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Budharaju H, Zennifer A, Sethuraman S, Paul A, Sundaramurthi D. Designer DNA biomolecules as a defined biomaterial for 3D bioprinting applications. MATERIALS HORIZONS 2022; 9:1141-1166. [PMID: 35006214 DOI: 10.1039/d1mh01632f] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
DNA has excellent features such as the presence of functional and targeted molecular recognition motifs, tailorability, multifunctionality, high-precision molecular self-assembly, hydrophilicity, and outstanding biocompatibility. Due to these remarkable features, DNA has emerged as a leading next-generation biomaterial of choice to make hydrogels by self-assembly. In recent times, novel routes for the chemical synthesis of DNA, advances in tailorable designs, and affordable production ways have made DNA as a building block material for various applications. These advanced features have made researchers continuously explore the interesting properties of pure and hybrid DNA for 3D bioprinting and other biomedical applications. This review article highlights the topical advancements in the use of DNA as an ideal bioink for the bioprinting of cell-laden three-dimensional tissue constructs for regenerative medicine applications. Various bioprinting techniques and emerging design approaches such as self-assembly, nucleotide sequence, enzymes, and production cost to use DNA as a bioink for bioprinting applications are described. In addition, various types and properties of DNA hydrogels such as stimuli responsiveness and mechanical properties are discussed. Further, recent progress in the applications of DNA in 3D bioprinting are emphasized. Finally, the current challenges and future perspectives of DNA hydrogels in 3D bioprinting and other biomedical applications are discussed.
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Affiliation(s)
- Harshavardhan Budharaju
- Tissue Engineering & Additive Manufacturing (TEAM) Lab, Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), ABCDE Innovation Centre, School of Chemical & Biotechnology, SASTRA Deemed University, Tirumalaisamudram, Thanjavur 613 401, Tamil Nadu, India.
| | - Allen Zennifer
- Tissue Engineering & Additive Manufacturing (TEAM) Lab, Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), ABCDE Innovation Centre, School of Chemical & Biotechnology, SASTRA Deemed University, Tirumalaisamudram, Thanjavur 613 401, Tamil Nadu, India.
| | - Swaminathan Sethuraman
- Tissue Engineering & Additive Manufacturing (TEAM) Lab, Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), ABCDE Innovation Centre, School of Chemical & Biotechnology, SASTRA Deemed University, Tirumalaisamudram, Thanjavur 613 401, Tamil Nadu, India.
| | - Arghya Paul
- Department of Chemical and Biochemical Engineering, The University of Western Ontario, London, ON N6A 5B9, Canada
- School of Biomedical Engineering, The University of Western Ontario, London, ON N6A 5B9, Canada
- Department of Chemistry, The University of Western Ontario, London, ON N6A 5B9, Canada
| | - Dhakshinamoorthy Sundaramurthi
- Tissue Engineering & Additive Manufacturing (TEAM) Lab, Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), ABCDE Innovation Centre, School of Chemical & Biotechnology, SASTRA Deemed University, Tirumalaisamudram, Thanjavur 613 401, Tamil Nadu, India.
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Prediction of PCR amplification from primer and template sequences using recurrent neural network. Sci Rep 2021; 11:7493. [PMID: 33820936 PMCID: PMC8021588 DOI: 10.1038/s41598-021-86357-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 03/09/2021] [Indexed: 11/09/2022] Open
Abstract
We have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network, the usual double-stranded formation between the primer and template nucleotide sequences was herein expressed as a five-lettered word. The set of words (pseudo-sentences) was placed to indicate the success or failure of PCR targeted to learn recurrent neural network (RNN). After learning pseudo-sentences, RNN predicted PCR results from pseudo-sentences which were created by primer and template sequences with 70% accuracy. These results suggest that PCR results could be predicted using learned RNN and the trained RNN could be used as a replacement for preliminary PCR experimentation. This is the first report which utilized the application of neural network for primer design and prediction of PCR results.
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