Cell Growth Predictions with Machine Learning

Abstract: This thesis analyzes data on E. coli cell growth in a bioreactor to investigate the possibility of finding predictable correlations between the environmental parameters (sensor data) and the growth using machine learning. Discovering these correlations could be a first step toward optimizing the growth of cells to be used for cell therapy: an effective but very expensive treatment method for cancer. This could ultimately lead to decreased manufacturing costs and larger treatment availability. The data first underwent a thorough preprocessing to obtain useful features that were divided into batches. In addition, a few separate further processing methods were applied to the data for further analysis. Thereafter several different machine learning methods were implemented and evaluated on the data. All possible sensor combinations were then fed into the best-performing network and the mean absolute error was calculated for each combination. The results showed that the implemented machine learning models did not find predictable patterns between sensor inputs and growth, as the predictions did not follow the growth variations and the models mainly predicted the average yield. However, the possibility that the used approach would benefit from additional data should not be discarded.