Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology

ISBN-13: 9781071626160 / Angielski / Twarda / 2022

Opis

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. 

Spis treści

Preface…
Table of Contents…
Contributing Authors…

1. Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications
Fayza Daboussi and Nic D. Lindley

2. Synthetic Biology Meets Machine Learning
Brendan Fu-Long Sieow, Ryan De Sotto, Zhi Ren Darren Seet, In Young Hwang, and Matthew Wook Chang

3. Design and Analysis of Massively Parallel Reporter Assays using FORECAST
Pierre-Aurelien Gilliot and Thomas E. Gorochowski

4. Modelling Protein Complexes and Molecular Assemblies using Computational Method
Romain Launay, Elin Teppa, Jérémy Esque, and Isabelle André

5. From Genome Mining to Protein Engineering: A Structural Bioinformatics Route
Derek J. Smith

6. Creating De Novo Overlapped Genes
Dominic Y. Logel and Paul R. Jaschke 

7. Design of Gene Boolean Gates and Circuits with Convergent Promoters
Biruck Woldai Abraha and Mario Andrea Marchisio

8. Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications
Ana Zúñiga, Jérôme Bonnet, and Sarah Guiziou

9. Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization
Jing Wui Yeoh and Chueh Loo Poh

10. Modeling Subcellular Protein Recruitment Dynamics for Synthetic Biology
Kwabena A. Badu-Nkansah, Diana Sernas, Dean E. Natwick, and Sean R. Collins

11. Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio Natriegens for the Production of 1,3-Propanediol
Ye Zhang, Dehua Liu, and Zhen Chen

12. Application of GeneCloudOmics: Transcriptomics Data Analytics for Synthetic Biology
Mohamed Helmy and Kumar Selvarajoo

13. Overview of Bioinformatics Software and Databases for Metabolic Engineering
Deena M.A. Gendoo

14. Computational Simulation of Tumor-Induced Angiogenesis
Masahiro Sugimoto

15. Computational Methods and Deep Learning for Elucidating Protein Interaction Networks
Dhvani Sandip Vora, Yogesh Kalakoti, and Durai Sundar

16. Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer
Le Minh Thao Doan, Claudio Angione, and Annalisa Occhipinti

17. Machine Learning Using Neural Networks for Metabolomic Pathway Analyses
Rosalin Bonetta Valentino, Jean-Paul Ebejer, and Ingc Gianluca Valentino

18. Machine Learning and Hybrid Methods for Metabolic Pathway Modeling
Miroslava Cuperlovic-Culf, Thao Nguyen-Tran, and Steffany A.L. Bennett

19. A Machine Learning Based Approach Using Multi Omics Data to Predict Metabolic Pathways
Vidya Niranjan, Akshay Uttarkar, Aakaanksha Kaul, and Maryanne Varghese

Subject Index List…