Alper Machine Learning and Big Data-enabled Biotechnology

1. Auflage 2026
ISBN: 978-3-527-85051-8
Verlag: Wiley-VCH
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 541 Seiten, E-Book

Reihe: Advanced Biotechnology

Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fields
 
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.

Autoren/Hrsg.

Alper, Hal S.

Inhaltsverzeichnis

Part I - From DNA?
1 Deep learning approaches for synthetic biology part design
2 Automated approaches for GSM development from DNA sequence
3 Predictive models from genome sequences
Part II - ?.to Proteins?
4 De novo protein structure and design tools
5 Machine learning approaches for protein engineering
6 Pathway discovery / Retrobiosynthesis
7 Enzyme functional classifications
8 Proteomics machine learning approaches and de novo identification
Part III - ?to whole cells and beyond
9 Machine learning approaches for gene expression
10 Metabolomics big data approaches
11 Use of Generative AI and natural language processing for cell models
12 Metabolic production, strain engineering, and flux design
13 Automated function and learning in biofoundries/strain designs
14 Machine learning predictions of phenotype and bioreactor performance

Über Autor(innen)

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.