uu.seUppsala universitets publikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Fast processing of label-free video microscopy movies of human and bacterial cell populations growing in vitro during chemical exposure
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för biologisk grundutbildning. (Cancer Pharmacology and Computational Medicine, Department of Medical Sciences)
2016 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

A fast computational framework for large-scale parallel processing of label-free video microscopy movies of human and bacterial cell populations growing in vitro during chemical exposure was developed in MATLAB®. The overarching aim was to quantify and study time evolving morphological effects due to chemical perturbations caused by single drugs and combinations. Using this framework, a previously reported method for characterization of differences in time evolving morphologies of human cell populations, based on pixel histogram hierarchies of phase-contrast microscopy images, was re-implemented, refined and subsequently optimized with respect to method-specific tuning parameters. This implementation  was also generalized for time-lapse microscopy movies of bacterial cell cultures, generated by the oCelloScope™ system, which acquires multiple series of images of non-adherent cell populations in the cell culture medium. In addition, a separate computational framework for large-scale parallel quantification of the bacterial growth was deployed as an alternative to the growth kinetics analysis provided by the integrated commercial software of the oCelloScope™ system. The potential of the implemented frameworks was demonstrated on experimental data by processing time-lapse movies from different human and bacterial cell populations, while being exposed to different single chemical compounds and combinations. These novel computational tools are compatible with either single high-end multi-core computers or cloud-based distributed computing infrastructures offered via MapReduce, and Hadoop® MapReduce, respectively. This enables fast and fault-tolerant processing of huge video microscopy datasets and opens for optimization of user-defined tuning parameters.

sted, utgiver, år, opplag, sider
2016. , s. 118
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-303946OAI: oai:DiVA.org:uu-303946DiVA, id: diva2:974708
Utdanningsprogram
Master Programme in Bioinformatics
Presentation
2016-09-20, C4:301, BMC, 13:00 (engelsk)
Veileder
Tilgjengelig fra: 2016-09-27 Laget: 2016-09-27 Sist oppdatert: 2016-09-27bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric

urn-nbn
Totalt: 458 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf