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
