Byrne HM. Mathematical modelling reveals cellular dynamics within tumour spheroids. PLoS Comput Biol 2020; 16:e1007961. [PMID: 32810174 PMCID: PMC7455028 DOI: 10.1371/journal.pcbi.1007961] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 08/28/2020] [Accepted: 05/18/2020] [Indexed: 12/22/2022] Open
Abstract
Tumour spheroids are widely used as an in vitro assay for characterising the dynamics and response to treatment of different cancer cell lines. Their popularity is largely due to the reproducible manner in which spheroids grow: the diffusion of nutrients and oxygen from the surrounding culture medium, and their consumption by tumour cells, causes proliferation to be localised at the spheroid boundary. As the spheroid grows, cells at the spheroid centre may become hypoxic and die, forming a necrotic core. The pressure created by the localisation of tumour cell proliferation and death generates an cellular flow of tumour cells from the spheroid rim towards its core. Experiments by Dorie et al. showed that this flow causes inert microspheres to infiltrate into tumour spheroids via advection from the spheroid surface, by adding microbeads to the surface of tumour spheroids and observing the distribution over time. We use an off-lattice hybrid agent-based model to re-assess these experiments and establish the extent to which the spatio-temporal data generated by microspheres can be used to infer kinetic parameters associated with the tumour spheroids that they infiltrate. Variation in these parameters, such as the rate of tumour cell proliferation or sensitivity to hypoxia, can produce spheroids with similar bulk growth dynamics but differing internal compositions (the proportion of the tumour which is proliferating, hypoxic/quiescent and necrotic/nutrient-deficient). We use this model to show that the types of experiment conducted by Dorie et al. could be used to infer spheroid composition and parameters associated with tumour cell lines such as their sensitivity to hypoxia or average rate of proliferation, and note that these observations cannot be conducted within previous continuum models of microbead infiltration into tumour spheroids as they rely on resolving the trajectories of individual microbeads. Tumour spheroids are an experimental assay used to characterise the dynamics and response to treatment of different cancer cell lines. Previous experiments have demonstrated that the localisation of tumour cell proliferation to the spheroid edge (due to the gradient formed by nutrient diffusing from the surrounding medium) causes cells to be pushed from the proliferative rim towards the nutrient-deficient necrotic core. This movement allows inert particles to infiltrate tumour spheroids. We use a hybrid agent-based model to reproduce this data. We show further how data from individual microbead trajectories can be used to infer the composition of simulated tumour spheroids, and to estimate model parameters pertaining to tumour cell proliferation rates and their responses to hypoxia. Since these measurements are possible using modern imaging techniques, this could motivate new experiments in which spheroid composition could be inferred by observing passive infiltration of inert particles.
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Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Franziska Mech
- Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Centre Munich, Germany
| | - Tom Quaiser
- Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Centre Munich, Germany
| | - Sarah L. Waters
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
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22
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Aguilar B, Gibbs DL, Reiss DJ, McConnell M, Danziger SA, Dervan A, Trotter M, Bassett D, Hershberg R, Ratushny AV, Shmulevich I. A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma. Gigascience 2020; 9:giaa075. [PMID: 32696951 PMCID: PMC7374045 DOI: 10.1093/gigascience/giaa075] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/14/2020] [Accepted: 06/21/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
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Affiliation(s)
- Boris Aguilar
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David L Gibbs
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David J Reiss
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Mark McConnell
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Samuel A Danziger
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Andrew Dervan
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Matthew Trotter
- BMS Center for Innovation and Translational Research Europe (CITRE), Pabellon de Italia, Calle Isaac Newton 4, Sevilla 41092, Spain
| | - Douglas Bassett
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Robert Hershberg
- Formerly Celgene Corporation, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Alexander V Ratushny
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Ilya Shmulevich
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
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23
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Tomeu AJ, Salguero AG. A Lock Free Approach To Parallelize The Cellular Potts Model: Application To Ductal Carcinoma In Situ. J Integr Bioinform 2020; 17:jib-2019-0070. [PMID: 32267247 PMCID: PMC7734501 DOI: 10.1515/jib-2019-0070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 12/19/2019] [Indexed: 01/08/2023] Open
Abstract
In the field of computational biology, in order to simulate multiscale biological systems, the Cellular Potts Model (CPM) has been used, which determines the actions that simulated cells can perform by determining a hamiltonian of energy that takes into account the influence that neighboring cells exert, under a wide range of parameters. There are some proposals in the literature that parallelize the CPM; in all cases, either lock-based techniques or other techniques that require large amounts of information to be disseminated among parallel tasks are used to preserve data coherence. In both cases, computational performance is limited. This work proposes an alternative approach for the parallelization of the model that uses transactional memory to maintain the coherence of the information. A Java implementation has been applied to the simulation of the ductal adenocarcinoma of breast in situ (DCIS). Times and speedups of the simulated execution of the model on the cluster of our university are analyzed. The results show a good speedup.
