# Towards an Evolvable Cancer Treatment Simulator

## TL;DR

This paper demonstrates that surrogate-assisted evolutionary algorithms can efficiently optimize cancer treatment simulations, reducing computational costs while effectively identifying therapeutic strategies to minimize tumor size.

## Contribution

It introduces the first application of surrogate-assisted evolutionary algorithms in high-throughput multicellular cancer simulation for therapy optimization.

## Key findings

- Surrogate models outperform standard evolutionary algorithms.
- Evolutionary algorithms effectively explore biophysical parameter space.
- Optimized strategies significantly reduce tumor size in simulations.

## Abstract

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.