Towards an evolvable cancer treatment simulator

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

Abstract

© 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
• 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
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 182
• Pages: 1-7
• DOI: https://doi.org/10.1016/j.biosystems.2019.05.005
• Keywords: agent-based model, evolutionary algorithm, cancer, PhysiCell, high-throughput computing, surrogate modelling
• Public URL: https://uwe-repository.worktribe.com/output/845856
• Publisher URL: https://doi.org/10.1016/j.biosystems.2019.05.005
• Additional Information: Additional Information : This is the author's accepted manuscript. The final published version is available here: https://doi.org/10.1016/j.biosystems.2019.05.005.
• Contract Date: May 10, 2019