Methods to incorporate machine learning analytics for optimizing protein purity, potency and quality in an on-demand production system for point-of-care delivery

A method of analyzing and estimating purity of a protein produced in a portable, cell-free bioprocessing system, wherein the bioprocessing system comprises a protein expression module and a protein purification module, wherein each module is associated with on-board analytics, the method comprising:

• producing the protein in the protein expression module;
• purifying the produced protein using a two-step purification process in the protein purification module, wherein the protein purification module comprises a UV sensor for each purification process and a multiplicity of programmable syringe pumps with integrated pressure sensors positioned at a back-end of each programmable syringe pump;
• obtaining at least pressure and UV sensor data from the protein purification module during purification of the produced protein;
• transmitting the data to a computer aided classification system;
• extracting features from the data with the computer aided classification system for classifying the produced protein and process conditions, wherein extracted features characterize the produced protein and such sample characterization is compared to characterization of extracted features from previously prepared proteins to provide classified features of the produced protein;
• and
• applying an unsupervised clustering process to the classified features to provide a plurality of output clusters to provide an estimation of the purity of the produced protein using in-line analysis.