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Affiliation(s)
- Antonio J Tomeu
- University of Cadiz, Computer Science, Escuela Superior de Ingeniería, Campus of Puerto RealPuerto Real, Spain.,University of Cadiz, Faculty of Engineering, Department of Computer Science, Puerto Real, Spain
| | - Alberto G Salguero
- University of Cadiz, Faculty of Engineering, Department of Computer Science, Puerto Real, Spain
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24
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Cooper FR, Baker RE, Bernabeu MO, Bordas R, Bowler L, Bueno-Orovio A, Byrne HM, Carapella V, Cardone-Noott L, Jonatha C, Dutta S, Evans BD, Fletcher AG, Grogan JA, Guo W, Harvey DG, Hendrix M, Kay D, Kursawe J, Maini PK, McMillan B, Mirams GR, Osborne JM, Pathmanathan P, Pitt-Francis JM, Robinson M, Rodriguez B, Spiteri RJ, Gavaghan DJ. Chaste: Cancer, Heart and Soft Tissue Environment. JOURNAL OF OPEN SOURCE SOFTWARE 2020; 5:1848. [PMID: 37192932 PMCID: PMC7614534 DOI: 10.21105/joss.01848] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Chaste (Cancer, Heart And Soft Tissue Environment) is an open source simulation package for the numerical solution of mathematical models arising in physiology and biology. To date, Chaste development has been driven primarily by applications that include continuum modelling of cardiac electrophysiology ('Cardiac Chaste'), discrete cell-based modelling of soft tissues ('Cell-based Chaste'), and modelling of ventilation in lungs ('Lung Chaste'). Cardiac Chaste addresses the need for a high-performance, generic, and verified simulation framework for cardiac electrophysiology that is freely available to the scientific community. Cardiac chaste provides a software package capable of realistic heart simulations that is efficient, rigorously tested, and runs on HPC platforms. Cell-based Chaste addresses the need for efficient and verified implementations of cell-based modelling frameworks, providing a set of extensible tools for simulating biological tissues. Computational modelling, along with live imaging techniques, plays an important role in understanding the processes of tissue growth and repair. A wide range of cell-based modelling frameworks have been developed that have each been successfully applied in a range of biological applications. Cell-based Chaste includes implementations of the cellular automaton model, the cellular Potts model, cell-centre models with cell representations as overlapping spheres or Voronoi tessellations, and the vertex model. Lung Chaste addresses the need for a novel, generic and efficient lung modelling software package that is both tested and verified. It aims to couple biophysically-detailed models of airway mechanics with organ-scale ventilation models in a package that is freely available to the scientific community. Chaste is designed to be modular and extensible, providing libraries for common scientific computing infrastructure such as linear algebra operations, finite element meshes, and ordinary and partial differential equation solvers. This infrastructure is used by libraries for specific applications, such as continuum mechanics, cardiac models, and cell-based models. The software engineering techniques used to develop Chaste are intended to ensure code quality, re-usability and reliability. Primary applications of the software include cardiac and respiratory physiology, cancer and developmental biology.
