Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure

Document Type : Research Paper


1 Agricultural Machinery Engineering Dept., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

2 Associate Professor in agricultural Machinery Engineering Dept., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

3 Professor in agricultural Machinery Engineering Dept., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

4 Assistant Professor in Department of Advanced Medical Sciences and Technologies, School of Paramedicine, Shahid Sadoughi University of Medical Sciences, Yazd 8916188635, Iran


Developing a biosensor faces the different challenges for parameter optimization and calibration. In this study, a machine learning based approach is used to model and optimize the effective parameters of an electrochemical nanobiosensor based on thiolated probe-functionalized gold nanorods (GNRs) decorated on the graphene oxide (GO) sheet on the surface of a glassy carbon electrode (GCE). The response of the biosensor was considered as the output and eight effective factors including GO concentration, GNR concentration, probe concentration, probe time, MCH time, hybridization time, Oracet Blue (OB) concentration, and OB incubation time were used as inputs to train and model an artificial neural network. The experimental results demonstrate that the output of the developed model has an acceptable compatibility with the results obtained in the laboratory. The developed model is able to predict the output of the nanobiosensor with accuracy of 96.91% and the mean absolute percentage error (MAPE) value of 5.5090 %. Finally, genetic algorithm is used to find the optimum values of these parameters which yield the maximum value of the nanobiosensor output. The optimization results indicated that this method has better performance compared to the laboratory results and this method can be used for nanobiosensor design.


Main Subjects

Abdullah, J., Ahmad, M., Heng, L. Y., Karuppiah, N., & Sidek, H. (2008). Evaluation of an optical phenolic biosensor signal employing artificial neural networks. Sensors and Actuators B: Chemical134(2), 959-965.
Akbari, E., Buntat, Z., Shahraki, E., Zeinalinezhad, A., & Nilashi, M. (2016). ANFIS modeling for bacteria detection based on GNR biosensor. Journal of Chemical Technology & Biotechnology91(6), 1728-1736.
Alonso, G. A., Istamboulie, G., Ramírez-García, A., Noguer, T., Marty, J. L., & Muñoz, R. (2010). Artificial neural network implementation in single low-cost chip for the detection of insecticides by modeling of screen-printed enzymatic sensors response. Computers and Electronics in Agriculture74(2), 223-229.
Alonso, G. A., Istamboulie, G., Noguer, T., Marty, J. L., & Muñoz, R. (2012). Rapid determination of pesticide mixtures using disposable biosensors based on genetically modified enzymes and artificial neural networks. Sensors and Actuators B: Chemical164(1), 22-28.
Azimzadeh, M., Rahaie, M., Nasirizadeh, N., Ashtari, K., & Naderi-Manesh, H. (2016). An electrochemical nanobiosensor for plasma miRNA-155, based on graphene oxide and gold nanorod, for early detection of breast cancer. Biosensors and Bioelectronics77, 99-106.
Azimzadeh, M., Rahaie, M., Nasirizadeh, N., Daneshpour, M., & Naderi-Manesh, H. (2017). Electrochemical miRNA biosensors: the benefits of nanotechnology. Nanomedicine Research Journal2(1), 36-48.
Baronas, R., Ivanauskas, F., & Kulys, J. (2009). Mathematical modeling of biosensors: an introduction for chemists and mathematicians (Vol. 9). Springer Science & Business Media.
Bartlett, P. N., Toh, C. S., Calvo, E. J., & Flexer, V. (2008). Modelling biosensor responses (pp. 267-325). Wiley: Chichester, England.
Blaedel, W. J., Kissel, T. R., & Boguslaski, R. C. (1972). Kinetic behavior of enzymes immobilized in artificial membranes. Analytical Chemistry44(12), 2030-2037.
Eaimkhong, S. (2013). Application of Nanotechnology in Biological Research: Diagnostics and Physical Manipulation (Doctoral dissertation, UCLA).
Ferentinos, K. P., Yialouris, C. P., Blouchos, P., Moschopoulou, G., Tsourou, V., & Kintzios, S. (2012). The use of artificial neural networks as a component of a cell-based biosensor device for the detection of pesticides. Procedia Engineering47, 989-992.
Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer27(2), 83-85.
Hou, H., Fan, Y., Wang, S., Si, L., & Li, B. (2016). Immunomodulatory activity of Alaska pollock hydrolysates obtained by glutamic acid biosensor–Artificial neural network and the identification of its active central fragment. Journal of functional foods24, 37-47.
Gutés, A., Céspedes, F., Alegret, S., & Del Valle, M. (2005). Determination of phenolic compounds by a polyphenol oxidase amperometric biosensor and artificial neural network analysis. Biosensors and Bioelectronics20(8), 1668-1673.
Liu, J. M., Hu, Y., Yang, Y. K., Liu, H., Fang, G. Z., Lu, X., & Wang, S. (2018). Emerging functional nanomaterials for the detection of food contaminants. Trends in Food Science & Technology71, 94-106.
Maleki, N., Kashanian, S., Maleki, E., & Nazari, M. (2017). A novel enzyme based biosensor for catechol detection in water samples using artificial neural network. Biochemical engineering journal128, 1-11.
Mell, L. D., & Maloy, J. (1975). Model for the amperometric enzyme electrode obtained through digital simulation and applied to the immobilized glucose oxidase system. Analytical Chemistry47(2), 299-307.
Mishra, R. K., Alonso, G. A., Istamboulie, G., Bhand, S., & Marty, J. L. (2015). Automated flow based biosensor for quantification of binary organophosphates mixture in milk using artificial neural network. Sensors and Actuators B: Chemical208, 228-237.
Rangelova, V., Tsankova, D., & Dimcheva, N. (2010). Soft computing techniques in modelling the influence of ph and temperature on dopamine biosensor. Intelligent and Biosensors, 99.
Rodriguez-Mozaz, S., Marco, M. P., De Alda, M. L., & Barceló, D. (2004). Biosensors for environmental applications: Future development trends. Pure and applied chemistry76(4), 723-752.
Sapeliauskas, E., Barauskas, D., Vanagas, G., & Virzonis, D. (2014, September). Surface micromachined CMUTs for liquid phase sensing. In 2014 IEEE International Ultrasonics Symposium (pp. 2580-2583). IEEE.
Sharma, R., Ragavan, K. V., Thakur, M. S., & Raghavarao, K. S. M. S. (2015). Recent advances in nanoparticle based aptasensors for food contaminants. Biosensors and Bioelectronics74, 612-627.
Topkaya, S. N., Azimzadeh, M., & Ozsoz, M. (2016). Electrochemical biosensors for cancer biomarkers detection: Recent advances and challenges. Electroanalysis28(7), 1402-1419.
Topkaya, S. N., & Azimzadeh, M. (2016). Biosensors of in vitro detection of cancer and bacterial cells. Nanobiosensors for Personalized and Onsite Biomedical Diagnosis. Institution of Engineering and Technology, 73-94.
Valdés-Ramírez, G., Gutiérrez, M., Del Valle, M., Ramírez-Silva, M. T., Fournier, D., & Marty, J. L. (2009). Automated resolution of dichlorvos and methylparaoxon pesticide mixtures employing a Flow Injection system with an inhibition electronic tongue. Biosensors and Bioelectronics24(5), 1103-1108.