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A Review of Cell-Based Computational Modeling in Cancer Biology

Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote gr...

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Autores principales: Metzcar, John, Wang, Yafei, Heiland, Randy, Macklin, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Clinical Oncology 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584763/
https://www.ncbi.nlm.nih.gov/pubmed/30715927
http://dx.doi.org/10.1200/CCI.18.00069
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author Metzcar, John
Wang, Yafei
Heiland, Randy
Macklin, Paul
author_facet Metzcar, John
Wang, Yafei
Heiland, Randy
Macklin, Paul
author_sort Metzcar, John
collection PubMed
description Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.
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spelling pubmed-65847632019-06-20 A Review of Cell-Based Computational Modeling in Cancer Biology Metzcar, John Wang, Yafei Heiland, Randy Macklin, Paul JCO Clin Cancer Inform REVIEW ARTICLE Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control. American Society of Clinical Oncology 2019-02-04 /pmc/articles/PMC6584763/ /pubmed/30715927 http://dx.doi.org/10.1200/CCI.18.00069 Text en © 2019 by American Society of Clinical Oncology https://creativecommons.org/licenses/by/4.0/ Licensed under the Creative Commons Attribution 4.0 License: https://creativecommons.org/licenses/by/4.0/
spellingShingle REVIEW ARTICLE
Metzcar, John
Wang, Yafei
Heiland, Randy
Macklin, Paul
A Review of Cell-Based Computational Modeling in Cancer Biology
title A Review of Cell-Based Computational Modeling in Cancer Biology
title_full A Review of Cell-Based Computational Modeling in Cancer Biology
title_fullStr A Review of Cell-Based Computational Modeling in Cancer Biology
title_full_unstemmed A Review of Cell-Based Computational Modeling in Cancer Biology
title_short A Review of Cell-Based Computational Modeling in Cancer Biology
title_sort review of cell-based computational modeling in cancer biology
topic REVIEW ARTICLE
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584763/
https://www.ncbi.nlm.nih.gov/pubmed/30715927
http://dx.doi.org/10.1200/CCI.18.00069
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