Browsing by Author "Dayanik, Savas"

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Analysis of the in vitro nanoparticle-cell interactions via a smoothing-splines mixed-effects model
(TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND, 2016) Dogruoz, Elifnur; Dayanik, Savas; Budak, Gurer; Sabuncuoglu, Ihsan; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü;
A mixed-effects statistical model has been developed to understand the nanoparticle (NP)-cell interactions and predict the rate of cellular uptake of NPs. NP-cell interactions are crucial for targeted drug delivery systems, cell-level diagnosis, and cancer treatment. The cellular uptake of NPs depends on the size, charge, chemical structure, and concentration of NPs, and the incubation time. The vast number of combinations of these variable values disallows a comprehensive experimental study of NP-cell interactions. A mathematical model can, however, generalize the findings from a limited number of carefully designed experiments and can be used for the simulation of NP uptake rates, to design, plan, and compare alternative treatment options. We propose a mathematical model based on the data obtained from in vitro interactions of NP-healthy cells, through experiments conducted at the Nanomedicine and Advanced Technologies Research Center in Turkey. The proposed model predicts the cellular uptake rate of silica, polymethyl methacrylate, and polylactic acid NPs, given the incubation time, size, charge and concentration of NPs. This study implements the mixed-model methodology in the field of nanomedicine for the first time, and is the first mathematical model that predicts the rate of cellular uptake of NPs based on sound statistical principles. Our model provides a cost-effective tool for researchers developing targeted drug delivery systems.
Artificial Neural Network Modeling and Simulation of In-Vitro Nanoparticle-Cell Interactions
(AMER SCIENTIFIC PUBLISHERS, 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA, 2014) Cenk, Neslihan; Budak, Gurer; Dayanik, Savas; Sabuncuoglu, Ihsan; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü;
In this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The 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 a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.