PDF电子书：Yeast Systems Biology

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8 z/ d: Z; ], A+ wYeast Systems Biology
8 Y, ^. z' L& P5 @7 DMethods and Protocols
% b% j: p6 m6 `: L- Z. {Edited by
+ j2 w# @1 S3 E9 X6 i, h4 `Juan I. Castrillo2 ~, F% T& ^* A8 ?

9 Z# `" u1 p% H* F$ w+ Z( |* h4 UContents
1 {/ ~' o3 ?% t# W9 m, {: h1 @) i  VPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v1 U0 S6 E# F* r" x
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi, H0 s/ s9 H, {: r- L
SECTION I: YEAST SYSTEMS BIOLOGY& `) B* E' H! B- L" j+ m5 Y/ t
1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3
$ c8 V$ a2 \7 l7 HJuan I. Castrillo and Stephen G. Oliver! i- _: Z) S  r* W& x
SECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE! }8 o6 o. a4 R9 m( V3 E
AND MOLECULAR STUDIES: ~6 c; }. i3 }* \* g0 g- m
2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
- s- N; C% f# J1 c- p3 G2 a6 qof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31
& S# c! B! h$ nEd Louis
3 g4 Y& p5 b2 G5 D! M# h3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41' s6 I7 K) H9 h# w
Oliver J. Rando$ `' u6 f4 `5 Q+ J5 V: G* `
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
# i* A: ^6 i( h4 ]0 |0 @Dustin E. Schones, Kairong Cui, and Suresh Cuddapah& T; X8 N& ?5 |# _
5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 730 r8 i. N/ t7 f. z+ `, s
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef
" u$ T; q" x( w; _9 r6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide4 l; k1 b. Z. r8 |. n: v
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4 v3 n, L5 `/ Z+ CHelen Neil and Alain Jacquier" a& H: w# G4 G4 x
7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
+ t: L8 W) g: E2 S) dTiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7 X3 r) z. ?; f) w7 \2 t5 D2 }Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz
& f. c( U7 {6 Y4 U1 x& Q' ^$ x8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
& f' D' o# Q2 n/ F) A$ s* ^; HKarl Waern, Ugrappa Nagalakshmi, and Michael Snyder1 w) I, w* ^5 F* T4 q2 [2 V" O
9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
4 O! ]) O2 V- B1 [6 L: _; j5 R0 j) O% LTraude H. Beilharz and Thomas Preiss; X- b0 T% P6 I0 O0 ^/ p; p3 F2 F
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149- {  W5 F/ \0 M' l/ H& b/ D
Johanna Rees and Kathryn Lilley6 m8 [, N/ o: n: M" |
11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179% U* D: S  y) F* D3 q4 s
Amy J. Claydon and Robert J. Beynon" W. k5 U7 V0 B- F8 ~( n
12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
3 q+ l) Y3 N" ^0 `* Y* K4 GBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,# t. d8 [4 n+ D6 b: d$ u
David E. Hill, and Marc Vidal* V5 L, l6 @4 e3 {; _+ ]4 P
13. Use of Proteome Arrays to Globally Identify Substrates for E3; ^4 X; z3 T+ |( h' z
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
1 t) r2 s$ f/ VAvinash Persaud and Daniela Rotin/ |# S0 g9 }! q* d) z! z5 p2 W: W
14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic4 E7 u# ~7 U  U9 e5 m: q
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225* @. I, U; _. B8 v$ K
Catherine L. Winder and Warwick B. Dunn
  x& |' _1 f1 m% T, }. E4 w, T: \15. The Automated Cell: Compound and Environment Screening System
  l) C9 y% j# [9 D4 g+ B; L6 Y" `(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239& e+ y- i) }0 R2 C
Michael Proctor, Malene L. Urbanus, Eula L. Fung,- o- t4 J' h* A" u* B; N
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,) r0 x0 k4 g2 ]% @
and Guri Giaever. h2 S7 G' Z+ b2 o& B4 X% d
16. Competition Experiments Coupled with High-Throughput Analyses for! d$ P' ?! i2 m
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
; l' i0 M/ ~! A8 \Daniela Delneri
. r0 _& `+ P+ p- f7 \, A17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
# q* G, R% [& `1 @2 NExamination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 2832 M' q9 R$ J4 t$ X
Brian D. Slaughter, Jay R. Unruh, and Rong Li
& U6 }& n0 j9 S6 J5 t% ]' f18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307! Y3 p% o/ r* V' ^! y% g- X
Yuehua Wei and X.F. Steven Zheng/ h  S9 t* S6 J- Q& E* ?
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES
9 E/ p1 P$ }7 H0 n2 d' `8 r8 JAND ANALYSES2 @: a6 m# \1 m3 J! _! J2 k5 R
19. Computational Yeast Systems Biology: A Case Study for the MAP
1 l; ]: c4 S* b# P& @' v9 {- sKinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3230 k" M7 e( p1 }; N6 \: ?  o
Edda Klipp4 g8 t: }6 A6 g, ^
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
$ G  |0 i/ V4 }) Q0 O" H& j# d# kAxel Kowald and Christoph Wierling- }. C0 m  J* `8 R
21. A Computational Method to Search for DNA Structural Motifs in. X  f$ F0 D/ h' f
Functional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 3679 \- [' O' E6 g; u9 z4 u& R
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius3 E* I" L7 K+ j9 m$ J% M7 v* y
22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381
  _" l! r+ c8 K: p7 VShoshana J. Wodak, Jim Vlasblom, and Shuye Pu# D7 }: k" J' }% |  |* T- {* ^
23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407
# `) n- t1 \- e: }# u+ H9 e1 p. LYitzhak Pilpel6 H- S2 L- ^7 }9 `8 Y, G$ w* T
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
+ H. g; W7 K! H1 Z' l* d3 T4 eProcesses in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427$ n+ s5 X+ h& l: `( ^. U1 @& K. c
Jean-Marc Schwartz and Claire Gaugain0 B5 L( T# _( h& a+ Y. v) F) Y
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
& J; E: Y$ a6 U9 T4 NIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr
4 M$ c& X" \' z6 ]7 aBhumiratana, and Jens Nielsen- L* p0 R. \6 M: l* z, N6 |. f
26. Representation, Simulation, and Hypothesis Generation in Graph
& s6 E5 z& u/ @! w8 I4 @& `and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465
$ C8 M6 T: ]" X. G4 sKen Whelan, Oliver Ray, and Ross D. King
! R) c9 S+ c. z27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483) ]9 S8 u5 K1 h7 L# O
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart
* ?9 f7 D" ]' D( S% T& m# R/ Q( N+ qSECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE- B! r) o5 E' p/ p9 e& |
AS A TOOL FOR MAMMALIAN STUDIES: H- G& L. }% q1 \7 R
28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian# I8 n( _% m' C8 m9 C1 N0 h
Gene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
$ p# _3 j  n. G# N3 b! Z0 FNianshu Zhang and Elizabeth Bilsland0 S3 k. j2 x9 E# {/ K; j
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5251 h% Y8 Q# Z6 q9 Q3 D/ d" i
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