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Immersive Visualization of a 3D Model of Tumor-Immune Dynamics

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Interactions between cancer and immune cells in the tumor microenvironment lead to a progression of disease when cancer cells strategically bind to a set of inhibitory receptors on the immune cell surface. These inhibitory interactions render immune cells exhausted, or less able to recognize cancer as foreign, such that a T cell’s ability to recognize and respond to cancer is dependent on the level of exhaustion. We constructed a mathematical model for tumor-immune interactions intended for use in a virtual tumor microenvironment. By perturbing the system and prescribing biologically motivated rules to these interactions, we investigated the potential for exhaustion and our ability to intervene via immune checkpoint blockade. We projected this model system into a Cave Automatic Visualization Environment (CAVE) immersive virtual environment. Through immersion, we observed individual and local cell interactions in addition to the overall behavior of the system of interacting cells. The CAVE provided a new layer of spatial understanding and intuition. This model framework and visualization process is a tool that holds great potential to complement ongoing and future efforts in mathematical oncology.

Original languageEnglish
Title of host publicationMathematical Approaches to Challenges in Biology and Biomedicine - ICMASC 2024
EditorsMartin Golubitsky, Stefano Boccaletti, Carla M.A. Pinto
PublisherSpringer
Pages17-26
Number of pages10
ISBN (Print)9783031979491
DOIs
StatePublished - 2025
EventInternational Conference on Mathematical Analysis and Applications in Science and Engineering, ICMASC 2024 - Porto, Portugal
Duration: Jun 20 2024Jun 22 2024

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume507 PROMS

Conference

ConferenceInternational Conference on Mathematical Analysis and Applications in Science and Engineering, ICMASC 2024
Country/TerritoryPortugal
CityPorto
Period06/20/2406/22/24

Keywords

  • Agent-based model
  • Cancer
  • Immunotherapy
  • Visualization

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