Systems Metabolic Engineering

Methods and Protocols

Inbunden, Engelska, 2013

1 389 kr

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This book describes the methodologies and approaches in systems metabolic engineering and provides a step-by-step guide for their implementation. Includes readily reproducible laboratory protocols and tips on troubleshooting.

Produktinformation

Utgivningsdatum2013-02-16
Mått178 x 254 x 31 mm
Vikt1 107 g
FormatInbunden
SpråkEngelska
SerieMethods in Molecular Biology
Antal sidor474
Upplaga2013
FörlagHumana Press Inc.
ISBN9781627032988

Tillhör följande kategorier

Genome-Scale Model Management and Comparison.- Automated Genome Annotation and Metabolic Model Reconstruction in the SEED and Model SEED.- Metabolic Model Refinement Using Phenotypic Microarray Data.- Linking Genome-Scale Metabolic Modeling and Genome Annotation.- Resolving Cell Composition through Simple Measurements, Genome-Scale Modeling, and a Genetic Algorithm.- A Guide to Integrating Transcriptional Regulatory and Metabolic Networks Using PROM (Probabilistic Regulation of Metabolism).- Kinetic Modeling of Metabolic Pathways: Application to Serine Biosynthesis.- Computational Tools for Guided Discovery and Engineering of Metabolic Pathways.- Retrosynthetic Design of Heterologous Pathways.- Customized Optimization of Metabolic Pathways by Combinatorial Transcriptional Engineering.- Adaptive Laboratory Evolution for Strain Engineering.- Trackable Multiplex Recombineering for Gene-Trait Mapping in E. coli.- Identification of Mutations in Evolved Bacterial Genomes.- Discovery of Post-Transcriptional Regulatory RNAs Using Next Generation Sequencing Technologies.- 13C-based Metabolic Flux Analysis: Fundamentals and Practice.- Nuclear Magnetic Resonance Methods for Metabolic Fluxomics.- Using Multiple Tracers for 13C Metabolic Flux Analysis.- Isotopically Nonstationary 13C Metabolic Flux Analysis.- Sample Preparation and Biostatistics for Integrated Genomics Approaches.- Targeted Metabolic Engineering Guided by Computational Analysis of Single Nucleotide Polymorphisms (SNPs).- Linking RNA Measurements and Proteomics with Genome-Scale Models.- Comparative Transcriptome Analysis for Metabolic Engineering.- Merging Multiple Omics Datasets In Silico: Statistical Analyses and Data Interpretation.