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Systems Metabolic Engineering

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Cover of 'Systems Metabolic Engineering'

Table of Contents

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    Book Overview
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    Chapter 1 Genome-Scale Model Management and Comparison
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    Chapter 2 Automated Genome Annotation and Metabolic Model Reconstruction in the SEED and Model SEED
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    Chapter 3 Metabolic Model Refinement Using Phenotypic Microarray Data
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    Chapter 4 Linking genome-scale metabolic modeling and genome annotation.
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    Chapter 5 Resolving Cell Composition Through Simple Measurements, Genome-Scale Modeling, and a Genetic Algorithm
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    Chapter 6 A Guide to Integrating Transcriptional Regulatory and Metabolic Networks Using PROM (Probabilistic Regulation of Metabolism)
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    Chapter 7 Kinetic Modeling of Metabolic Pathways: Application to Serine Biosynthesis
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    Chapter 8 Computational tools for guided discovery and engineering of metabolic pathways.
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    Chapter 9 Retrosynthetic design of heterologous pathways.
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    Chapter 10 Customized Optimization of Metabolic Pathways by Combinatorial Transcriptional Engineering
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    Chapter 11 Adaptive Laboratory Evolution for Strain Engineering
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    Chapter 12 Systems Metabolic Engineering
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    Chapter 13 Identification of Mutations in Evolved Bacterial Genomes
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    Chapter 14 Discovery of Posttranscriptional Regulatory RNAs Using Next Generation Sequencing Technologies
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    Chapter 15 13 C-Based Metabolic Flux Analysis: Fundamentals and Practice
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    Chapter 16 Nuclear Magnetic Resonance Methods for Metabolic Fluxomics
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    Chapter 17 Using Multiple Tracers for 13 C Metabolic Flux Analysis
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    Chapter 18 Isotopically Nonstationary 13 C Metabolic Flux Analysis
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    Chapter 19 Sample Preparation and Biostatistics for Integrated Genomics Approaches
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    Chapter 20 Targeted Metabolic Engineering Guided by Computational Analysis of Single-Nucleotide Polymorphisms (SNPs)
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    Chapter 21 Linking RNA Measurements and Proteomics with Genome-Scale Models
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    Chapter 22 Comparative Transcriptome Analysis for Metabolic Engineering
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    Chapter 23 Merging multiple omics datasets in silico: statistical analyses and data interpretation.
Attention for Chapter 8: Computational tools for guided discovery and engineering of metabolic pathways.
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Chapter title
Computational tools for guided discovery and engineering of metabolic pathways.
Chapter number 8
Book title
Systems Metabolic Engineering
Published in
Methods in molecular biology, February 2013
DOI 10.1007/978-1-62703-299-5_8
Pubmed ID
Book ISBNs
978-1-62703-298-8, 978-1-62703-299-5
Authors

Moura M, Broadbelt L, Tyo K, Matthew Moura, Linda Broadbelt, Keith Tyo, Moura, Matthew, Broadbelt, Linda, Tyo, Keith

Abstract

With a high demand for increasingly diverse chemicals, as well as sustainable synthesis for many existing chemicals, the chemical industry is increasingly looking to biosynthesis. The majority of biosynthesis examples of useful chemicals are either native metabolites made by an organism or the heterologous expression of known metabolic pathways into a more amenable host. For chemicals that no known biosynthetic route exists, engineers are increasingly relying on automated computational algorithms, as described here, to identify potential metabolic pathways. In this chapter, we review a broad range of approaches to predict novel metabolic pathways. Broadly, these can rely on biochemical databases to assemble known reactions into a new pathway or rely on generalized biochemical rules to predict unobserved enzymatic reactions that are likely feasible. Many programs are freely available and immediately useable by non-computationally experienced scientists.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Greece 1 3%
Germany 1 3%
Thailand 1 3%
Unknown 27 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Researcher 5 16%
Professor 3 10%
Other 3 10%
Professor > Associate Professor 3 10%
Other 4 13%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 29%
Biochemistry, Genetics and Molecular Biology 4 13%
Chemical Engineering 3 10%
Engineering 2 6%
Computer Science 1 3%
Other 2 6%
Unknown 10 32%