Mario Alfredo Carrillo Arevalo, RPTU Kaiserslautern-Landau
Title: Investigation of machine learning methods to monitor 3D stem cell culture in a bioreactor
The development and discovery of new drugs require a large number of tests to fulfill strict quality protocols. These tests are mainly performed on animals which involve an ethical debate, are expensive, and are time-consuming. The most relevant alternative to replace, reduce and refine this practice, is the use of cell-based model systems. Especially the cultivation of human induced pluripotent stem cells (hiPSC) in a 3D environment. This process is carried out in bioreactors with controlled conditions. However, the cultivation process can be prone to failures or unexpected behaviors. Therefore, it is necessary to establish a real-time monitoring mechanism that allows taking early decisions and enables large-scale production in the long term.
This talk presents an interdisciplinary work that investigates machine learning methods and other computational science tools to be applied to monitor the state of these processes. It also illustrates the results of the applied algorithms as well as their behavior when run on an embedded platform with limited resources.
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