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Towards an evolvable cancer treatment simulator

Preen, Richard J.; Bull, Larry; Adamatzky, Andrew


Dr Richard Preen
Senior Research Fellow in Machine Learning

Lawrence Bull
School Director (Research & Enterprise) and Professor


© 2019 Elsevier B.V. The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies. However, increasing realism comes at the cost of increasing computational requirements. This article explores the use of surrogate-assisted evolutionary algorithms to optimise the targeted delivery of a therapeutic compound to cancerous tumour cells with the multicellular simulator, PhysiCell. The use of both Gaussian process models and multi-layer perceptron neural network surrogate models are investigated. We find that evolutionary algorithms are able to effectively explore the parameter space of biophysical properties within the agent-based simulations, minimising the resulting number of cancerous cells after a period of simulated treatment. Both model-assisted algorithms are found to outperform a standard evolutionary algorithm, demonstrating their ability to perform a more effective search within the very small evaluation budget. This represents the first use of efficient evolutionary algorithms within a high-throughput multicellular computing approach to find therapeutic design optima that maximise tumour regression.


Preen, R. J., Bull, L., & Adamatzky, A. (2019). Towards an evolvable cancer treatment simulator. BioSystems, 182, 1-7.

Journal Article Type Article
Acceptance Date May 10, 2019
Online Publication Date May 14, 2019
Publication Date Aug 1, 2019
Deposit Date May 10, 2019
Publicly Available Date May 15, 2020
Journal BioSystems
Print ISSN 0303-2647
Electronic ISSN 1872-8324
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 182
Pages 1-7
Keywords agent-based model, evolutionary algorithm, cancer, PhysiCell, high-throughput computing, surrogate modelling
Public URL
Publisher URL
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here:


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