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Hierarchical Cluster Analysis of Medical Chemicals Detected by a Bacteriophage-Based Colorimetric Sensor Array

1
Department of Nanofusion Technology, Pusan National University, Busan 46241, Korea
2
Interdisciplinary Program of Genomic Data Science, Pusan National University, Busan 46241, Korea
3
Department of Statistics, Pusan National University, Busan 46241, Korea
4
Research Center for Energy Convergence and Technology, Pusan National University, Busan 46241, Korea
5
BK21PLUS Program in Embodiment: Health-Society Interaction, Department of Public Health Sciences, Graduate School, Korea University, Seoul 02841, Korea
6
Laboratory of Cardiovascular Disease, Division of Cardiology, School of Medicine, The Catholic University of Korea, Seoul 06591, Korea
7
Department of Cogno-Mechatronics Engineering, College of Nanoscience & Nanotechnology, Pusan National University, Busan 46241, Korea
8
Department of Nanoenergy Engineering, Pusan National University, Busan 46241, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Nanomaterials 2020, 10(1), 121; https://doi.org/10.3390/nano10010121
Received: 29 November 2019 / Revised: 31 December 2019 / Accepted: 6 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Virus-Based Nanomaterials and Nanostructures)
M13 bacteriophage-based colorimetric sensors, especially multi-array sensors, have been successfully demonstrated to be a powerful platform for detecting extremely small amounts of target molecules. Colorimetric sensors can be fabricated easily using self-assembly of genetically engineered M13 bacteriophage which incorporates peptide libraries on its surface. However, the ability to discriminate many types of target molecules is still required. In this work, we introduce a statistical method to efficiently analyze a huge amount of numerical results in order to classify various types of target molecules. To enhance the selectivity of M13 bacteriophage-based colorimetric sensors, a multi-array sensor system can be an appropriate platform. On this basis, a pattern-recognizing multi-array biosensor platform was fabricated by integrating three types of sensors in which genetically engineered M13 bacteriophages (wild-, RGD-, and EEEE-type) were utilized as a primary building block. This sensor system was used to analyze a pattern of color change caused by a reaction between the sensor array and external substances, followed by separating the specific target substances by means of hierarchical cluster analysis. The biosensor platform could detect drug contaminants such as hormone drugs (estrogen) and antibiotics. We expect that the proposed biosensor system could be used for the development of a first-analysis kit, which would be inexpensive and easy to supply and could be applied in monitoring the environment and health care. View Full-Text
Keywords: M13 bacteriophage; multi-array sensors; hierarchical cluster analysis; high selectivity M13 bacteriophage; multi-array sensors; hierarchical cluster analysis; high selectivity
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Kim, C.; Lee, H.; Devaraj, V.; Kim, W.-G.; Lee, Y.; Kim, Y.; Jeong, N.-N.; Choi, E.J.; Baek, S.H.; Han, D.-W.; Sun, H.; Oh, J.-W. Hierarchical Cluster Analysis of Medical Chemicals Detected by a Bacteriophage-Based Colorimetric Sensor Array. Nanomaterials 2020, 10, 121.

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