Description
Book SynopsisThis 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 valuabl
Table of ContentsPreface…
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…
