Machine Learning and Big Data-enabled Biotechnology (Advanced Biotechnology) （1. Auflage）

Alper, Hal S.EDT

Wiley-VCH（2026/03発売）

• 製本 Hardcover:ハードカバー版
• 商品コード 9783527354740

Full 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.

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