blogpost about me and the project

Author: salva24

_posts/2025-05-14-biodynamo-cart-simulation.md

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+---
+title: "Agent-Based Simulation of CAR-T Cell Therapy Using BioDynaMo"
+layout: post
+excerpt: "This GSoC 2025 project, Agent-Based Simulation of CAR-T Cell Therapy, aims to develop a BioDynaMo-based model to simulate CAR-T cell dynamics and interactions. The goal is to provide researchers with a tool to evaluate therapy efficacy and identify strategies to enhance treatment outcomes."
+sitemap: true
+author: Salvador de la Torre Gonzalez
+permalink: blogs/gsoc25_salvador_introduction_blog/
+banner_image: /images/blog/gsoc-banner.png
+date: 2025-05-14
+tags: gsoc BioDynaMo c++
+---
+
+### 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](https://www.researchgate.net/lab/Mathematical-Oncology-Laboratory-MoLAB-Victor-M-Perez-Garcia)) team at the University of Cádiz. 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.
+
+
+### Related Links
+
+- [Project Description](https://hepsoftwarefoundation.org/gsoc/2025/proposal_BioDynamo-CART.html)
+- [BioDynaMo Repository](https://github.com/BioDynaMo/biodynamo)
+- [My GitHub Profile](https://github.com/salva24)