Data-Driven Prediction of Stem Cell Expansion Cultures


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.

Meeting Name

Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2011: Aug. 30-Sept. 3, Boston, MA)


Computer Science

Keywords and Phrases

Cell Images; Cell-Based Therapy; Culture Systems; Data-Driven; Data-Driven Approach; Ex Vivo Expansion; Growth Process; Human Operator; Robotic Cell; Stem Cell Expansion; Adaptive Control Systems; Animal Cell Culture; Expansion; Real Time Control; Robotics; Stem Cells; Forecasting

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2011 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Aug 2011