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Yeast Systems Biology

Overview of attention for book
Cover of 'Yeast Systems Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Yeast systems biology: the challenge of eukaryotic complexity.
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    Chapter 2 Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State of the Art Through Comparative Genomics
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    Chapter 3 Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast
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    Chapter 4 Genome-Wide Approaches to Studying Yeast Chromatin Modifications
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    Chapter 5 Absolute and relative quantification of mRNA expression (transcript analysis).
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    Chapter 6 Enrichment of Unstable Non-coding RNAs and Their Genome-Wide Identification
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    Chapter 7 Genome-Wide Transcriptome Analysis in Yeast Using High-Density Tiling Arrays
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    Chapter 8 RNA Sequencing
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    Chapter 9 Polyadenylation State Microarray (PASTA) Analysis
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    Chapter 10 Enabling technologies for yeast proteome analysis.
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    Chapter 11 Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC
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    Chapter 12 Protein-protein interactions and networks: forward and reverse edgetics.
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    Chapter 13 Use of Proteome Arrays to Globally Identify Substrates for E3 Ubiquitin Ligases
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    Chapter 14 Fit-for-Purpose Quenching and Extraction Protocols for Metabolic Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms
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    Chapter 15 The Automated Cell: Compound and Environment Screening System (ACCESS) for Chemogenomic Screening
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    Chapter 16 Competition experiments coupled with high-throughput analyses for functional genomics studies in yeast.
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    Chapter 17 Fluorescence Fluctuation Spectroscopy and Imaging Methods for Examination of Dynamic Protein Interactions in Yeast
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    Chapter 18 Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast
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    Chapter 19 Computational Yeast Systems Biology: A Case Study for the MAP Kinase Cascade
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    Chapter 20 Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data
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    Chapter 21 A Computational Method to Search for DNA Structural Motifs in Functional Genomic Elements
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    Chapter 22 High-Throughput Analyses and Curation of Protein Interactions in Yeast
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    Chapter 23 Noise in Biological Systems: Pros, Cons, and Mechanisms of Control
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    Chapter 24 Genome-scale integrative data analysis and modeling of dynamic processes in yeast.
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    Chapter 25 Genome-Scale Metabolic Models of Saccharomyces cerevisiae
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    Chapter 26 Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks
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    Chapter 27 Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology
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    Chapter 28 Contributions of Saccharomyces cerevisiae to understanding mammalian gene function and therapy.
Attention for Chapter 11: Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC
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Chapter title
Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC
Chapter number 11
Book title
Yeast Systems Biology
Published in
Methods in molecular biology, July 2011
DOI 10.1007/978-1-61779-173-4_11
Pubmed ID
Book ISBNs
978-1-61779-172-7, 978-1-61779-173-4
Authors

Claydon, Amy J., Beynon, Robert J., Amy J. Claydon, Robert J. Beynon

Abstract

Early achievements in proteomics were qualitative, typified by the identification of very small quantities of proteins. However, as the subject has developed, there has been a pressure to develop approaches to define the amounts of each protein--whether in a relative or an absolute sense. A further dimension to quantitative proteomics embeds the behavior of each protein in terms of its turnover. Virtually every protein in the cell is in a dynamic state, subject to continuous synthesis and degradation, the relative rates of which control the expansion or the contraction of the protein pool, and the absolute values of which dictate the temporal responsiveness of the protein pool. Strategies must therefore be developed to assess the turnover of individual proteins in the proteome. Because a protein can be turning over rapidly even when the protein pool is in steady state, the only acceptable approach to measure turnover is to use metabolic labels that are incorporated or lost from the protein pool as it is replaced. Using metabolic labeling on a proteome-wide scale in turn requires metabolic labels that contain stable isotopes, the incorporation or loss of which can be assessed by mass spectrometry. A typical turnover experiment is complex. The choice of metabolic label is dictated by several factors, including abundance in the proteome, metabolic redistribution of the label in the precursor pool, and the downstream mass spectrometric analytical protocols. Key issues include the need to control and understand the relative isotope abundance of the precursor, the optimization of label flux into and out of the protein pool, and a sampling strategy that ensures the coverage of the greatest range of turnover rates. Finally, the informatics approaches to data analysis will not be as straightforward as in other areas of proteomics. In this chapter, we will discuss the principles and practice of workflow development for turnover analysis, exemplified by the development of methodologies for turnover analysis in the model eukaryote Saccharomyces cerevisiae.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
United States 1 4%
Canada 1 4%
Unknown 23 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 35%
Student > Ph. D. Student 7 27%
Student > Bachelor 2 8%
Student > Doctoral Student 1 4%
Student > Master 1 4%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 69%
Biochemistry, Genetics and Molecular Biology 5 19%
Unknown 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 May 2012.
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#5,651,928
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#2,710
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#59,488
of 91,622 outputs
Outputs of similar age from Methods in molecular biology
#28
of 47 outputs
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