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Affiliation(s)
- Fergus R Cooper
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Ruth E Baker
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Miguel O Bernabeu
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Rafel Bordas
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Louise Bowler
- Department of Computer Science, University of Oxford, Oxford, UK
| | | | - Helen M Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Valentina Carapella
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | | | - Cooper Jonatha
- Research IT Services, University College London, London, UK
| | - Sara Dutta
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Benjamin D Evans
- Centre for Biomedical Modelling and Analysis, Living Systems Institute, University of Exeter, Exeter, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Alexander G Fletcher
- School of Mathematics & Statistics, University of Sheffield, Sheffield, UK
- Bateson Centre, University of Sheffield, Sheffield, UK
| | - James A Grogan
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Wenxian Guo
- Department of Computer Science, University of Saskatchewan, Canada
| | - Daniel G Harvey
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Maurice Hendrix
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
- Digital Research Service, University of Nottingham, Nottingham, UK
| | - David Kay
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Jochen Kursawe
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Beth McMillan
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Victoria, Australia
| | - Pras Pathmanathan
- Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration (FDA), Silver Spring, MD 20993, USA
| | | | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, UK
| | | | - David J Gavaghan
- Department of Computer Science, University of Oxford, Oxford, UK
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25
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Douillet A, Ballet P. A GPU Algorithm for Agent-Based Models to Simulate the Integration of Cell Membrane Signals. Acta Biotheor 2020; 68:61-71. [PMID: 31468242 DOI: 10.1007/s10441-019-09360-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 08/23/2019] [Indexed: 11/29/2022]
Abstract
Simulation of complex biological systems with agent-based models is becoming more relevant with the increase in Graphics Processing Unit (GPU) power. In those simulations, up to millions of virtual cells are individually computed, involving daunting processing times. An important part of computational models is the algorithm that manages how agents perceive their surroundings. This can be particularly problematic in three-dimensional environments where agents have deformable virtual membranes. This article presents a GPU algorithm that gives the possibility for agents to integrate the signals scattered on their virtual membrane. It is detailed to be coded in languages like OpenCL or Cuda. Its performances are tested to show its speed with current GPU devices. Finally, it was implemented inside an existing software to test and illustrate the possibilities it offers.
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Affiliation(s)
- Arthur Douillet
- Laboratory of Medical Data Processing, Inserm UMR 1101, University of Brest, 20 avenue Le Gorgeu, 29238, Brest, France.
| | - Pascal Ballet
- Laboratory of Medical Data Processing, Inserm UMR 1101, University of Brest, 20 avenue Le Gorgeu, 29238, Brest, France
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26
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Bravo RR, Baratchart E, West J, Schenck RO, Miller AK, Gallaher J, Gatenbee CD, Basanta D, Robertson-Tessi M, Anderson ARA. Hybrid Automata Library: A flexible platform for hybrid modeling with real-time visualization. PLoS Comput Biol 2020; 16:e1007635. [PMID: 32155140 PMCID: PMC7105119 DOI: 10.1371/journal.pcbi.1007635] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/30/2020] [Accepted: 01/06/2020] [Indexed: 12/12/2022] Open
Abstract
The Hybrid Automata Library (HAL) is a Java Library developed for use in mathematical oncology modeling. It is made of simple, efficient, generic components that can be used to model complex spatial systems. HAL's components can broadly be classified into: on- and off-lattice agent containers, finite difference diffusion fields, a GUI building system, and additional tools and utilities for computation and data collection. These components are designed to operate independently and are standardized to make them easy to interface with one another. As a demonstration of how modeling can be simplified using our approach, we have included a complete example of a hybrid model (a spatial model with interacting agent-based and PDE components). HAL is a useful asset for researchers who wish to build performant 1D, 2D and 3D hybrid models in Java, while not starting entirely from scratch. It is available on GitHub at https://github.com/MathOnco/HAL under the MIT License. HAL requires the Java JDK version 1.8 or later to compile and run the source code.
