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.
Table of Contents:
Preface
Chapter 1: From genome to actionable insights in biotechnology
James Morrissey, Benjamin Strain, Cleo Kontoravdi
Chapter 2: Automated approaches for the development of genome-scale metabolic network models
Emma M. Glass, Deborah A. Powers, Jason A. Papin
Chapter 3: Machine-guided approaches for synthetic biology part design
Marc Amil, Leandro N. Ventimiglia, Aleksej Zelezniak
Chapter 4: Machine Learning for Sequence-to-Function Approaches
Rana A. Barghout, Maxim Kirby, Austin Zheng, Lya Chinas, Marjan Mohammadi, Zhiqing Xu, Benjamin Sanchez-Lengeling, and Radhakrishnan Mahadevan
Chapter 5: Prediction of Enzyme Functions by Artificial Intelligence
Ha Rim Kim, Hongkeun Ji, Gi Bae Kim, and Sang Yup Lee
Chapter 6: Design of Biochemical Pathways via AI/ML enabled Retrobiosynthesis
Hongxiang Li, Xuan Liu, and Huimin Zhao
Chapter 7: Machine learning to accelerate the discovery of therapeutic peptides
Nicole Soto-Garcia, Mehdi D. Davari, and David Medina-Ortiz
Chapter 8: Machine Learning Approaches for HTP Microbial Identification/Culturing
Mohamed Mastouri, Yang Zhang
Chapter 9: Generative AI for Knowledge Mining of Synthetic Biology and Bioprocess Engineering Literature
Zhengyang Xiao, Yinjie J. Tang
Chapter 10: Metabolomics big data approaches
Kenya Tanaka, Christopher J. Vavricka, Tomohisa Hasunuma
Chapter 11: Strain engineering, flux design, and metabolic production using Big Data: Ongoing advances and opportunities
Rafael S. Costa and Rui Henriques
Chapter 12: Next-generation metabolic flux analysis using machine learning
Ahmed Almunaifi, Richard C. Law, Samantha O’Keeffe, Kartikeya Pande, Tongjun Xiang, Onyedika Ukwueze, Aranaa Odai-Okley, Pin-Kuang Lai, Junyoung O. Park
Chapter 13: Streamlining the Design-Build-Test-Learn Process in Automated Biofoundries
Enrico Orsi, Nicolás Gurdo, and Pablo I. Nikel
Chapter 14: Machine Learning-Enhanced Hybrid Modeling for Phenotype Prediction and Bioreactor Optimization
Oliver Pennington, Yirong Chen, Youping Xie, and Dongda Zhang
About the Author :
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 serves on the Graduate Studies Committee for the Cell and Molecular Biology Department and in the Biochemistry Department. His research focuses on applying and extending the approaches of synthetic biology, systems biology, and protein engineering.
