PDF电子书：Yeast Systems Biology

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! I' d$ O# r0 {7 |( i. CYeast Systems Biology
2 I! W* m  t+ a4 jMethods and Protocols
5 s. E% w! L7 {$ wEdited by
4 o* H8 _* g- {5 e1 y5 e: hJuan I. Castrillo
  w" P+ `7 U# c; W( s# ]4 T8 k9 e. _" ?4 e) J
Contents) O4 A6 j/ c  E% `, x4 E
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
& L  i0 X0 w5 V8 M3 UContributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
" t8 Y2 I8 L! j, q9 mSECTION I: YEAST SYSTEMS BIOLOGY
5 q8 G% h( q% o( a. A1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3
- t, {# f8 O" V$ d% `* }% o0 c. lJuan I. Castrillo and Stephen G. Oliver2 ~6 x! b6 q0 f$ Y
SECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE
- G+ @0 V* d, R: s4 QAND MOLECULAR STUDIES" |" h8 [7 z& H9 F3 \7 T
2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
! o4 S4 v3 L! u+ g" L! B$ C6 C" P6 c( Qof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31
7 }& g/ ^! n7 t3 PEd Louis
  D& Q" M: w2 ?+ M! P! Y$ G6 P3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 418 k$ V; S% W7 r9 b
Oliver J. Rando$ _8 U! z9 `! {4 H
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 618 q1 f& {, b# b) ~3 ^4 v
Dustin E. Schones, Kairong Cui, and Suresh Cuddapah' o6 p( p$ U' m$ U9 J( b6 l
5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73/ N  b/ J% G* I
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef! X1 g9 }. s7 x$ U1 v% w
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide
* [. F' i& }8 o7 R( ]+ N1 Q# eIdentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87. f6 o3 u/ W9 @. q
Helen Neil and Alain Jacquier
# P. q% f. V0 T4 m$ |; r7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
: _  L5 H) u) MTiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107% g4 V/ p  h, Y8 F" ]6 Z, |2 u6 O
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz
$ a6 l* c) N! p, ]$ S4 L/ C5 ?4 b8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
+ p; a# k+ B1 K; hKarl Waern, Ugrappa Nagalakshmi, and Michael Snyder
5 l' }3 g2 @. q9 J& T* G, G7 q9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
! Y7 i) M, ^0 V3 v8 q6 x9 `/ RTraude H. Beilharz and Thomas Preiss
" X) a  G4 I9 l0 D10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149
+ @5 }* S; @# L4 z8 o9 x1 h* cJohanna Rees and Kathryn Lilley
: G' p$ H. B$ @: n11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179" s- Z. N, s( h. p5 b% E
Amy J. Claydon and Robert J. Beynon
* e  s& t9 g4 W; ?/ x  q12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
, s% _  B  V$ xBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
5 o  C1 P) S, h5 L. MDavid E. Hill, and Marc Vidal
- p# O9 `. M: V* e13. Use of Proteome Arrays to Globally Identify Substrates for E33 P6 l0 J2 X8 N2 f3 n  N
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2155 N' p, Y8 p% y  ^4 {! U6 v
Avinash Persaud and Daniela Rotin5 f6 r+ W) _, n( \4 c
14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic: n5 _: M- ?) S- b' q4 ?
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225
2 O4 \# M' w% E; UCatherine L. Winder and Warwick B. Dunn, {& s. j% T' L; F" y0 t+ H: M
15. The Automated Cell: Compound and Environment Screening System* x3 l( j8 B1 n" T
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239
0 h. _. o. Q# P9 [Michael Proctor, Malene L. Urbanus, Eula L. Fung,- o! w8 k! n) |* h7 m
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,
" ^3 Q& M6 ^2 X3 k0 O6 X! oand Guri Giaever+ u4 l6 e9 n& a2 ^$ Y
16. Competition Experiments Coupled with High-Throughput Analyses for- j  R. o- i! P! J2 S1 G
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
! C3 m# ^6 h1 }  @, _3 FDaniela Delneri
0 t1 s* C& |' W' f; u17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for. o5 R7 _1 l( L7 L  Y* o8 j
Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283
3 N0 j( v& x& _2 P8 v  vBrian D. Slaughter, Jay R. Unruh, and Rong Li
' e7 S6 F% q+ ~2 c18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 3071 X$ T3 z# i: ?" b3 M5 T9 ]
Yuehua Wei and X.F. Steven Zheng" C$ ~% H0 L# t) f5 U1 {8 S
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES
( _4 U0 _7 m; D! ^& vAND ANALYSES
# I# P/ R7 B, ]! X& u19. Computational Yeast Systems Biology: A Case Study for the MAP
1 K. l' R% N$ \" _# i- f3 K! ^6 |Kinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323+ W6 K: g8 p8 R& Y
Edda Klipp5 m" Y" X9 O1 o
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345$ Q( ~5 H* d5 q* G9 [* t( [" ^
Axel Kowald and Christoph Wierling
% z7 d" [( a7 s  v8 C7 D3 @8 R21. A Computational Method to Search for DNA Structural Motifs in% B3 v& S) k2 Q1 n
Functional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367. k7 M4 M- ?' S0 H
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius
. D* N" e. @9 L22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381
" h+ ?7 y) {: c7 E. v+ aShoshana J. Wodak, Jim Vlasblom, and Shuye Pu& j* D( |( o0 w/ l! H# T1 Y
23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407
& }4 @4 z, V' \7 X& X; AYitzhak Pilpel
# M9 W8 T- H! n24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
1 y2 l0 j+ F0 y8 L# ~6 Z% ^! {Processes in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4271 W, G0 F; v' F! I& r$ j
Jean-Marc Schwartz and Claire Gaugain8 B+ i1 v4 }  }! k. ^
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
0 X, \, R! V! QIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr3 U! b  d3 j8 H. G* Z) }
Bhumiratana, and Jens Nielsen
6 ~- H, n( D- a26. Representation, Simulation, and Hypothesis Generation in Graph: m  J1 q0 q! f; ~0 H
and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465: k. G% |8 }+ t3 _: u; P
Ken Whelan, Oliver Ray, and Ross D. King( M) T& q* v9 E
27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483! ~  p6 d' Y6 p, n1 \% c2 Z; |+ k
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart% u& l. \, `3 ]/ P0 i& j  T  g
SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE8 b+ P! x" I. o# o9 G, k5 V5 f) H
AS A TOOL FOR MAMMALIAN STUDIES* \, O. I. F! j
28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
0 |" T8 }1 @5 \( v3 r: [9 D0 F. G* L" DGene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5013 g7 [% N2 E. D, g
Nianshu Zhang and Elizabeth Bilsland4 z$ V( [1 C: K1 F, q
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525  X9 X- _! s- \2 z( Q, B& l7 R
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