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Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology

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Cover of 'Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications
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    Chapter 2 Synthetic Biology Meets Machine Learning
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    Chapter 3 Design and Analysis of Massively Parallel Reporter Assays Using FORECAST
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    Chapter 4 Modeling Protein Complexes and Molecular Assemblies Using Computational Methods
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    Chapter 5 From Genome Mining to Protein Engineering: A Structural Bioinformatics Route
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    Chapter 6 Creating De Novo Overlapped Genes
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    Chapter 7 Design of Gene Boolean Gates and Circuits with Convergent Promoters
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    Chapter 8 Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications
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    Chapter 9 Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization
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    Chapter 10 Modeling Subcellular Protein Recruitment Dynamics for Synthetic Biology
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    Chapter 11 Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio natriegens for the Production of 1,3-Propanediol
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    Chapter 12 Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology
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    Chapter 13 Overview of Bioinformatics Software and Databases for Metabolic Engineering
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    Chapter 14 Computational Simulation of Tumor-Induced Angiogenesis
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    Chapter 15 Computational Methods and Deep Learning for Elucidating Protein Interaction Networks
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    Chapter 16 Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer
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    Chapter 17 Machine Learning Using Neural Networks for Metabolomic Pathway Analyses
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    Chapter 18 Machine Learning and Hybrid Methods for Metabolic Pathway Modeling
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    Chapter 19 A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways
Attention for Chapter 19: A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways
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Chapter title
A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways
Chapter number 19
Book title
Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology
Published by
Humana, New York, NY, October 2022
DOI 10.1007/978-1-0716-2617-7_19
Pubmed ID
36227554
Book ISBNs
978-1-07-162616-0, 978-1-07-162617-7
Authors

Vidya Niranjan, Akshay Uttarkar, Aakaanksha Kaul, Maryanne Varghese, Niranjan, Vidya, Uttarkar, Akshay, Kaul, Aakaanksha, Varghese, Maryanne

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