Date of Award

8-1-2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Chemistry

Content Description

1 online resource (xiv, 184 pages) : color illustrations.

Dissertation/Thesis Chair

Mehmet V Yigit

Committee Members

Maksim Royzen, Jia Sheng, Ken Halvorsen

Keywords

2D- nanoparticles, Bacterial sorting, Machine learning, Nanosensor, Nanozyme, PLSDA, Biosensors, Nanoparticles, Enzymes, Nanobiotechnology, Oligonucleotides

Subject Categories

Chemistry

Abstract

Biosensing is an ever-evolving field with many resources devoted towards new approaches in sensing and refining the existing methods. For a long time, such approaches involved use of state-of-the-art instruments and were often proved to be time consuming and expensive. Nanoparticles with their unique properties, inexpensive synthesis and ease of manipulation have found purpose in myriad fields including biosensing. Different nanoparticles based on their composition, morphology, dimensionality, and surface modifications have already gained foot hold in the biosensor ranks. The unique opto-electric properties due to the quantum effect in the nanoparticles have also made them one of the leading alternatives for biological enzymes. One such emerging class of biosensors is the one involving two dimensional nanoparticles. Single stranded oligonucleotides have garnered considerable attention due to their role in all the stages of DNA metabolism (transcription, replication, recombination, and repair) and their ability to interact with other non-DNA biomolecules Owing to this lots of inroads have been made in techniques to synthesize ssDNAs with appreciable yields. Compared to double stranded oligonucleotides, ssDNAs have flexibility and can form secondary structures by the typical Watson-Crick pairing as well as the atypical non-canonical base pairing. Many applications in the emerging field of the nucleic acid technology takes advantage of this knowledge by using the ssDNAs as aptamers in the detection of the biomolecules they interact with. Generally, these ssDNA-aptamers are composed of the four types of nucleobases (adenine (A), cytosine (C), guanine (G), and thymine (T)). The base pair stacking along with the overall intra-strand interactions create a microenvironment favorable for target binding. The combination of these nanoparticles with nucleic acids has been employed in diagnostics and environmental monitoring applications. The sensors based on nanoparticles and ssDNA-aptamers often rely on the binding efficiency of the aptamer to the target for detection. The nanoparticles generally act as reporters for these binding events. Many of these high-throughput tools face unique challenges for data analysis and interpretation. The use of statistical and more advanced machine learning tools has proven useful in combating some of these pitfalls. The more the researchers embrace the use of artificial intelligence in data analysis, the more it is proving to help in accelerating the optimization process and increase the scope of data interpretation. Here, we demonstrate that the pitfalls in data analysis especially in the alternate detection techniques can be circumvented to some extent using statistical and machine learning tools. We describe how machine learning along with the systematic study of the effects of the contributing elements in the array when applied to data analysis was helpful in fabricating a less labor intensive but more sensitive sensor design. Further, the use of different machine learning tools was used to improve in sample prediction of complex matrices by discerning changes in pattern due to as small difference as a single gene mutation. The use of statistical analysis of the effects of proteins and metal ions on the nanozyme activity of 2D-nanoparticles has been described towards designing nanosensors.

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