Beschreibung
The book discusses how Machine Learning and Big Data is and can be used in biotechnology for a wide breath of topics. It is separated into three main parts, with the first covering DNA and ranging from ?synthetic biology part design (such as promoters)? to ?predictions from genome sequences?. The second part concerns proteins, with topics ranging from ?structure and design tools? to ?pathway discovery / retrobiosynthesis?, while the last part covers whole cells and ranges from ?Machine Learning approaches for gene expression? to ?Machine Learning predictions of phenotype and bioreactor performance?
Autorenporträt
Dr. Hal Alper is the Kenneth A. Kobe Professor in Chemical Engineering and Executive Director of the Center for Biomedical Research Support at The University of Texas at Austin. He earned his Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology in 2006 and was a postdoctoral research associate at the Whitehead Institute for Biomedical Research from 2006-2008, and at Shire Human Genetic Therapies from 2007-2008. Dr. Alper also serves on the Graduate Studies Committee for the Cell and Molecular Biology Department and the Biochemistry Department. He is currently the Principal Investigator of the Laboratory for Cellular and Metabolic Engineering at The University of Texas at Austin where his lab focuses on metabolic and cellular engineering in the context of biofuel, biochemical, and biopharmaceutical production in an array of model host organisms. His research focuses on applying and extending the approaches of synthetic biology, systems biology, and protein engineering.
Herstellerkennzeichnung:
Wiley-VCH GmbH
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