Dr Richard Preen Richard2.Preen@uwe.ac.uk
Senior Research Fellow in Machine Learning
Towards an evolvable cancer treatment simulator
Preen, Richard J.; Bull, Larry; Adamatzky, Andrew
Authors
Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Andrew Adamatzky Andrew.Adamatzky@uwe.ac.uk
Professor
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 |
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