Figure 2.
Example ML application for simulating CQAs using different feeding and physiochemical variables. (a) The training and testing strategy to develop the final feeding and physiochemical model. From the full dataset of M fed-batch cultures, split it into a training set used for model optimization and a testing set used to evaluate the performance of the model. The final model is the one that performs best on the test set; (b) If the model performance is acceptable in (A), then it can be used to simulate what media components and physiochemical variables can be used for a desired CQA prediction. In this example, “s” simulations are performance with the ith showing closest match to the desired CQA. A final validation of the model can be done by using the ith CPPs in the fed batch process to confirm experimentally that the desired CQA was achieved.
(a) Schematic representation of how to deploy ML to model bioprocesses. First, a dataset is split into training and test sets, after which the training set goes through multiple iterations of tuning model parameters and calculating training error until minimum error is achieved. The model is then applied to the test set and evaluated if it is a good or bad model. (b) Demonstration of simulated critical process parameters being input into the final model derived in (a) until the set of critical process parameters that produce a desired critical quality attribute can be simulated and subsequently experimental evaluation.