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Updated: May 30, 2026

A 3D Spheroid Model for Glioblastoma

A 3D Spheroid Model for Glioblastoma

Published on: April 9, 2020

Computational challenges of tumor spheroid modeling.

Roberto Chignola¹, Alessio Del Fabbro, Marcello Farina

¹Dipartimento di Biotecnologie, Università di Verona, and INFN - Sezione di Trieste, Strada le Grazie 15-CV1, I-37134 Verona, Italia. roberto.chignola@univr.it

Journal of Bioinformatics and Computational Biology

|July 22, 2011

Summary

We present a computational method to simulate tumor growth, overcoming limitations through an incremental, experimentally validated approach. This aids in understanding early tumor development and improving cancer treatments.

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Area of Science:

Computational biology
Cancer research
Mathematical modeling

Background:

Modern computers enable complex biological system simulations.
Modeling large tumor cell populations and spheroids is computationally intensive.
Fine-grained numerical models require efficient approaches.

Purpose of the Study:

To review a computational approach for modeling tumor cell populations and spheroids.
To discuss methods for overcoming computational limitations in tumor modeling.
To present results on tumor growth and suggest applications for treatment optimization.

Main Methods:

A review of a proposed computational approach for tumor modeling.
Discussion of strategies to bypass computational limitations.

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Published on: July 8, 2014

Generation of 3D Tumor Spheroids for Drug Evaluation Studies

Generation of 3D Tumor Spheroids for Drug Evaluation Studies

Published on: February 24, 2023

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A 3D Spheroid Model for Glioblastoma

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High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

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Generation of 3D Tumor Spheroids for Drug Evaluation Studies

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An incremental modeling approach validated by quantitative experimental observations.

Main Results:

Demonstration of tumor cell growth in closed and open environments.
Presentation of results for tumor spheroid growth.
Validation of computational steps through experimental data.

Conclusions:

The proposed computational approach offers new ways to study early solid tumor growth.
This method can potentially optimize antitumor treatment strategies.
Experimental validation is crucial for the reliability of fine-grained numerical tumor models.