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Lookup NU author(s): Mohammad Al-Mamun
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Cancer metastasis is a complex multistep process which allows cancer cells to establish new tumours in distant organs. The process of metastasis involves cell migration and invasion; it is what makes cancer a fatal disease. The efficiency of most cancer treatments depends on metastasis suppression. Maspin is a type II tumour metastasis suppressor which has multiple cellular effects. It has been described as a key regulatory protein in both the intracellular and extracellular environments. Maspin has been shown to reduce cell migration, invasion, proliferation and angiogenesis, and increase apoptosis and cell-cell adhesion in in vitro and in vivo experiments. The clinical data regarding the predictive effects of maspin expression are variable. To date, the whole cellular mechanisms that maspin uses to influence tumour cell behaviours have not been clearly defined. The diversity of the effects of maspin motivated us to develop an intelligent model to investigate its effects on cellular proliferation and migration. This paper reports a hybrid model of solid tumour growth in order to investigate the impact of maspin on the growth and evolutionary dynamics of the cancer cell. A feed-forward neural network was used to model the behaviours (proliferation, quiescence, apoptosis and/or movement) of each cell, which has been suggested as a suitable model of cell signalling pathways. Results show that maspin reduces migration by 10-40%, confirmed by published in vitro data. The model also shows a reduction in cell proliferation by 20-30% in the presence of maspin. So far, this is the first attempt to model the effect of maspin in a computational model to verify in vitro data. This will provide new insights into the tumour suppressive properties of maspin and inform the development of novel cancer therapies.
Author(s): Al-Mamun MA, Brown LJ, Hossain MA, Fall C, Wagstaff L, Bass R
Publication type: Article
Publication status: Published
Journal: Journal of Theoretical Biology
Year: 2013
Volume: 337
Pages: 150-160
Print publication date: 21/11/2013
Online publication date: 27/08/2013
Acceptance date: 15/08/2013
ISSN (print): 0022-5193
ISSN (electronic): 1095-8541
Publisher: Academic Press
URL: http://dx.doi.org/10.1016/j.jtbi.2013.08.016
DOI: 10.1016/j.jtbi.2013.08.016
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