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Affiliation(s)
- Rafael R. Bravo
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Etienne Baratchart
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Jeffrey West
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Ryan O. Schenck
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Anna K. Miller
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Jill Gallaher
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Chandler D. Gatenbee
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - David Basanta
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Alexander R. A. Anderson
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
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27
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T Magalhães B, Lourenço A, Azevedo NF. Computational resources and strategies to assess single-molecule dynamics of the translation process in S. cerevisiae. Brief Bioinform 2019; 22:219-231. [PMID: 31879749 DOI: 10.1093/bib/bbz149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 10/16/2019] [Accepted: 10/30/2019] [Indexed: 11/13/2022] Open
Abstract
This work provides a systematic and comprehensive overview of available resources for the molecular-scale modelling of the translation process through agent-based modelling. The case study is the translation in Saccharomyces cerevisiae, one of the most studied yeasts. The data curation workflow encompassed structural information about the yeast (i.e. the simulation environment), and the proteins, ribonucleic acids and other types of molecules involved in the process (i.e. the agents). Moreover, it covers the main process events, such as diffusion (i.e. motion of molecules in the environment) and collision efficiency (i.e. interaction between molecules). Data previously determined by wet-lab techniques were preferred, resorting to computational predictions/extrapolations only when strictly necessary. The computational modelling of the translation processes is of added industrial interest, since it may bring forward knowledge on how to control such phenomena and enhance the production of proteins of interest in a faster and more efficient manner.
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Affiliation(s)
| | - Anália Lourenço
- Department of Computer Science, University of Vigo, Spain, Centre of Biological Engineering, University of Minho, Portugal
| | - Nuno F Azevedo
- Department of Chemical Engineering, Faculty of Engineering of the University of Porto, Portugal
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28
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Intosalmi J, Scott AC, Hays M, Flann N, Yli-Harja O, Lähdesmäki H, Dudley AM, Skupin A. Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies. BMC Mol Cell Biol 2019; 20:59. [PMID: 31856706 PMCID: PMC6923950 DOI: 10.1186/s12860-019-0234-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 10/24/2019] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited. RESULTS Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. CONCLUSIONS We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.
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Affiliation(s)
- Jukka Intosalmi
- Department of Computer Science, Aalto University, P.O.Box 15400, Aalto, FI-00076, Finland.
| | - Adrian C Scott
- Pacific Northwest Research Institute, 720 Broadway, Seattle, WA, 98122, USA
| | - Michelle Hays
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, 98195, USA
| | - Nicholas Flann
- Department of Computer Science, Utah State University, 4205 Old Main Hill, Logan, UT, 84322, USA
| | - Olli Yli-Harja
- BioMediTech and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, P.O.Box 553, Tampere, 33101, Finland
- Institute for Systems Biology, 1441N 34th Street, Seattle, WA, 98103-8904, USA
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, P.O.Box 15400, Aalto, FI-00076, Finland
| | - Aimée M Dudley
- Pacific Northwest Research Institute, 720 Broadway, Seattle, WA, 98122, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, 98195, USA
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 2, avenue de l'Université, Esch-sur-Alzette, L-4365, Luxembourg.
- University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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29
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Abstract
Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found. Synthetic biology uses cells as its computing substrate, often based on the genetic circuit concept. In this Perspective, the authors argue that existing synthetic biology approaches based on classical models of computation limit the potential of biocomputing, and propose that living organisms have under-exploited capabilities.
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30
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Douillet A, Douillet C, Garcia M, Nicodem M, Guillemot F, Ballet P. A computational framework to simulate bio-printed cells and extracellular matrix mechanobiochemical interactions. Comput Methods Biomech Biomed Engin 2019. [DOI: 10.1080/10255842.2020.1714935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- A. Douillet
- Laboratory of Medical Data Processing, Inserm UMR 1101, Univ. de Bretagne Occidentale, Brest, France
- Poietis, Pessac, France
| | - C. Douillet
- Laboratory of Medical Data Processing, Inserm UMR 1101, Univ. de Bretagne Occidentale, Brest, France
- BioTis, Inserm UMR 1026, Univ. de Bordeaux, Bordeaux, France
| | | | | | | | - P. Ballet
- Laboratory of Medical Data Processing, Inserm UMR 1101, Univ. de Bretagne Occidentale, Brest, France
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31
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ya||a: GPU-Powered Spheroid Models for Mesenchyme and Epithelium. Cell Syst 2019; 8:261-266.e3. [DOI: 10.1016/j.cels.2019.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 08/03/2018] [Accepted: 02/19/2019] [Indexed: 11/20/2022]
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32
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Saad C, Bauer B, Mansmann UR, Li J. AutoAnalyze in Systems Biology. Bioinform Biol Insights 2019; 13:1177932218818458. [PMID: 30670917 PMCID: PMC6328952 DOI: 10.1177/1177932218818458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 11/14/2018] [Indexed: 11/16/2022] Open
Abstract
AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.
