Artificial neural networks modeling and simulation of the in-vitro nanoparticles - cell interactions

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buir.advisorSabuncuoğlu, İhsan
dc.contributor.authorCenk, Neslihan
dc.date.accessioned2016-01-08T18:19:09Z
dc.date.available2016-01-08T18:19:09Z
dc.date.issued2012
dc.departmentDepartment of Industrial Engineeringen_US
dc.descriptionAnkara : The Department ofIndustrial Engineering, Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical references leaves 54-56.en_US
dc.description.abstractIn this research a prediction model for cellular uptake efficiency of nanoparticles (NPs), which is the rate of NPs adhered to the cell surface or entered into the cell, is investigated via Artificial Neural Network (ANN) method. Prediction of cellular uptake rate of NPs is an important study considering the technical limitations of volatile environment of organism and the time limitation of conducting numerous experiments for thousands of possible variations of different variables that have an impact on NP uptake rate. Moreover, this study constitutes a basis for the targeted drug delivery and cell-level detection, treatment and diagnoses of existing pathologies through simulating experimental procedure of NP-Cell interactions. Accordingly, this study will accelerate nano-medicine researches. The research focuses on constructing a proper ANN model based on multilayered feed-forward back-propagation algorithm for prediction of cellular uptake efficiency which depends on NP type, NP size, NP surface charge, concentration and time. NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA) all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing number of hidden layers, node numbers and training functions. The data sets for training and testing of the network are provided through in-vitro NP-cell interaction experiments conducted by a Nano-Medicine Research Center in Turkey. The dispersion characteristics and cell interactions of the different nanoparticles in organisms are explored through constructing and implementing an optimal prediction model using ANNs. Simulating the possible interactions of targeted nanoparticles with cells via ANN model could lead to a more rapid, more convenient and less expensive approach in comparison to numerous experimental variations.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityCenk, Neslihanen_US
dc.format.extentxi, 56 leaves, illustrationsen_US
dc.identifier.urihttp://hdl.handle.net/11693/15479
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectNano-medicineen_US
dc.subjecttargeted drug deliveryen_US
dc.subjectnanoparticle uptake rateen_US
dc.subjectartificial neural networksen_US
dc.subjectprediction modelen_US
dc.subject.lccQT36.5 .C45 2012en_US
dc.subject.lcshNanomedicine.en_US
dc.subject.lcshNanoparticles.en_US
dc.subject.lcshNeural networks (Computer science)en_US
dc.subject.lcshArtificial intelligence--Medical applications.en_US
dc.subject.lcshDrug delivery systems.en_US
dc.titleArtificial neural networks modeling and simulation of the in-vitro nanoparticles - cell interactionsen_US
dc.typeThesisen_US
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