Research Spotlight (PFNS/UWE/UOB): Harnessing adaptive novelty for automated generation of cancer treatments

Our team members from the University of Novi Sad, University of the West of England, and University of Bristol have just released a paper on EVO NANO’s simulation capabilities.

For the modelling, they use a Python-based platform called Mesa – which allows cancer cells, cancer stem cells, and nano-agents to be modelled in 2D. The nano-agents are varied and given values based on how well they kill or inhibit the different cancer cell types. This value then allows the model to select the top 5% and remove the bottom 5% performing nano-agents, and mutates the middle 5%. In this way, the nano-agents evolve and improve in fitness far more rapidly than a model that has no evolution.

From the author, Igor Balaz:
We showed that this strategy is especially efficient when dealing with the emergence of tumour resistance. In such scenarios, nano-agents are able to continuously track down newly emerging tumour mutations and keeps them under control.