Multi-level machine learning for predictive and prescriptive applications

A non-transitory machine-readable storage medium comprising instructions that, when executed by a processing device, cause the processing device to perform operations comprising:

• receive cell growth data associated with a current cell culture of a cell growth system, the cell growth data comprising:
• growth input parameter values indicative of a growth rate of the current cell culture, and
• process analytical technology (PAT) sensor data comprising indications of one or more of cell count, density, or target product formation;
• provide the cell growth data to a mechanistic model as input;
• obtain, from the mechanistic model, mechanistic output comprising a prediction of future conditions of the current cell culture based on the cell growth data;
• provide the cell growth data to a statistical model;
• obtain, from the statistical model, statistical output comprising relationships in the cell growth data and historical cell growth data;
• identify, using a first machine learning model, a prescriptive action to alter a yield of a target product of the current cell culture based on the mechanistic output and the statistical output, wherein the prescriptive action modifies a metabolism rate of the current cell culture based on the cell growth data, the prescriptive action comprising an adjustment to at least one of feed flow rate, air flow rate, agitation rate, applied heat, applied pressure, or oxygen flow rate;
• identify, using the first machine learning model, a first level of confidence that the prescriptive action, when performed, alters the yield of the target product;
• and
• cause the cell growth system to perform the identified prescriptive action in view of the first level of confidence.