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Model-based cell number quantification using online single-oxygen sensor data for tissue engineering perfusion

T Lambrechts1, I Papantoniou, M Sonnaert

  • 1Division M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Heverlee, Belgium; Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.

Biotechnology and Bioengineering
|April 29, 2014
PubMed
Summary
This summary is machine-generated.

A new method uses oxygen sensor data to non-invasively measure cell number and metabolic activity in tissue engineering (TE) bioreactors. This approach enables automated quality control for cell manufacturing.

Keywords:
bioprocess controldata-based mechanistic modelingonline monitoringoxygen sensorstem cell bioreactortissue engineering

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Area of Science:

  • Biotechnology
  • Biomedical Engineering
  • Cellular Engineering

Background:

  • Automated manufacturing of cellular products requires non-invasive monitoring of tissue engineering (TE) constructs within bioreactors.
  • Current monitoring techniques are insufficient for real-time quality assessment in TE bioreactors.

Purpose of the Study:

  • To develop a generic and robust method for quantifying cell number and metabolic activity in TE constructs.
  • To enable online, non-invasive quality attribute monitoring in perfusion bioreactors.

Main Methods:

  • Utilized a data-based mechanistic modeling technique correlating oxygen sensor data to cell attributes.
  • Applied dynamic perfusion conditions with step changes in perfusion rate.
  • Analyzed oxygen response dynamics to infer cell number and metabolic activity.

Main Results:

  • Successfully correlated cell number (R(2) = 0.80) and metabolic activity (R(2) = 0.82) to oxygen dynamics.
  • Demonstrated effectiveness for cell quantities ranging from 1.0 × 10^5 to millions of cells.
  • Validated a non-destructive measurement technique for bioprocess monitoring.

Conclusions:

  • The developed method provides a non-invasive, robust approach for monitoring TE constructs in bioreactors.
  • This technique supports the cost-effective up-scaling and automation of cellular construct manufacturing.
  • Opens new possibilities for effective bioprocess monitoring and quality control.