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Affiliation(s)
- Christian Saad
- Department of Computer Science, University of Augsburg, Augsburg, Germany
| | - Bernhard Bauer
- Department of Computer Science, University of Augsburg, Augsburg, Germany
| | - Ulrich R Mansmann
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Jian Li
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
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33
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Soheilypour M, Mofrad MRK. Agent-Based Modeling in Molecular Systems Biology. Bioessays 2018; 40:e1800020. [PMID: 29882969 DOI: 10.1002/bies.201800020] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Indexed: 12/13/2022]
Abstract
Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.
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Affiliation(s)
- Mohammad Soheilypour
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Mohammad R K Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA
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34
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Hao X, Hao J, Wang L, Hou H. Effective norm emergence in cell systems under limited communication. BMC Bioinformatics 2018; 19:119. [PMID: 29671391 PMCID: PMC5907317 DOI: 10.1186/s12859-018-2097-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background The cooperation of cells in biological systems is similar to that of agents in cooperative multi-agent systems. Research findings in multi-agent systems literature can provide valuable inspirations to biological research. The well-coordinated states in cell systems can be viewed as desirable social norms in cooperative multi-agent systems. One important research question is how a norm can rapidly emerge with limited communication resources. Results In this work, we propose a learning approach which can trade off the agents’ performance of coordinating on a consistent norm and the communication cost involved. During the learning process, the agents can dynamically adjust their coordination set according to their own observations and pick out the most crucial agents to coordinate with. In this way, our method significantly reduces the coordination dependence among agents. Conclusion The experiment results show that our method can efficiently facilitate the social norm emergence among agents, and also scale well to large-scale populations.
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Affiliation(s)
- Xiaotian Hao
- School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China
| | - Jianye Hao
- School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China
| | - Li Wang
- School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China.
| | - Hanxu Hou
- School of Electrical Engineering and Intelligentization, Dongguan University of Technology, No. 1, university road, songshan lake district, dongguan, 221116, China.
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35
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Aguilar B, Ghaffarizadeh A, Johnson CD, Podgorski GJ, Shmulevich I, Flann NS. Cell death as a trigger for morphogenesis. PLoS One 2018; 13:e0191089. [PMID: 29565975 PMCID: PMC5863959 DOI: 10.1371/journal.pone.0191089] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 11/29/2017] [Indexed: 11/19/2022] Open
Abstract
The complex morphologies observed in many biofilms play a critical role in the survival of these microbial communities. Recently, the formation of wrinkles has been the focus of many studies aimed at finding fundamental information on morphogenesis during development. While the underlying genetic mechanisms of wrinkling are not well-understood, recent discoveries have led to the counterintuitive idea that wrinkle formation is triggered by localized cell death. This work examines the hypothesis that the material properties of a biofilm both power and control wrinkle formation within biofilms in response to localized cell death. Using an agent-based model and a high-performance platform (Biocellion), we built a model that qualitatively reproduced wrinkle formation in biofilms due to cell death. Through the use of computational simulations, we determined important relationships between cellular level mechanical interactions and changes in colony morphology. These simulations were also used to identify significant cellular interactions that are required for wrinkle formation. These results are a first step towards more comprehensive models that, in combination with experimental observations, will improve our understanding of the morphological development of bacterial biofilms.
