Machine Learning and Big Data-enabled Biotechnology discusses how machine learning and big data can be used in biotechnology for a wide breadth of topics, providing tools essential to support efforts in process control, reactor performance evaluation, and research target identification.

Topics explored in Machine Learning and Big Data-enabled Biotechnology include:

Deep learning approaches for synthetic biology part design and automated approaches for GSM development from DNA sequences
De novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approaches
Metabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell models
Automated function and learning in biofoundries and strain designs
Machine learning predictions of phenotype and bioreactor performance

Machine Learning and Big Data-enabled Biotechnology earns a well-deserved spot on the bookshelves of reaction, process, catalytic, and environmental engineers seeking to explore the vast opportunities presented by rapidly developing technologies.

About the Author

Dr. Hal S. Alper is the Cockrell Family Regents Chair in Engineering #1 at The University of Texas at Austin in the McKetta Department of Chemical Engineering. His research focuses on applying and extending the approaches of metabolic engineering, synthetic biology, systems biology, and protein engineering.

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