Machine Learning and Big Data-enabled Biotechnology

著者名：Alper, Hal S. (EDT)

Wiley-VCH（2026/01/15発売）

• 言語：ENG
• ISBN：9783527354740
• eISBN：9783527850518

Description

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