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A Computational Approach Using Cellular Potts Model for Simulation of Neural Stem Cells Grown in PCL-Graphene Scaffolds

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dc.contributor.author Haliti, Pegi
dc.date.accessioned 2018-04-27T15:30:32Z
dc.date.available 2018-04-27T15:30:32Z
dc.date.issued 2018-03-23
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2220
dc.description.abstract In the last decade, new computational model environment that includes both discrete and continuous models is getting more attention. One of the most well-known hybrid approaches is Cellular Potts Model also known as the Glazier-Graner-Hogeweg model, a lattice-based, cellular level computational framework that accurately describes biological phenomena including stem cell differentiation, tumor growth, cell migration, angiogenesis, cell rearrangement and adhesion. In this study we use the Cellular Potts Model (CPM) to design a dynamic microenvironment for cellular organization and its functioning at a Nano-scale level. Particularly we focus on cellular level interactions of neural stem cells grown on a Poly(ε-caprolactone) (PCL)-graphene scaffolds which are proven to provide better protein and cell adhesion and eventually increase the biological responses of the cells. The interactions such as cell-cell, cell-extracellular matrix adhesion and cellular motility contribute to the system’s energy given by a function known as Hamiltonian which manages the lattice rearrangement using the stochastic Monte Carlo’s model by minimizing the energy. en_US
dc.language.iso en_US en_US
dc.subject Cellular Potts model en_US
dc.subject Polycaprolatone (PCL)-graphene nanostructured scaffolds en_US
dc.subject Stem cells en_US
dc.title A Computational Approach Using Cellular Potts Model for Simulation of Neural Stem Cells Grown in PCL-Graphene Scaffolds en_US
dc.type Other en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US


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