Digitala Vetenskapliga Arkivet

Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Decoding Spider Silk: Machine Learning Approaches to Uncovering Property Determinants
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
2024 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Spider silk, renowned for its high performance and biodegradable nature, holds significant promise across various industries seeking alternatives to unsustainable petroleum-based fibres. However, despite advancements in the artificial spinning of spider silk fibres, much remains to be uncovered to achieve both high yield and the exceptional mechanical properties characteristic of native silks.To address this challenge, the study delved into the expression patterns across 291 spider individuals and their silk compositions, aiming to elucidate the factors influencing the toughness, tensile strength, strain at break, and young's modulus of silk fibres. Through comprehensive analysis, the study sheds light on the challenges of conducting quantitative analyses across diverse species. Despite limitations arising from evolutionary distant samples and homologous clustering, the novel analysis method revealed signals of combinatory protein expressions and their impact on the mechanical properties of the silk fibre. The quantitative expression levels appear to contain valuable information when considered alongside protein sequences and motifs within Spidroin proteins, indicating paths for further analysis.The methodology shows promise, and the findings suggest clear directions for future research, paving the way for optimizing silk production and harnessing its full potential in diverse applications. 

Place, publisher, year, edition, pages
2024. , p. 43
Series
UPTEC X ; 24035
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-535504OAI: oai:DiVA.org:uu-535504DiVA, id: diva2:1886747
External cooperation
SciLifeLab Uppsala
Educational program
Molecular Biotechnology Engineering Programme
Supervisors
Examiners
Available from: 2024-08-13 Created: 2024-08-04 Last updated: 2024-09-19Bibliographically approved

Open Access in DiVA

fulltext(5128 kB)519 downloads
File information
File name FULLTEXT01.pdfFile size 5128 kBChecksum SHA-512
23f6918853722057daf09897a2bdee536e96d95cc8c1342cb43c84f7e2165ba246107e427b20fa5b2a738f3f58c0e27d9f8892cb5a226a244283f8b71184997d
Type fulltextMimetype application/pdf

By organisation
Computational Biology and Bioinformatics
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 519 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1095 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf