PDF电子书：Yeast Systems Biology

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/ }$ g  J; H( u) j' x. y! QYeast Systems Biology5 i% M/ h% T+ |4 \
Methods and Protocols
; z. I0 e6 P  e, N' b, N5 JEdited by6 ^7 N+ u1 A2 ~
Juan I. Castrillo
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Contents5 R/ s+ Z7 n4 v$ U! H
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v) s( {* L! }# _' h* s# R
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
, F1 S( P! w) TSECTION I: YEAST SYSTEMS BIOLOGY
" F1 _/ s/ F& X* n7 }. t2 e1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3
* e. N+ ~& `. F$ b/ D8 hJuan I. Castrillo and Stephen G. Oliver5 C8 f8 y: L+ P
SECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE
6 D- J4 s6 ?0 w0 E: ~! }4 \AND MOLECULAR STUDIES
+ `- ]$ S0 Z( P5 F9 H2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
$ [5 Z1 a; b6 m' m4 D3 E8 N7 \of the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 314 G7 ?6 T$ a& `% o7 V
Ed Louis
- d- Y% T! j. U3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41$ h9 ^/ s+ p. N% U+ m' a% ?
Oliver J. Rando3 Y7 K5 F4 _8 I0 I
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61, Q8 d' c& k0 |/ V5 n% I8 x
Dustin E. Schones, Kairong Cui, and Suresh Cuddapah
, p# z9 x9 A: u5 ]/ J! U: g5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73
$ r5 `' v, Q) zAndrew Hayes, Bharat M. Rash, and Leo A.H. Zeef
6 R0 P+ r) B6 S6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide2 s- D5 G! I% u/ x
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7 t9 ~+ }7 {# {' c8 E6 E3 ]& wHelen Neil and Alain Jacquier
7 t" |0 n% E9 ?4 E) H7 D7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
& \/ [! w; c3 S# X, @5 nTiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5 R: w1 W% \0 o6 u( u5 aLior David, Sandra Clauder-Münster, and Lars M. Steinmetz9 c1 y, Z/ `" k4 z4 j7 j: u
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6 C- E9 F# N  p& a  N3 hKarl Waern, Ugrappa Nagalakshmi, and Michael Snyder
5 ?5 M' y; M1 d0 q9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
' V# B0 j" @6 |" c' Z1 dTraude H. Beilharz and Thomas Preiss
4 n7 `$ [" t1 E; M10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 1491 I4 W! p0 o, P/ N
Johanna Rees and Kathryn Lilley
, C& T9 L1 c; y" q5 ]7 c11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179
, K2 M( }7 Q0 S( s/ I2 d, TAmy J. Claydon and Robert J. Beynon
. c7 B. ~, |: C) n) h12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
$ ]# ]1 t. s1 T3 D8 d% Z( QBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
4 z- m- p2 C5 n( TDavid E. Hill, and Marc Vidal, @' r/ [  }1 P/ C; Y: l
13. Use of Proteome Arrays to Globally Identify Substrates for E3$ f  O6 q% e8 a/ w! `- Y
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215, B* C( z, \# ^
Avinash Persaud and Daniela Rotin3 U6 f7 c* B9 y  Z
14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic; p. g* m. f/ Z
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 2255 b& u3 u7 K* ^$ ?" _
Catherine L. Winder and Warwick B. Dunn# p2 d6 W" |9 ]; n0 T
15. The Automated Cell: Compound and Environment Screening System' j! \: n+ d: Y8 m% z3 i
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239
6 D6 X+ a6 I! M: Q2 ?Michael Proctor, Malene L. Urbanus, Eula L. Fung,2 S) h) p( p; K
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,1 h3 @1 U+ ?& n8 S0 P
and Guri Giaever1 n1 e: q' S' C7 }$ Q' k8 Y7 K
16. Competition Experiments Coupled with High-Throughput Analyses for
; C4 i9 I! E# y' _Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
0 r0 ~- @( Z, D# ~Daniela Delneri
8 D9 r2 l3 C+ {; J* |* k17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
+ O9 d& d7 t; WExamination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283
* |% I: p6 V, ?/ v6 a# f" iBrian D. Slaughter, Jay R. Unruh, and Rong Li+ s2 g6 ?' g( L9 x0 C4 Y  f
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307
; b3 C9 r, t- d9 l! j% B6 X, v$ MYuehua Wei and X.F. Steven Zheng
  t9 w' b: ?8 t6 gSECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES
) e' E' m5 J: O! `7 f2 iAND ANALYSES
; C6 n. ?6 w3 H19. Computational Yeast Systems Biology: A Case Study for the MAP
( O8 q9 s' U: i& IKinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3234 X2 m: ]) }3 x9 r3 n% V; {. S5 N; y" y
Edda Klipp# f. @& {8 _/ O$ f  G0 z! c  J
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
4 m9 E  N2 Y7 L; y9 z. ?Axel Kowald and Christoph Wierling
; f1 ^" A& ~" x  N: K21. A Computational Method to Search for DNA Structural Motifs in2 Q; s8 s# a- r: E. c6 N
Functional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 3673 ~; b, O8 \& v, }! r
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius1 F: F# G& }! c3 l" w* i
22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381
. f" d2 M! f4 E6 y4 oShoshana J. Wodak, Jim Vlasblom, and Shuye Pu
; `2 I4 J: N; [% ^23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407
# G5 m- x3 z2 Z7 MYitzhak Pilpel$ n2 `" a+ p4 _5 _7 v3 V
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
" h  K4 E- I- B; F% @8 OProcesses in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4270 B$ X" W) ^' R" I; J
Jean-Marc Schwartz and Claire Gaugain. i7 v1 G6 Z: W3 ^0 e$ V
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
, ~! ^. h6 z3 n8 q  @Intawat Nookaew, Roberto Olivares-Hernández, Sakarindr7 i' L9 w+ Y9 y. o* n0 h
Bhumiratana, and Jens Nielsen
4 m) u  y$ G  |* A3 {+ |26. Representation, Simulation, and Hypothesis Generation in Graph& f) J  O, Y& D  C8 L+ I8 j& Z
and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465
3 I" `: B+ O# M- {- CKen Whelan, Oliver Ray, and Ross D. King
- c! L' v3 v- z$ p' r0 z$ `2 h27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483
5 G) ~- G% x) C$ u5 lBalázs Papp, Balázs Szappanos, and Richard A. Notebaart3 e2 l1 n- f7 N3 _$ Y& L
SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE
: p8 @' S4 k1 S/ Y. fAS A TOOL FOR MAMMALIAN STUDIES
. P% {; f/ R; e* d- ?28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian8 \( O5 J# L! u, L' n! ~2 x
Gene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501$ ?( P% d& C$ a2 k
Nianshu Zhang and Elizabeth Bilsland
; b: L( i( t6 `: ]6 v) [4 N2 q! G  oSubject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5251 v& R' I4 N0 ^# R2 b
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