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Related Experiment Video

Updated: Aug 20, 2025

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

Agent-based methods facilitate integrative science in cancer.

Jeffrey West¹, Mark Robertson-Tessi¹, Alexander R A Anderson¹

¹Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Trends in Cell Biology

|November 20, 2022

Summary

Agent-based modeling is crucial for understanding cancer progression and metastasis by simulating cell interactions. This approach complements experiments, guiding new research directions for cancer therapy.

Keywords:

agent-based mathematical models cancer metabolism immune–tumor interactions integrative science tissue homeostasis

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Area of Science:

Computational Biology
Cancer Research
Mathematical Modeling

Background:

Cancer progression, therapeutic resistance, and metastasis involve complex cell-cell and cell-environment interactions.
Understanding these interactions is critical for developing effective cancer treatments.
Traditional experimental methods have limitations in capturing the dynamic, multi-scale nature of these processes.

Purpose of the Study:

To highlight agent-based modeling (ABM) as a powerful computational tool for cancer research.
To emphasize the utility of ABM in exploring the mechanisms driving cancer progression, resistance, and metastasis.
To showcase how ABM can integrate with and inform experimental cancer studies.

Main Methods:

Agent-based modeling (ABM), also known as individual-based modeling.
Simulation of cellular behaviors and interactions at the cell-scale resolution.
Integration of computational modeling with experimental data (in vitro and in vivo).

Main Results:

ABM provides a unique cell-scale resolution to capture complex biological phenomena in cancer.
These models enable the exploration of emergent properties arising from individual cell behaviors and interactions.
ABM facilitates parameterization and data extrapolation, enhancing the interpretation of experimental results.

Conclusions:

Agent-based modeling is an indispensable tool for advancing our understanding of cancer biology.
ABM complements experimental approaches by generating testable predictions and guiding future research.
The application of ABM holds significant promise for improving cancer diagnosis and treatment strategies.