In-silico modelling of phenotypic switching in tumours: Investigating potentials for non-invasive therapies

Dario Panada, Ross King, Bijan Parsia

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

We developed an in-silico model of cancer growth to investigate the extent to which metabolic switching occurs in tumour masses. Cancer therapies based on glycoconjugation, the linking of a drug to glucose or another sugar, allow improved selectivity and targeting, thus reducing harmful side effects. This mechanism exploits the over-expression of glucose membrane transporters, a phenotypic alteration in cancer cells included in an array of metabolic alterations known as the Warburg effect. However, the extent to which tumour masses adopt the Warburg phenotype is unclear, potentially limiting the efficacy of therapies based on glycoconjugation. We simulated multiple 'what-if' scenarios, each modelling increasing proportions of tumour populations that adopted the Warburg phenotype, and compared the results to the expected growth curves derived from laboratory studies. Our results suggest that the Warburg phenotype is prevalent in tumours, with the population of cancer cells adopting this phenotype significantly outnumbering that of cells that do not.

Original languageEnglish
Title of host publication2021 IEEE 9th International Conference on Bioinformatics and Computational Biology, ICBCB 2021
PublisherIEEE
Pages83-90
Number of pages8
ISBN (Electronic)9780738132020
ISBN (Print)9780738132020
DOIs
Publication statusPublished - 25 May 2021
Event9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021 - Taiyuan, China
Duration: 25 May 202127 May 2021

Publication series

Name2021 IEEE 9th International Conference on Bioinformatics and Computational Biology, ICBCB 2021

Conference

Conference9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021
Country/TerritoryChina
CityTaiyuan
Period25/05/2127/05/21

Keywords

  • Cancer
  • Cellular automaton
  • Computer model
  • Glycoconjugation
  • Hybrid multiscale model

Fingerprint

Dive into the research topics of 'In-silico modelling of phenotypic switching in tumours: Investigating potentials for non-invasive therapies'. Together they form a unique fingerprint.

Cite this