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Data-driven prediction of stem cell expansion cultures

  • Zhaozheng Yin
  • , Dai Fei Ker
  • , Silvina Junkers
  • , Takeo Kanade
  • , Mei Chen
  • , Lee Weiss
  • , Phil Campbell
  • Carnegie Mellon University
  • Intel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Stem cell expansion culture aims to generate sufficient number of clinical-grade cells for cell-based therapies. One challenge for ex vivo expansion is to decide the appropriate time to perform subculture. Traditionally, this decision has been reliant on human estimation of cell confluency and predicting when confluency will approach a desired threshold. However, the use of human operators results in highly subjective decision-making and is prone to inter- and intra-operator variability. Using a real-time cell image analysis system, we propose a data-driven approach to model the cell growth process and predict the cell confluency levels, signaling times to subculture. This approach has great potential as a tool for adaptive real-time control of subculturing, and it can be integrated with robotic cell culture systems to achieve complete automation.

Original languageEnglish
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages3577-3580
Number of pages4
DOIs
StatePublished - 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Country/TerritoryUnited States
CityBoston, MA
Period08/30/1109/3/11

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