An in silico model to demonstrate the effects of Maspin on cancer cell dynamics

Al-Mamun, Mohammad, Farid, Dewan, Ravenhil, Lorna, Hossain, Alamgir, Fall, Charles and Bass, Rosemary (2016) An in silico model to demonstrate the effects of Maspin on cancer cell dynamics. Journal of Theoretical Biology, 388. pp. 37-49. ISSN 1095-8541

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Maspin on cancer cell dynamics JTB-D-15-00241.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1016/j.jtbi.2015.10.007

Abstract

Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support. This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell–extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research.

Item Type: Article
Additional Information: published online ahead of print
Uncontrolled Keywords: SERPINB5, Serine protease inhibitor, cellular automata, clustering, tumor growth
Subjects: C900 Others in Biological Sciences
Department: Faculties > Health and Life Sciences > School of Life Sciences > Applied Sciences
Depositing User: Nicola King
Date Deposited: 16 Nov 2015 11:31
Last Modified: 17 May 2017 21:12
URI: http://nrl.northumbria.ac.uk/id/eprint/24490

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