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Affiliation(s)
- Boris Aguilar
- Institute for Systems Biology, Seattle, WA, United States of America
| | | | | | - Gregory J. Podgorski
- Biology Department, Utah State University, Logan, UT, United States of America
- Center for Integrated BioSystems, Utah State University, Logan, UT, United States of America
| | - Ilya Shmulevich
- Institute for Systems Biology, Seattle, WA, United States of America
| | - Nicholas S. Flann
- Institute for Systems Biology, Seattle, WA, United States of America
- Computer Science Department, Utah State University, Logan, UT, United States of America
- Synthetic Biomanufacturing Institute, Logan, UT, United States of America
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36
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Song Y, Yang S, Lei J. ParaCells: A GPU Architecture for Cell-Centered Models in Computational Biology. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 16:994-1006. [PMID: 29994073 DOI: 10.1109/tcbb.2018.2814570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In computational biology, the hierarchy of biological systems requires the development of flexible and powerful computational tools. Graphics processing unit (GPU) architecture has been a suitable device for parallel computing in simulating multi-cellular systems. However, in modeling complex biological systems, scientists often face two tasks, mathematical formulation and skillful programming. In particular, specific programming skills are needed for GPU programming. Therefore, the development of an easy-to-use computational architecture, which utilizes GPU for parallel computing and provides intuitive interfaces for simple implementation, is needed so that general scientists can perform GPU simulations without knowing much about the GPU architecture. Here, we introduce ParaCells, a cell-centered GPU simulation architecture for NVIDIA compute unified device architecture (CUDA). ParaCells was designed as a versatile architecture that connects the user logic (in C++) with NVIDIA CUDA runtime and is specific to the modeling of multi-cellular systems. An advantage of ParaCells is its object-oriented model declaration, which allows it to be widely applied to many biological systems through the combination of basic biological concepts. We test ParaCells with two applications. Both applications are significantly faster when compared with sequential as well as parallel OpenMP and OpenACC implementations. Moreover, the simulation programs based on ParaCells are cleaner and more readable than other versions.
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37
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Ghaffarizadeh A, Heiland R, Friedman SH, Mumenthaler SM, Macklin P. PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput Biol 2018; 14:e1005991. [PMID: 29474446 PMCID: PMC5841829 DOI: 10.1371/journal.pcbi.1005991] [Citation(s) in RCA: 205] [Impact Index Per Article: 34.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 03/07/2018] [Accepted: 01/19/2018] [Indexed: 02/07/2023] Open
Abstract
Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal "virtual laboratory" for such multicellular systems simulates both the biochemical microenvironment (the "stage") and many mechanically and biochemically interacting cells (the "players" upon the stage). PhysiCell-physics-based multicellular simulator-is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility "out of the box." The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a "cellular cargo delivery" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.
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Affiliation(s)
- Ahmadreza Ghaffarizadeh
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Randy Heiland
- Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Samuel H. Friedman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
- Opto-Knowledge Systems, Inc., Torrance, California, United States of America
| | - Shannon M. Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Paul Macklin
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
- Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
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Zhang C, Li X, Li S, Feng Z. Dynamically analyzing cell interactions in biological environments using multiagent social learning framework. J Biomed Semantics 2017; 8:31. [PMID: 29297360 PMCID: PMC5763467 DOI: 10.1186/s13326-017-0142-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. RESULTS In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. CONCLUSION Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.
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Affiliation(s)
- Chengwei Zhang
- School of Computer Science and Technology, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Xiaohong Li
- School of Computer Science and Technology, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Shuxin Li
- School of Computer Science and Technology, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Zhiyong Feng
- School of Computer Computer Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
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Cantone M, Santos G, Wentker P, Lai X, Vera J. Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection. Front Physiol 2017; 8:645. [PMID: 28912729 PMCID: PMC5582318 DOI: 10.3389/fphys.2017.00645] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 08/16/2017] [Indexed: 12/13/2022] Open
Abstract
Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.
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Affiliation(s)
| | | | | | | | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum ErlangenErlangen, Germany
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Gutiérrez M, Gregorio-Godoy P, Pérez del Pulgar G, Muñoz LE, Sáez S, Rodríguez-Patón A. A New Improved and Extended Version of the Multicell Bacterial Simulator gro. ACS Synth Biol 2017; 6:1496-1508. [PMID: 28438021 DOI: 10.1021/acssynbio.7b00003] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.
