Artificial Neural Network Modeling and Simulation of In-VitroNanoparticle-Cell Interactions
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.
Keywords: ARTIFICIAL NEURAL NETWORKS; NANOMEDICINE; NANOPARTICLE UPTAKE RATE; PREDICTION MODEL; TARGETED DRUG DELIVERY
Document Type: Research Article
Publication date: 01 January 2014
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
- Editorial Board
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content