An in silico model to demonstrate the effects of Maspin on cancer cell dynamics. (7th January 2016)

• Record Type: Journal Article
• Authors: Al-Mamun, M.A.; Farid, D.M.; Ravenhil, L.; Hossain, M.A.; Fall, C.; Bass, R.
• Is Part Of: Journal of theoretical biology. Volume 388(2016)
• Page Start: 37
• Page End: 49
• Publication Date: 2016-01-07
• Subjects: SERPINB5 -- Serine protease inhibitor -- Cellular automata -- Clustering -- Tumor growth; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2015.10.007
• Languages: English
• ISSNs: 0022-5193

Abstract

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. Highlights: A computational model is proposed to demonstrate the exogenous effects of maspin. Proposed model shows the effects of maspin in cell–extracellular matrix mechanism. A novel cell–ECM in vitro study has been presented to support the in silico model. A data verification method is presented using clustering algorithms.