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Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology

  • Book
  • © 2023

Overview

Editors:
  1. Kumar Selvarajoo

    1. Bioinformatics Institute, Agency for Science, Technology & Research, Singapore, Singapore

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  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 2553)

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About this book

This volume provides protocols for computational, statistical, and machine learning methods that are mainly applied to the study of metabolic engineering, synthetic biology, and disease applications. These techniques support the latest progress in cross-disciplinary research that integrates the different scales of biological complexity. The topics covered in this book are geared toward researchers with a background in engineering, computational analytical, and modeling experience and cover a broad range of topics in computational and machine learning approaches. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Comprehensive and practical, Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology is a valuable resource for any researcher or scientist who wants to learn more about the latest computational methods and how they are applied toward the understanding and prediction of complex biology. 


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Table of contents (19 protocols)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications

    • Fayza Daboussi, Nic D. Lindley
    Pages 1-20

  3. Synthetic Biology Meets Machine Learning

    • Brendan Fu-Long Sieow, Ryan De Sotto, Zhi Ren Darren Seet, In Young Hwang, Matthew Wook Chang
    Pages 21-39

  4. Design and Analysis of Massively Parallel Reporter Assays Using FORECAST

    • Pierre-Aurelien Gilliot, Thomas E. Gorochowski
    Pages 41-56

  5. Modeling Protein Complexes and Molecular Assemblies Using Computational Methods

    • Romain Launay, Elin Teppa, Jérémy Esque, Isabelle André
    Pages 57-77

  6. From Genome Mining to Protein Engineering: A Structural Bioinformatics Route

    • Derek J. Smith
    Pages 79-94

  7. Creating De Novo Overlapped Genes

    • Dominic Y. Logel, Paul R. Jaschke
    Pages 95-120

  8. Design of Gene Boolean Gates and Circuits with Convergent Promoters

    • Biruck Woldai Abraha, Mario Andrea Marchisio
    Pages 121-154

  9. Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications

    • Ana Zúñiga, Jérôme Bonnet, Sarah Guiziou
    Pages 155-171

  10. Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization

    • Jing Wui Yeoh, Chueh Loo Poh
    Pages 173-187

  11. Modeling Subcellular Protein Recruitment Dynamics for Synthetic Biology

    • Kwabena A. Badu-Nkansah, Diana Sernas, Dean E. Natwick, Sean R. Collins
    Pages 189-207

  12. Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio natriegens for the Production of 1,3-Propanediol

    • Ye Zhang, Dehua Liu, Zhen Chen
    Pages 209-220

  13. Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology

    • Mohamed Helmy, Kumar Selvarajoo
    Pages 221-263

  14. Overview of Bioinformatics Software and Databases for Metabolic Engineering

    • Deena M. A. Gendoo
    Pages 265-274

  15. Computational Simulation of Tumor-Induced Angiogenesis

    • Masahiro Sugimoto
    Pages 275-283

  16. Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

    • Dhvani Sandip Vora, Yogesh Kalakoti, Durai Sundar
    Pages 285-323

  17. Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer

    • Le Minh Thao Doan, Claudio Angione, Annalisa Occhipinti
    Pages 325-393

  18. Machine Learning Using Neural Networks for Metabolomic Pathway Analyses

    • Rosalin Bonetta Valentino, Jean-Paul Ebejer, Gianluca Valentino
    Pages 395-415

  19. Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

    • Miroslava Cuperlovic-Culf, Thao Nguyen-Tran, Steffany A. L. Bennett
    Pages 417-439

  20. A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways

    • Vidya Niranjan, Akshay Uttarkar, Aakaanksha Kaul, Maryanne Varghese
    Pages 441-452
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Editors and Affiliations

  • Bioinformatics Institute, Agency for Science, Technology & Research, Singapore, Singapore

    Kumar Selvarajoo

Bibliographic Information

  • Book Title: Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology

  • Editors: Kumar Selvarajoo

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-2617-7

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

  • Hardcover ISBN: 978-1-0716-2616-0Published: 14 October 2022

  • Softcover ISBN: 978-1-0716-2619-1Published: 13 October 2022

  • eBook ISBN: 978-1-0716-2617-7Published: 13 October 2022

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XII, 455

  • Number of Illustrations: 27 b/w illustrations, 133 illustrations in colour

  • Topics: Bioinformatics

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