An in silico model to demonstrate the effects of Maspin on cancer cell dynamics.
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.
Department of Population Medicine & Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14850, USA. Electronic address: ma875@cornell.edu.
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Al-Mamun
Department of Computer Science & Engineering, United International University, Bangladesh. Electronic address: dewanfarid@cse.uiu.ac.bd.
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Farid
Department of Applied Sciences, Faculty of Health and Life Sciences, University of Northumbria at Newcastle, UK.
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Ravenhil
Anglia Ruskin IT Research Institute (ARITI), Anglia Ruskin University, Cambridge, UK. Electronic address: alamgir.hossain@anglia.ac.uk.
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Hossain
Computational Intelligence Group, Faculty of Engineering and Environment, University of Northumbria at Newcastle, UK. Electronic address: charles.fall@northumbria.ac.uk.
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Fall
Department of Applied Sciences, Faculty of Health and Life Sciences, University of Northumbria at Newcastle, UK; Computational Intelligence Group, Faculty of Engineering and Environment, University of Northumbria at Newcastle, UK. Electronic address: rosemary.bass@northumbria.ac.uk.
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Bass
D000465:Algorithms; D017209:Apoptosis; D002448:Cell Adhesion; D045744:Cell Line, Tumor; D002465:Cell Movement; D049109:Cell Proliferation; D003198:Computer Simulation; D005109:Extracellular Matrix / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D009362:Neoplasm Metastasis; D009369:Neoplasms / Q000378:metabolism* / Q000473:pathology; D011485:Protein Binding; D060066:Protein Interaction Maps; D015843:Serpins / Q000378:metabolism*; D015398:Signal Transduction; D059016:Tumor Microenvironment
C085344:SERPIN-B5; D015843:Serpins
Cellular automata; Clustering; SERPINB5; Serine protease inhibitor; Tumor growth