Modern bioprocess management, guided by sophisticated analytical techniques, digitalization, and automation, is generating abundant experimental data crucial for process optimization. Machine Learning (ML) techniques have proven crucial in analyzing these huge datasets, allowing for the efficient exploration of design spaces in bioprocessing. ML techniques are utilized in strain engineering, bioprocess optimization, scale-up, and real-time monitoring and control.
Traditional sensors in chemical and bioprocess monitoring measure fundamental parameters like pressure, temperature, and pH, but assessing the concentration of other chemicals often requires slower, invasive methods. Raman spectroscopy, which leverages the interaction of monochromatic light with molecules, enables real-time sensing and differentiating of chemical elements through their unique spectral profiles.
Machine Learning (ML) and Deep Learning (DL) applications in processing Raman spectral data can significantly enhance the prediction accuracy and robustness of analyte concentrations in complex mixtures. Advanced regression models when applied on preprocessed Raman spectra have shown better performance than traditional methods, especially in handling high-dimensional data with overlapping spectral contributions.
ML has had a significant impact on bioprocess development, particularly in strain selection and engineering stages. By leveraging large, complex datasets, ML optimizes biocatalyst design and metabolic pathway predictions, boosting productivity and efficiency. Difficulties include extrapolation limitations and the necessity for diverse datasets for non-model organisms.
ML is also instrumental in optimizing bioprocesses, where it focuses on improving titers, rates, and yields (TRY) by accurately controlling physicochemical parameters. In Bioprocess Monitoring and Control, ML techniques are vital for monitoring crucial process parameters (CPPs) and upholding critical quality attributes (CQAs) of biopharmaceutical products.
Raman spectroscopy offers real-time sensing capabilities using monochromatic light to distinguish molecules based on their unique spectral profiles. This technique has been enhanced by ML and DL methods through modeling relationships between spectral profiles and analyte concentrations. Methods such as preprocessing of spectra, feature selection, and augmentation of training data have been employed to improve prediction accuracy and robustness for monitoring multiple variables crucial in bioprocess control.
In conclusion, ML is becoming increasingly fundamental in bioprocess development, evolving from individual tools to comprehensive frameworks covering entire process pipelines. The open-source methodologies and databases are critical for rapid advancement, fostering collaboration and data accessibility. As ML methods like deep learning and reinforcement learning continue to advance with computational capabilities, they offer transformative potential for optimizing bioprocesses and shaping a data-driven future in biotechnology.
