Introduction

I am Salvador de la Torre Gonzalez, a Mathematics and Computer Engineering student from the University of Seville, and a Google Summer of Code 2025 contributor who will be working on “Agent-Based Simulation of CAR-T Cell Therapy Using BioDynaMo project.

Mentors: Vassil Vassilev, Lukas Breitwieser

Briefly about CAR-T Cell Therapy and BioDynaMo

Chimeric Antigen Receptor T-cell (CAR-T) therapy is a promising immunotherapy that reprograms a patient’s T-cells to recognize and eliminate cancer cells. While CAR-T has achieved remarkable success in blood cancers, its efficacy in solid tumors remains limited due to factors such as poor T-cell infiltration, immune suppression, and T-cell exhaustion.

This project will be built on BioDynaMo, an open-source, high-performance simulation engine for large-scale agent-based biological modeling. BioDynaMo provides an efficient framework for modeling cellular dynamics and complex microenvironments at scale, making it ideally suited for simulating CAR-T therapies under diverse tumor conditions.

The simulation will capture essential components of CAR-T behavior, including T-cell migration, tumor cell engagement, and the influence of microenvironmental factors like hypoxia, regulatory T-cells, and immunosuppressive cytokines. The goal is not only to provide the simulation, but also custom analysis scripts for visualizing and testing how therapy parameters influence treatment outcomes.

Why I Chose This Project

This project represents an exciting opportunity to apply my dual academic background in mathematics and computer engineering to a highly impactful domain: cancer immunotherapy.

My interest in oncology and CAR-T treatments deepened significantly after attending a course on Mathematical Modeling and Data Analysis in Oncology, taught by researchers from the Mathematical Oncology Laboratory” (MôLAB) team at the University of Cadiz. During this course, I was introduced to the fundamentals of immunotherapy and CAR-T cell dynamics, and became fascinated by the potential of mathematical and computational tools to contribute to this area.

I believe that building a scalable, open-source simulation of CAR-T therapy can become a valuable resource for scientists and clinicians worldwide, helping them to better understand and optimize treatment strategies considering the complex landscape of solid tumors.

Implementation Details and Plans

This project will develop a scalable agent-based simulation of CAR-T therapy using BioDynaMo. The simulation will include:

  • T-cell migration, proliferation, and tumor cell killing,
  • Simulation of both solid tumors and hematological cancers,
  • Modeling of tumor microenvironment components such as:
    • Hypoxia,
    • Regulatory T-cells,
    • Immunosuppressive cytokines,
  • Development of custom scripts for:
    • Visualizing tumor progression/regression,
    • Quantifying CAR-T efficacy,
  • Exploration of therapy strategies including:
    • CAR-T dosage and administration timing,
    • Performance benchmarking for different therapeutic scenarios.

A modular architecture will ensure that the simulation is extensible and reusable in future studies. Insights gained from these simulations will be summarized in a comprehensive report including replication of real data and comparison between treatment strategy results.

Conclusion

By building a BioDynaMo-based model of CAR-T cell therapy, we aim to provide a flexible and high-performance tool for exploring treatment strategies in complex tumor environments. This is really valuable work for the community since it could help identify conditions that enhance CAR-T efficacy, contributing to improved design of immunotherapies.