PDF电子书：Yeast Systems Biology

zsg

& w2 G/ h- f+ X, H  C* O* e3 z. m5 W# N' A
Yeast Systems Biology% Y+ [; v( w# C( d2 Q2 i
Methods and Protocols
* l! G0 u* p) N3 ]1 KEdited by
2 b3 ?" w  M0 n8 [9 T/ [* X4 kJuan I. Castrillo8 r/ Z7 ?6 Q' A7 X/ Z; ?

: z8 \1 N9 c: o4 ~7 x. gContents
2 _: P6 H( k3 u' K! dPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v2 v! v  r. b' L3 f' X
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi: O# Y' @& V2 T8 b1 R
SECTION I: YEAST SYSTEMS BIOLOGY* I- [# P. b9 s% j, X0 g
1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3$ y3 U6 r# S  P
Juan I. Castrillo and Stephen G. Oliver
% J7 {9 n( A, `4 Z5 }1 }. BSECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE
2 x6 o8 H, x  s% i4 Y1 _6 k' qAND MOLECULAR STUDIES
. i4 Y, e( u9 x9 j2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State$ a4 [* Z" p+ q# u  o
of the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31
4 o* y& F5 H+ g* R6 e+ uEd Louis
- A( U% C' n+ _' T/ [3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41" A; H1 f$ T$ L5 k" H/ r$ h- w4 J
Oliver J. Rando
) U$ u/ L" A* o4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
: G) o1 I0 ?$ t) l4 X- vDustin E. Schones, Kairong Cui, and Suresh Cuddapah
, k. j4 t8 O$ M5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73; U$ z1 X7 u3 ~5 h, l
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef$ S# u- e/ X! I, \  p  g) V# s& y
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide! t' C# O; m/ m& |% n) f
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874 z+ M, O% J0 S0 r4 l- Z( D0 {- }
Helen Neil and Alain Jacquier
6 q; o2 S% g0 A0 r* n' [3 ?0 F7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density2 j, B0 n  k4 x' e) a& X
Tiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1079 U- ?! ^; J; O/ l  g# c3 }
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz* v% w! \* E  B! H
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125# K5 U( G8 U! p+ B" J9 u: j
Karl Waern, Ugrappa Nagalakshmi, and Michael Snyder
, g) N* C% F: ^. L5 f, _" m6 g! y9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
5 T$ S5 C) e0 A6 T* LTraude H. Beilharz and Thomas Preiss6 N: `* F& W% }# R
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149/ k* z" w. b8 z- K+ m
Johanna Rees and Kathryn Lilley
6 n% E2 n7 C" b) i11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 1790 J5 o3 L5 p$ R: [
Amy J. Claydon and Robert J. Beynon( o/ p" i( M8 M$ ~& d' |5 A/ _* g6 r; p
12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
& \) C9 D: `2 A# q* u2 D% W7 t1 jBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,# h: u- y' {4 o
David E. Hill, and Marc Vidal2 p* u. Z5 p! W# ~; C
13. Use of Proteome Arrays to Globally Identify Substrates for E3
0 n) J- p( z! f1 q4 {Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
1 p; T# d1 r0 ~4 V' FAvinash Persaud and Daniela Rotin: p  T+ @3 a& d; C, p6 Y$ f
14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic
: O$ t1 l7 q* r; z: Q% HProfiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225' R( W& M2 o* t3 n
Catherine L. Winder and Warwick B. Dunn. b8 b+ M: e  I7 j" d
15. The Automated Cell: Compound and Environment Screening System
; J7 m3 @  u( C% L1 _0 P* g/ X(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 2397 E0 e  r, {- M! ?
Michael Proctor, Malene L. Urbanus, Eula L. Fung,- l6 s8 y4 L+ q- ]8 S# o% p; J; f
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,! b3 x1 o0 U. ~0 Z! e! t0 U  d7 x6 S
and Guri Giaever
/ @  b. d( @" p+ j0 G! p' R$ }16. Competition Experiments Coupled with High-Throughput Analyses for. q6 L1 g- |9 K% ]9 ?, c
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
1 G, y5 T5 O/ _4 ^7 b7 K# n2 I; n! A8 WDaniela Delneri
8 |/ f3 ]" A; y7 ]  d17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
* }) U, u" a+ U! P' PExamination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283- c  p9 |- ~' ?: J
Brian D. Slaughter, Jay R. Unruh, and Rong Li* F/ t8 ^( Y# F2 F3 b( G4 D
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307
- ~8 n; F4 i2 t$ e$ u: q' rYuehua Wei and X.F. Steven Zheng8 i2 Q; W1 Q% b+ z1 p
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES; z1 g, |+ r( q0 T- X  w- t6 ]9 U. o6 d
AND ANALYSES
/ i4 U3 a, F* @# f! o19. Computational Yeast Systems Biology: A Case Study for the MAP0 ~( O* e2 M+ `( i* ]5 O% U/ A
Kinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
+ {+ f. t5 G6 i# \$ P" KEdda Klipp
0 w8 k3 E% z. v1 P20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
& p. ?7 [! P* @1 z$ D* G& TAxel Kowald and Christoph Wierling
- n! H& W  |3 H4 X21. A Computational Method to Search for DNA Structural Motifs in
& }% }0 Q% Z+ S7 r2 C+ D( HFunctional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 3676 s" z* x/ n" e: J3 a
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius. Q! `5 v! X6 F0 T
22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381" j1 I3 N0 ]1 A& u; x' y
Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu* C4 b9 d: B# Y$ K7 u, o# I/ Y  W
23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407
5 E/ H" m' Y) Z  L5 WYitzhak Pilpel% @9 _+ w% f; T9 c6 C+ P" x
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic- B$ e$ a$ D& J7 t. [
Processes in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4272 y4 R% k" P0 e4 r# _$ D$ {) z
Jean-Marc Schwartz and Claire Gaugain
+ \. ~9 c8 w" w25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445# t$ ~4 F; I5 \4 U: K3 N
Intawat Nookaew, Roberto Olivares-Hernández, Sakarindr
0 Y; e# j- H; z9 J" wBhumiratana, and Jens Nielsen
3 L: F( B7 g# F26. Representation, Simulation, and Hypothesis Generation in Graph
8 t4 [; L( U* |2 ^' P5 l/ a& Xand Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 4658 Z, c( x* l& A: k
Ken Whelan, Oliver Ray, and Ross D. King
2 m5 V" x2 [+ I27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483
7 N$ w6 P: H4 H% {" J: PBalázs Papp, Balázs Szappanos, and Richard A. Notebaart7 ~& y, V. F! A9 [9 k. E
SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE
( x! T: c7 Q+ M6 @. tAS A TOOL FOR MAMMALIAN STUDIES; H6 m7 X) ?7 D6 X0 E8 Q- p
28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
$ K$ F: x- f2 P+ j: dGene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
+ I7 [) O- r# p6 vNianshu Zhang and Elizabeth Bilsland5 H6 ?) n& x7 \4 H$ j
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525
& C) X, Z( M) c' t* {0 p[hide][/hide]