PDF电子书：Yeast Systems Biology

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Yeast Systems Biology: O8 y3 |: W- ^2 }" Q$ p; p, o
Methods and Protocols
4 X, I( j( V1 h/ zEdited by$ k! X- y1 y  \1 q. g7 u
Juan I. Castrillo7 h' v' E  c% |3 k$ S

) s( G" u1 W! E# N( R8 z# }) l3 n1 Y% [Contents
8 j3 Y  g! N4 _* ^Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
2 R2 i. A" J& {5 w. j, f+ V+ bContributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi9 o" S* k) |9 O1 z2 l4 X
SECTION I: YEAST SYSTEMS BIOLOGY
2 H7 m& O  _3 \$ j6 M7 |/ k" i1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3
& D7 Y5 K' r4 n- bJuan I. Castrillo and Stephen G. Oliver1 ^9 m* K) @. J( n8 j8 Y
SECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE# O, d, f- D3 l' ]+ i+ a
AND MOLECULAR STUDIES* w6 c  ~2 k9 r8 O. A; a3 C0 a
2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
9 L& n3 ^  O; y. O* Bof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 310 t9 V  m1 i7 z
Ed Louis+ M  r; t; o; s( d7 u
3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 413 r' f! e& X' L- k: T$ j- T
Oliver J. Rando: Z$ k6 p3 X" u1 s
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
0 n  B9 @- f" w8 u: _Dustin E. Schones, Kairong Cui, and Suresh Cuddapah
; l. f4 o3 p2 a/ }. O1 ~3 b/ f& o5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 731 ~" @9 q# X6 x5 C5 R; W! q6 V% e
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef3 J9 K3 i% \0 o5 n7 [, f3 g/ f; F) n' f
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide( R) ^; x9 X* e! g5 w
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
& d2 ~( q0 }7 M& b1 aHelen Neil and Alain Jacquier% d0 c, [( ?- K" B# @1 }7 \
7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
  g+ d$ z2 j: R- d3 D6 N3 H  lTiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
; o3 e2 D3 Z& O& B* ~Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz/ Y0 E0 |. h& F+ `
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125- M. Y7 ]! ?' {: T3 s( @
Karl Waern, Ugrappa Nagalakshmi, and Michael Snyder
* r! J, N+ k) v1 M$ J" ?' @0 A9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
% c  q+ `+ Y/ e% F4 I  cTraude H. Beilharz and Thomas Preiss
1 {1 y/ u5 Z+ J( C  `1 j" V2 P1 w4 {10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 1490 t2 F: @7 K! q! [* O" [  @1 F5 ]
Johanna Rees and Kathryn Lilley
# r( N5 l  y, d# v5 _$ h11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179% \! X1 a. T" s1 e/ g7 e  {
Amy J. Claydon and Robert J. Beynon; B$ u$ U( a2 K7 d. k6 V3 D1 B1 S! [) s
12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
0 O% v0 P7 y1 X0 i% E2 FBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick," O, f2 j% [' S
David E. Hill, and Marc Vidal# f5 p- ^0 s9 _* G1 Z3 T
13. Use of Proteome Arrays to Globally Identify Substrates for E3
* y0 P( H  Z7 Z7 T: JUbiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
8 P8 ~7 D1 T( s2 ^6 U' }3 o# WAvinash Persaud and Daniela Rotin  a  r  s7 C$ h, v2 b+ Q, N  f
14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic5 U; v1 O& ~8 U- i7 X
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225
* S+ m$ T' V* ^/ @* ^Catherine L. Winder and Warwick B. Dunn
$ O' [1 Y# D" f" C15. The Automated Cell: Compound and Environment Screening System- Y$ @2 C; Q# V1 B7 m& Y8 H  s5 _
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239
8 v+ ]1 k  X! U* v5 j& _' @Michael Proctor, Malene L. Urbanus, Eula L. Fung,
. G9 @7 _, Q* E; b0 P, B: V* }Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,* q# ]( M5 X" w* t0 D" E
and Guri Giaever( R* k/ x- T$ Q, v2 d- C
16. Competition Experiments Coupled with High-Throughput Analyses for- R* D+ a3 s: ?6 x" Y1 p' q
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
7 c/ J5 K8 ?7 f) l* Y+ lDaniela Delneri
$ \" }* R3 V, V4 R9 k: k+ o( B17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
4 ~% J6 c' I! m2 z2 s" T: _3 ~Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283
( u3 a, i, ]+ _* c6 \9 V3 ^Brian D. Slaughter, Jay R. Unruh, and Rong Li4 _; \; e  w1 v+ a, L8 B1 l; j8 H
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307
$ Q5 W9 X- @# S# [) w$ rYuehua Wei and X.F. Steven Zheng
4 V9 w/ a5 U7 ySECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES5 }; o6 t+ l5 H$ [: R; [7 `& Q# f3 V& M+ L
AND ANALYSES
+ h0 B: p  F# U+ H  \19. Computational Yeast Systems Biology: A Case Study for the MAP) V9 s0 x2 {0 W2 W
Kinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
. {5 V, q7 \& ?3 REdda Klipp' w: @9 }' I) R( `6 A; b1 I$ f
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 3456 p4 O: A& h1 s. X$ i# }3 {6 m
Axel Kowald and Christoph Wierling
, @$ v8 l4 m1 S, o& e  n21. A Computational Method to Search for DNA Structural Motifs in
& g8 i- `- r; G3 C( E9 hFunctional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367
/ V# x4 f/ M9 ^) TStephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius) |  `: ]# N  q! F) T" P
22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 3815 g/ B4 t+ `* }$ z3 N
Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu
: ~1 f; F! m5 q- ~23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407( q" u6 u1 Z% K. ?  C# O
Yitzhak Pilpel6 ~1 a' f3 L' f
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
% i/ `& D  R4 NProcesses in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
: j1 R1 c7 w5 k; f" zJean-Marc Schwartz and Claire Gaugain. s3 C  d: n- Y7 U" U
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
( G- f7 T7 {+ L( N" w2 V; V) yIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr
: y% U( X: o" hBhumiratana, and Jens Nielsen: a1 L2 N0 F3 F( i2 g" T! d% D
26. Representation, Simulation, and Hypothesis Generation in Graph: J3 F( ~8 H' `" P- T- d
and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465
/ S# D+ o/ F# xKen Whelan, Oliver Ray, and Ross D. King3 w! v% f/ T7 T( q/ |  Q6 j
27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483
) z. l( [' Z! J- D1 X4 F: KBalázs Papp, Balázs Szappanos, and Richard A. Notebaart5 a- T) Q4 H: ^% V( q& G
SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE
) V! ^" {' y  C+ D: `" e7 H9 lAS A TOOL FOR MAMMALIAN STUDIES
; I8 M6 V+ A) [) _4 ]# a) X" k% [' l7 I28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
, w8 o1 ~: O' {+ qGene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5018 I5 y. `0 L/ L
Nianshu Zhang and Elizabeth Bilsland
; c, b4 ]: ]8 m- d0 N3 V% BSubject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5259 y! c5 n: p: Q  ?2 p* C6 L3 l; g$ ]6 A
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