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Yeast Systems Biology

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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 23: Noise in Biological Systems: Pros, Cons, and Mechanisms of Control
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Chapter title
Noise in Biological Systems: Pros, Cons, and Mechanisms of Control
Chapter number 23
Book title
Yeast Systems Biology
Published in
Methods in molecular biology, January 2011
DOI 10.1007/978-1-61779-173-4_23
Pubmed ID
Book ISBNs
978-1-61779-172-7, 978-1-61779-173-4
Authors

Yitzhak Pilpel

Abstract

Genetic regulatory circuits are often regarded as precise machines that accurately determine the level of expression of each protein. Most experimental technologies used to measure gene expression levels are incapable of testing and challenging this notion, as they often measure levels averaged over entire populations of cells. Yet, when expression levels are measured at the single cell level of even genetically identical cells, substantial cell-to-cell variation (or "noise") may be observed. Sometimes different genes in a given genome may display different levels of noise; even the same gene, expressed under different environmental conditions, may display greater cell-to-cell variability in specific conditions and more tight control in other situations. While at first glance noise may seem to be an undesired property of biological networks, it might be beneficial in some cases. For instance, noise will increase functional heterogeneity in a population of microorganisms facing variable, often unpredictable, environmental changes, increasing the probability that some cells may survive the stress. In that respect, we can speculate that the population is implementing a risk distribution strategy, long before genetic heterogeneity could be acquired. Organisms may have evolved to regulate not only the averaged gene expression levels but also the extent of allowed deviations from such an average, setting it at the desired level for every gene under each specific condition. Here we review the evolving understanding of noise, its molecular underpinnings, and its effect on phenotype and fitness--when it can be detrimental, beneficial, or neutral and which regulatory tools eukaryotic cells may use to optimally control it.

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
United States 1 3%
Italy 1 3%
Switzerland 1 3%
Unknown 35 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 50%
Researcher 6 15%
Student > Master 5 13%
Professor > Associate Professor 4 10%
Student > Postgraduate 2 5%
Other 2 5%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 40%
Biochemistry, Genetics and Molecular Biology 10 25%
Engineering 5 13%
Computer Science 2 5%
Physics and Astronomy 2 5%
Other 4 10%
Unknown 1 3%

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 25 September 2014.
All research outputs
#3,401,815
of 4,296,759 outputs
Outputs from Methods in molecular biology
#1,747
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Outputs of similar age
#90,391
of 115,073 outputs
Outputs of similar age from Methods in molecular biology
#40
of 59 outputs
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We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.