The cell cycle is an essential process in all living organisms that must be carefully regulated to ensure successful cell growth and division. Disregulation of the cell cycle is a key contributing factor towards the formation of cancerous cells. Understanding events at a cellular level is the first step towards comprehending how cancer manifests at an organismal level. Mathematical modelling can be used as a means of formalising and predicting the behaviour of the biological systems involved in cancer. In response, cell cycle models have been constructed to simulate and predict what happens to the mammalian cell over a time course in response to variable parameters.Current cell cycle models rarely account for certain precursors of cell growth such as energy usage and the need for non-essential amino acids as fundamental building blocks of macromolecules. Normal and cancer cell metabolism differ in the way they derive energy from glucose. In addition, normal and cancer cells also demonstrate different levels of gene expression. Two versions of a mammalian cell cycle and metabolism model, based on ordinary differential equations (ODEs) that respond to fluctuations in glucose concentration levels, have been developed here for the normal and cancer cell scenarios. Sensitivity analysis is performed for both normal and cancer cells using these cell cycle and metabolism models to investigate which kinetic reaction steps have a greater effect over the cell cycle period. Detailed analysis of the models and quantitatively assessing metabolite levels at various stages of the cell cycle may offer novel insights into how the glycolytic rate varies during the cell cycle for both normal and cancer cells.The results of the sensitivity analysis are used to identify potential drug targets in cancer therapy. Combinations of these individual targets are also investigated to compare the different effects of single and multiple drug compounds on the time it takes to complete a cell division cycle.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:574342 |
| Date | January 2013 |
| Creators | Yang, Jie |
| Contributors | Westerhoff, Hans; Snoep, Jacob; Pedrosa Mendes, Pedro |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/prediction-of-combination-efficacy-in-cancer-therapy(1b49824b-9d5f-4d21-89d7-6160a810d05e).html |
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