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.