Enabling New Functionality, Transport Analysis, and Deep Learning in Organ-on-a-Chip Systems

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The Petri dish, which was invented in the 19th century, remains a common device for culturing living cells in the biology research lab, but does not capture many of the important elements of the environment surrounding real living cells, tissues, and organs in the body. While the Petri dish and other common lab cultureware have led to many discoveries in the past, many complex biology questions today, especially those related to disease processes, involve critical factors in the cellular microenvironment such as communication between multiple cell types, biomechanical forces, and blood flow in nearby blood vessels, many of which have been previously neglected. Organ-on-a-chip (OOC) systems (or OOCs) are a new technology that addresses this problem. OOCs are living 3D tissue models engineered precisely in the lab by combining microfabrication techniques, microscale fluid flow, and various biological components such as living cells and biomaterials. By combining these elements appropriately, OOCs can mimic human tissue structure, behaviour, and function, and respond to environmental stimuli more accurately than any other existing in vitro tissue model. Thus, OOCs have potential to revolutionize biomedical research and accelerate scientific discovery. Despite this progress, major engineering challenges exist that hinder OOCs from widespread usage in research and industry. These engineering challenges include: lack of more efficient, reliable fabrication methods to mass manufacture OOCs; limited functionality built directly into each OOC; lack of understanding of fluid transport processes within OOCs; and bottleneck in the analysis of images and other data acquired from OOC experiments. ******The goal of my research is to solve these issues, leading to more widespread use of this technology. The project will focus on 3 main objectives: 1) increase functionality by developing new fabrication methods, creating multi-layered device architectures, adding new on-chip sensing elements, and connecting modular OOCs together to create complex OOC multi-systems; 2) analyze fluid transport processes in various OOCs using particle-tracking methods adopted from fluid mechanics research; and 3) applying deep learning computer algorithms to “train” a computer to automatically and efficiently identify biological features from microscopy images acquired from OOCs.******The project will significantly improve our understanding of OOC operation, and advance our engineering capabilities for developing next-generation OOCs. These advances will lead to new biomedical discoveries as well as novel innovations that will benefit the Canadian biotech industry, and feed the growing ecosystem of startup companies in Canada. It will also support the training of 7 highly qualified personnel and 10 additional engineering students, who will all be exposed to a highly interdisciplinary world-class research environment where they will acquire technical, transferrable, and leadership skills.**