Systems Metabolic Engineering : Methods and Protocols /

Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Alper, Hal S. (Editor )
Formato: eBook
Lenguaje:English
Publicado: Totowa, NJ : Humana Press : Imprint: Humana, 2013.
Edición:1st ed. 2013.
Colección:Methods in Molecular Biology,
Materias:
Tabla de Contenidos:
  • 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.