Advancing iPSC Cell Culture Robotics: Machine Learning for Cell Culture Diagnostics and User Experience Improvement

This proposal focuses on enhancing the automation of induced pluripotent stem cell (iPSC) culture by integrating Machine Learning (ML) models which will lead to more consistent and higher quality cultures. Recently, STEMCELL Technologies has introduced an automated system customized to assist scientists at BC Children’s Hospital Research Institute in culturing high-quality iPSCs. The current method of diagnosing the quality of iPSC culture is based on expert judgment, which presents limitations in terms of scalability and consistency. To address this, we aim to use the automated system to perform various experiments and generate diverse image datasets. Next, robust end-to-end machine learning models will be developed to efficiently evaluate iPSC culture quality with higher accuracy enabling more reliable decision-making. Moreover, we aim to improve the user-robot interactions of the system.The successful implementation of this project has the potential to accelerate iPSC biomanufacturing and facilitate the discovery of new treatments for various diseases.

Faculty Supervisor:

Glen Tibbits

Student:

Partner:

STEMCELL Technologies Canada Inc

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Manufacturing; Mining; Professional, scientific and technical services

University:

Simon Fraser University

Program: