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Towards an evolvable cancer treatment simulator

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


Richard Preen
Research Fellow - Deep Evolutionary Learning

Lawrence Bull
AHOD Research and Scholarship and Prof


© 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.

Journal Article Type Article
Publication Date Aug 1, 2019
Journal BioSystems
Print ISSN 0303-2647
Electronic ISSN 1872-8324
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 182
Pages 1-7
APA6 Citation Preen, R. J., Bull, L., & Adamatzky, A. (2019). Towards an evolvable cancer treatment simulator. BioSystems, 182, 1-7.
Keywords agent-based model, evolutionary algorithm, cancer, PhysiCell, high-throughput computing, surrogate modelling
Publisher URL
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here:


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