Browsing by Author "Budak, G."
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Item Open Access Analysis of the in vitro nanoparticle–cell interactions via a smoothing-splines mixed-effects model(Taylor and Francis, 2016) Dogruoz, E.; Dayanik, S.; Budak, G.; Sabuncuoglu, I.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.Item Open Access Artificial neural network modeling and simulation of in-vitro nanoparticle-cell interactions(American Scientific Publishers, 2014) Cenk, N.; Budak, G.; Dayanik, S.; Sabuncuoglu, I.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.Item Open Access Synthesis and characterization of iron oxide derivatized mutant cowpea mosaic virus hybrid nanoparticles(Wiley - VCH Verlag GmbH & Co. KGaA, 2008) Martinez-Morales, A. A.; Portney, N. G.; Zhang, Y.; Destito, G.; Budak, G.; Özbay, Ekmel; Manchester, M.; Ozkan, C. S.; Ozkan, M.The enhanced local magnetic field strength was qualitatively analyzed by magnetic force microscopy (MFM), demonstrating a characteristic advantage for attaching derivatized magnetic iron oxide (IO) nanoparticles in an organic medium. the synthesis of 11 nm size IO nanoparticles was carried out under nitrogen atmosphere using standard schlenk technique. The biocompatible γ-Fe2O3-COOH nanoparticles were synthesized by thermal decomposition of Fe(CO)5 and surface modified. Atomic force microscopy (AFM) was used to characterize structurally the as-synthesized IO nanoparticles on a silicon substrate. The histogram of the size distribution of the IO nanoparticles determined from 68 individual measurements on single IO nanoparticles exhibited a mean size of δ11 nm. MFM showed that the textured regions observed on each hybrid are indicative of IO nanoclusters decorating the surface of single virions.Item Open Access Zeta potential: A surface electrical characteristic to probe the interaction of nanoparticles with normal and cancer human breast epithelial cells(Springer New York LLC, 2008) Zhang, Y.; Yang, M.; Portney, N. G.; Cui, D.; Budak, G.; Özbay, Ekmel; Ozkan, M.; Ozkan, C. S.We demonstrate the use of surface Zeta potential measurements as a new tool to investigate the interactions of iron oxide nanoparticles and cowpea mosaic virus (CPMV) nanoparticles with human normal breast epithelial cells (MCF10A) and cancer breast epithelial cells (MCF7) respectively. A substantial understanding in the interaction of nanoparticles with normal and cancer cells in vitro will enable the capabilities of improving diagnostic and treatment methods in cancer research, such as imaging and targeted drug delivery. A theoretical Zeta potential model is first established to show the effects of binding process and internalization process during the nanoparticle uptake by cells and the possible trends of Zeta potential change is predicted for different cell endocytosis capacities. The corresponding changes of total surface charge of cells in the form of Zeta potential measurements were then reported after incubated respectively with iron oxide nanoparticles and CPMV nanoparticles. As observed, after MCF7 and MCF10A cells were incubated respectively with two types of nanoparticles, the significant differences in their surface charge change indicate the potential role of Zeta potential as a valuable biological signature in studying the cellular interaction of nanoparticles, as well as specific cell functionality.