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Affiliation(s)
- Martín Gutiérrez
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Escuela
de Informática y Telecomunicaciones, Universidad Diego Portales, 8370190 Santiago, Chile
| | - Paula Gregorio-Godoy
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Guillermo Pérez del Pulgar
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Luis E. Muñoz
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Sandra Sáez
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Alfonso Rodríguez-Patón
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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41
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Doloman A, Varghese H, Miller CD, Flann NS. Modeling de novo granulation of anaerobic sludge. BMC SYSTEMS BIOLOGY 2017; 11:69. [PMID: 28716030 PMCID: PMC5514506 DOI: 10.1186/s12918-017-0443-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 06/30/2017] [Indexed: 01/19/2023]
Abstract
BACKGROUND A unique combination of mechanical, physiochemical and biological forces influences granulation during processes of anaerobic digestion. Understanding this process requires a systems biology approach due to the need to consider not just single-cell metabolic processes, but also the multicellular organization and development of the granule. RESULTS In this computational experiment, we address the role that physiochemical and biological processes play in granulation and provide a literature-validated working model of anaerobic granule de novo formation. The agent-based model developed in a cDynoMiCs simulation environment successfully demonstrated a de novo granulation in a glucose fed system, with the average specific methanogenic activity of 1.11 ml C H 4/g biomass and formation of a 0.5 mm mature granule in 33 days. The simulated granules exhibit experimental observations of radial stratification: a central dead core surrounded by methanogens then encased in acidogens. Practical application of the granulation model was assessed on the anaerobic digestion of low-strength wastewater by measuring the changes in methane yield as experimental configuration parameters were systematically searched. CONCLUSIONS In the model, the emergence of multicellular organization of anaerobic granules from randomly mixed population of methanogens and acidogens was observed and validated. The model of anaerobic de novo granulation can be used to predict the morphology of the anaerobic granules in a alternative substrates of interest and to estimate methane potential of the resulting microbial consortia. The study demonstrates a successful integration of a systems biology approach to model multicellular systems with the engineering of an efficient anaerobic digestion system.
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Affiliation(s)
- Anna Doloman
- Department of Biological Engineering, Utah State University, Old Main Hill 4105, Logan, 84322-4105, UT, USA
| | - Honey Varghese
- Department of Computer Science, Utah State University, Old Main Hill 420, Logan, 84322-4205, UT, USA
| | - Charles D Miller
- Department of Biological Engineering, Utah State University, Old Main Hill 4105, Logan, 84322-4105, UT, USA
| | - Nicholas S Flann
- Department of Computer Science, Utah State University, Old Main Hill 420, Logan, 84322-4205, UT, USA.
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Macklin P, Frieboes HB, Sparks JL, Ghaffarizadeh A, Friedman SH, Juarez EF, Jonckheere E, Mumenthaler SM. Progress Towards Computational 3-D Multicellular Systems Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 936:225-246. [PMID: 27739051 DOI: 10.1007/978-3-319-42023-3_12] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Tumors cannot be understood in isolation from their microenvironment. Tumor and stromal cells change phenotype based upon biochemical and biophysical inputs from their surroundings, even as they interact with and remodel the microenvironment. Cancer should be investigated as an adaptive, multicellular system in a dynamical microenvironment. Computational modeling offers the potential to detangle this complex system, but the modeling platform must ideally account for tumor heterogeneity, substrate and signaling factor biotransport, cell and tissue biophysics, tissue and vascular remodeling, microvascular and interstitial flow, and links between all these sub-systems. Such a platform should leverage high-throughput experimental data, while using open data standards for reproducibility. In this chapter, we review advances by our groups in these key areas, particularly in advanced models of tissue mechanics and interstitial flow, open source simulation software, high-throughput phenotypic screening, and multicellular data standards. In the future, we expect a transformation of computational cancer biology from individual groups modeling isolated parts of cancer, to coalitions of groups combining compatible tools to simulate the 3-D multicellular systems biology of cancer tissues.
