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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 9: Retrosynthetic design of heterologous pathways.
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Citations

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Chapter title
Retrosynthetic design of heterologous pathways.
Chapter number 9
Book title
Systems Metabolic Engineering
Published in
Methods in molecular biology, January 2013
DOI 10.1007/978-1-62703-299-5_9
Pubmed ID
Book ISBNs
978-1-62703-298-8, 978-1-62703-299-5
Authors

Carbonell P, Planson AG, Faulon JL, Carbonell, Pablo, Planson, Anne-Gaëlle, Faulon, Jean-Loup, Pablo Carbonell, Anne-Gaëlle Planson, Jean-Loup Faulon

Abstract

Tools from metabolic engineering and synthetic biology are synergistically used in order to develop high-performance cell factories. However, the number of successful applications has been limited due to the complexity of exploring efficiently the metabolic space for the discovery of candidate heterologous pathways. To address this challenge, retrosynthetic biology provides an integrated framework to formalize and rationalize the problem of importing biosynthetic pathways into a chassis organism using methods at the interface from bottom-up and top-down strategies. Here, we describe step by step the process of implementing a retrosynthetic framework for the design of heterologous biosynthetic pathways in a chassis organism. The method consists of the following steps: choosing the chassis and the target, selection of an in silico model for the chassis, definition of the metabolic space, pathway enumeration, gene selection, estimation of yields, toxicity prediction of pathway metabolites, definition of an objective function to select the best pathway candidates, and pathway implementation and verification.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 2%
France 1 2%
Thailand 1 2%
Unknown 59 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 27%
Researcher 11 18%
Student > Master 7 11%
Professor 4 6%
Student > Doctoral Student 4 6%
Other 10 16%
Unknown 9 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 32%
Biochemistry, Genetics and Molecular Biology 15 24%
Engineering 4 6%
Immunology and Microbiology 4 6%
Computer Science 3 5%
Other 5 8%
Unknown 11 18%