PDF电子书：Yeast Systems Biology

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Yeast Systems Biology
2 @- a  V3 w% {8 E: yMethods and Protocols' O4 _1 Q5 M$ C
Edited by- I/ L2 s2 }* ^' I: H
Juan I. Castrillo7 N: s8 z! l8 z/ }1 `1 J- x: N
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Contents* E+ I9 h0 `$ \* e' I) `$ o9 _) n" _: r
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
( T0 U; q) N% e+ b9 P# Z+ XContributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
  W# m) B' |- |! vSECTION I: YEAST SYSTEMS BIOLOGY0 [( F+ Z, ]( _0 P
1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3
6 C7 |" S6 ~$ R3 A8 zJuan I. Castrillo and Stephen G. Oliver
# E$ a/ x/ |; B, [5 cSECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE( Z1 k4 }1 }5 U1 _0 x) k. D
AND MOLECULAR STUDIES: Q  j, R# `: \( x# J; a3 t
2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State- ]0 V' x! Q' j( |+ I, L
of the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31
, A! `7 V) V3 i3 t( J0 uEd Louis" ]1 k  J, ~# @- \" r# H/ }
3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41% R+ r  [( W- g3 f0 C6 j2 N& G. S
Oliver J. Rando
1 P$ c% U7 i) o( _7 G$ i. S7 P4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
1 w" r0 r* _3 P3 K3 K( |Dustin E. Schones, Kairong Cui, and Suresh Cuddapah) M  u# c: S* ?0 p) Y4 b5 y; X% k
5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73) k6 N$ I/ t6 u% A" I
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef4 B! \7 f2 `" z1 i/ q
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide* Z% e4 T3 c! Z" k+ O  b
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 877 g4 {* y2 w. k- Y7 i& {9 L! }
Helen Neil and Alain Jacquier' ?( @0 W% G, X2 c( X% [
7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
# l! x5 E5 L, ~; f1 qTiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107" E+ b5 r4 s) j8 P& T# G
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz
( g" U  W5 D9 O2 C  r8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125& Z$ D- q3 P: s
Karl Waern, Ugrappa Nagalakshmi, and Michael Snyder
9 e% o8 v( `4 \' Y) L9 s9 P9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
; y& G" y, @) A: G" S4 jTraude H. Beilharz and Thomas Preiss& ?! x; Z! b4 L" v( J$ s
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149- j, ^9 J! Y& L$ D1 w2 {
Johanna Rees and Kathryn Lilley% m3 {' n4 g  U4 N! o, t
11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179
( `6 k% l  [# o& I* c2 QAmy J. Claydon and Robert J. Beynon
) M' r+ n- h1 T5 u' h8 `2 Q2 _. a, ?12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197" ~5 ^6 D* Z) y# }& W3 f
Benoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
9 i% ~) D+ B1 u6 h. @  [4 |David E. Hill, and Marc Vidal
( `' K  k" ?" w7 H! c$ W% O9 I! L13. Use of Proteome Arrays to Globally Identify Substrates for E3
0 J* x+ W0 O0 b- U3 ?/ H1 l' OUbiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
% Z# Q0 ~! t( o% k2 l( v. CAvinash Persaud and Daniela Rotin( J5 p# q7 a! I2 C! T
14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic& C7 u+ ?2 H; k1 I# j9 G; W
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225
8 i1 [. {' X! n  Z3 ^- a1 WCatherine L. Winder and Warwick B. Dunn) d/ Z# Q; C& K, d
15. The Automated Cell: Compound and Environment Screening System, j3 B; x: T. U8 i& Z
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 2392 b6 `1 x9 {0 |: m8 `* y5 s
Michael Proctor, Malene L. Urbanus, Eula L. Fung,0 `9 k9 a# s" v  ]1 K
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,$ ]! B8 n2 N$ I' j  O0 b
and Guri Giaever2 z( y: H& ^% r8 J
16. Competition Experiments Coupled with High-Throughput Analyses for  @" P  T, U, R5 D
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 2715 K( }; Q3 q2 m0 b" t
Daniela Delneri) X$ X+ E0 f) j
17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for) r, {1 o: k5 X$ c
Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283* P( v$ x1 c4 G$ O
Brian D. Slaughter, Jay R. Unruh, and Rong Li3 B! H& L0 i5 t. R1 s3 Q0 @) \: j
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307/ F* [6 F7 U5 X$ u
Yuehua Wei and X.F. Steven Zheng8 K' E+ |" ?% Y' |3 e: ^
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES' ?8 b( y/ y; h7 |( \1 s+ k" H
AND ANALYSES
% [0 O/ w: y8 \! \' n19. Computational Yeast Systems Biology: A Case Study for the MAP
/ g! e4 D# Q9 v: d9 [2 g, XKinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3230 w6 Z4 t. Y9 C% L
Edda Klipp- s8 ]; f/ }% b- o
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345% I# b( u4 \. a- X% j2 d9 f6 U
Axel Kowald and Christoph Wierling9 T" v5 w9 a, J+ R, h, x
21. A Computational Method to Search for DNA Structural Motifs in) u5 e  q' N' u% ]& N, p3 p
Functional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367
: N! g6 c1 A' A( E( V4 K$ I- wStephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius% F6 y! f/ q$ W  {
22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381! \+ r6 Q2 C& b, @' w) H- ~
Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu
8 c2 _. M9 `$ `) X$ i% t23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407* M/ M4 j$ b, a: }' |7 {- V9 C
Yitzhak Pilpel1 c1 U( o+ u+ _$ L
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic' [+ @4 ^  [8 ^
Processes in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
/ u- q) Y6 V, b* K% |" QJean-Marc Schwartz and Claire Gaugain
1 p# l4 n& G) L& W5 c4 D25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445$ I+ D: T7 Z, d' X
Intawat Nookaew, Roberto Olivares-Hernández, Sakarindr
) j3 e* t9 v( w9 n( O$ GBhumiratana, and Jens Nielsen
8 h8 p- g2 a5 ]" k26. Representation, Simulation, and Hypothesis Generation in Graph
* R) ^1 ^6 n* U. ], y- B! @and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465
8 h% T8 V% D* xKen Whelan, Oliver Ray, and Ross D. King
; a$ G- B- z2 P0 H* w( `27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 4832 R* }! m: U/ Y
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart/ K: @6 d4 f' G  [, f4 l
SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE+ X7 |: m. h+ o* y. z2 {  f
AS A TOOL FOR MAMMALIAN STUDIES
% l5 P5 ~" ~' t. ~0 Y) y) f" ~28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian; o% K! c* i5 j2 l9 P
Gene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501' `4 T& N- |3 T4 s8 z
Nianshu Zhang and Elizabeth Bilsland8 c3 W6 \! p: I2 C$ U! Y- F
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525
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