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Affiliation(s)
- Paul Macklin
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Jessica L Sparks
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Ahmadreza Ghaffarizadeh
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Samuel H Friedman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Edwin F Juarez
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA.,Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Edmond Jonckheere
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
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Davis D, Doloman A, Podgorski GJ, Vargis E, Flann NS. Exploiting Self-organization in Bioengineered Systems: A Computational Approach. Front Bioeng Biotechnol 2017; 5:27. [PMID: 28503548 PMCID: PMC5408088 DOI: 10.3389/fbioe.2017.00027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 04/03/2017] [Indexed: 11/13/2022] Open
Abstract
The productivity of bioengineered cell factories is limited by inefficiencies in nutrient delivery and waste and product removal. Current solution approaches explore changes in the physical configurations of the bioreactors. This work investigates the possibilities of exploiting self-organizing vascular networks to support producer cells within the factory. A computational model simulates de novo vascular development of endothelial-like cells and the resultant network functioning to deliver nutrients and extract product and waste from the cell culture. Microbial factories with vascular networks are evaluated for their scalability, robustness, and productivity compared to the cell factories without a vascular network. Initial studies demonstrate that at least an order of magnitude increase in production is possible, the system can be scaled up, and the self-organization of an efficient vascular network is robust. The work suggests that bioengineered multicellularity may offer efficiency improvements difficult to achieve with physical engineering approaches.
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Affiliation(s)
- Delin Davis
- Computer Science Department, Utah State University, Logan, UT, USA
| | - Anna Doloman
- Department of Biological Engineering, Utah State University, Logan, UT, USA
| | | | - Elizabeth Vargis
- Department of Biological Engineering, Utah State University, Logan, UT, USA
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Osborne JM, Fletcher AG, Pitt-Francis JM, Maini PK, Gavaghan DJ. Comparing individual-based approaches to modelling the self-organization of multicellular tissues. PLoS Comput Biol 2017; 13:e1005387. [PMID: 28192427 PMCID: PMC5330541 DOI: 10.1371/journal.pcbi.1005387] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Revised: 02/28/2017] [Accepted: 01/28/2017] [Indexed: 12/28/2022] Open
Abstract
The coordinated behaviour of populations of cells plays a central role in tissue growth and renewal. Cells react to their microenvironment by modulating processes such as movement, growth and proliferation, and signalling. Alongside experimental studies, computational models offer a useful means by which to investigate these processes. To this end a variety of cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. However, it remains unclear how these approaches compare when applied to the same biological problem, and what differences in behaviour are due to different model assumptions and abstractions. Here, we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework, Chaste (http://www.cs.ox.ac.uk/chaste). This framework allows one to easily change constitutive assumptions within these models. In each case we provide full details of all technical aspects of our model implementations. We compare model implementations using four case studies, chosen to reflect the key cellular processes of proliferation, adhesion, and short- and long-range signalling. These case studies demonstrate the applicability of each model and provide a guide for model usage.
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Affiliation(s)
- James M. Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
| | - Alexander G. Fletcher
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
- Bateson Centre, University of Sheffield, Sheffield, United Kingdom
| | - Joe M. Pitt-Francis
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Philip K. Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - David J. Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Gameiro D, Pérez-Pérez M, Pérez-Rodríguez G, Monteiro G, Azevedo NF, Lourenço A. Computational resources and strategies to construct single-molecule metabolic models of microbial cells. Brief Bioinform 2015; 17:863-76. [DOI: 10.1093/bib/bbv096] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Indexed: 11/12/2022] Open
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47
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Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework. BIOMED RESEARCH INTERNATIONAL 2015; 2015:769471. [PMID: 25874228 PMCID: PMC4385633 DOI: 10.1155/2015/769471] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Accepted: 10/30/2014] [Indexed: 11/18/2022]
Abstract
Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.
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48
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Cockrell RC, Christley S, Chang E, An G. Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC). PLoS One 2015; 10:e0122192. [PMID: 25806784 PMCID: PMC4373890 DOI: 10.1371/journal.pone.0122192] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 02/11/2015] [Indexed: 02/06/2023] Open
Abstract
Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.
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Affiliation(s)
| | - Scott Christley
- Department of Surgery, University of Chicago, Chicago, IL, United States of America
| | - Eugene Chang
- Department of Medicine, University of Chicago, Chicago, IL, United States of America
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, IL, United States of America
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