PDF电子书：Yeast Systems Biology

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1 v( F" C* M9 h. l+ W$ YYeast Systems Biology' M; Z( Y9 e' k/ c
Methods and Protocols) `3 o$ l3 U& r7 @
Edited by
) ?" B: k7 O0 w# h  C  mJuan I. Castrillo
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6 T- O$ s4 p. YContents
+ Y1 D& v+ S6 d. w- J' s; j2 {Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
/ i, ]' T; s' F) I6 O/ I; Z6 @Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
$ o2 f! P; U8 H5 Q7 nSECTION I: YEAST SYSTEMS BIOLOGY
. ?( k- c! y  S1 O2 e1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3  Z5 i( e0 ^/ O8 O( ]6 i# e
Juan I. Castrillo and Stephen G. Oliver0 M7 H0 p3 f  m9 O
SECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE
7 M5 K: l1 Y7 M) xAND MOLECULAR STUDIES
0 v1 x( Q. E5 d) Y" }2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State$ x: y/ Q; F) v. ?0 C. z
of the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31
9 k7 }( w0 g) qEd Louis
# F1 _$ a; w1 w1 L5 G# L7 B1 @3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41: T# R, }# T: U; N8 z
Oliver J. Rando
4 @9 Z) O0 |& Q4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 616 K3 p, e6 s( r! y7 p9 ]% F
Dustin E. Schones, Kairong Cui, and Suresh Cuddapah1 v1 b$ S( Z/ U' K
5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73; }& S  h, g: F$ w8 r( Z. ~) v4 z7 v
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef; g# }1 `# ~8 V6 S& \. N0 J
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide
/ @. w. q; Q" R$ gIdentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6 C+ C- M+ ^1 T1 N/ [/ fHelen Neil and Alain Jacquier! M7 j) Y0 H% H1 F9 P
7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density9 t1 B! D# y+ b7 I9 S8 X; ]
Tiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073 X7 {7 d1 z( q4 A
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz0 q/ T0 T  P6 T1 O2 F7 H# k8 U$ s
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
) Y3 {# [+ ?9 o9 CKarl Waern, Ugrappa Nagalakshmi, and Michael Snyder
% Z5 }, R* n- C6 |3 r9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133" U( c3 C2 V$ a1 J3 v/ f  R
Traude H. Beilharz and Thomas Preiss
8 U. j% [( ^5 Z4 }3 Y6 C10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149+ n4 G$ D+ @7 D. K- S5 v
Johanna Rees and Kathryn Lilley
' p( J& p0 z! H( E( Z11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179
9 o7 R6 n" c6 M6 a# N  tAmy J. Claydon and Robert J. Beynon
' ?3 H+ F# ?" e4 J12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
0 a% ^& ]( u) s! S$ [Benoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
' @0 v6 s0 n& a1 e+ a* @David E. Hill, and Marc Vidal5 v) u/ C. D' }& j! v9 p" W
13. Use of Proteome Arrays to Globally Identify Substrates for E3. q5 i1 _) R: m6 M2 [' y
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215( I  W  Y1 P5 C
Avinash Persaud and Daniela Rotin
6 K* p+ t* S. P& s, I& E$ S14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic8 R" i% Z1 J+ S# t; B
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225
& i: w* r: ]! |1 bCatherine L. Winder and Warwick B. Dunn/ I$ n1 m( Q2 I, E
15. The Automated Cell: Compound and Environment Screening System* r; o, U9 c* ~. h, w
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 2399 v3 ?. a% |  m
Michael Proctor, Malene L. Urbanus, Eula L. Fung,
7 q( H2 h) i; n: X. v8 G. u( bDaniel F. Jaramillo, Ronald W. Davis, Corey Nislow,
4 r8 E3 s, [/ B* R/ kand Guri Giaever* H7 s- a0 p5 G2 e! B4 l
16. Competition Experiments Coupled with High-Throughput Analyses for' G: H0 M/ O) D0 O0 G
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271) i8 \; H% L" |7 D/ M& t# z3 |
Daniela Delneri  q0 M. M; {- j! n7 d3 Z
17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
0 V8 X' ?- n$ g1 b6 s0 }Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283, D/ [1 c: Q) p: D) n! |0 j0 f
Brian D. Slaughter, Jay R. Unruh, and Rong Li2 Z, |& ~8 ]# G
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307' D* Y1 f4 D3 _$ A9 q+ W
Yuehua Wei and X.F. Steven Zheng7 M1 V" j$ u3 n6 L
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES
( ?' V, `9 a8 W5 d% |AND ANALYSES9 t* ]) r- ?% y( t$ P
19. Computational Yeast Systems Biology: A Case Study for the MAP$ `! P6 ?% E" s/ U2 m& b( \, P- f
Kinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323" y9 g/ R7 U9 G2 s% u2 ]- R
Edda Klipp6 K! |) v7 C( d$ g3 n: t
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
0 p. G5 w8 i) w) A. V5 L( Q' x, JAxel Kowald and Christoph Wierling5 U3 U3 r4 \1 z  q" {
21. A Computational Method to Search for DNA Structural Motifs in4 t; E6 i5 A2 }( Z
Functional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367/ g, g6 F/ O5 A% x+ d: _
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius
1 Z) ?" C) e8 g0 O" F) h7 M9 A22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381
0 }. d. U) |& M& x" H+ o% ~% f1 WShoshana J. Wodak, Jim Vlasblom, and Shuye Pu; L2 \! {5 H. H* G9 L! J' \
23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407( z( x, m2 t% q) \$ S+ g
Yitzhak Pilpel* m6 s- ?% |' c( b
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
% d: s1 x% L2 P! B$ ]Processes in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4274 \# o' o! U9 p  X* j
Jean-Marc Schwartz and Claire Gaugain
: F) X6 e3 z2 K+ U8 \7 s25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
2 x# \% L  L' U4 XIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr
' m% g' a- J5 A2 f: |" vBhumiratana, and Jens Nielsen! F) h4 S% d& l! ?' n+ {# u
26. Representation, Simulation, and Hypothesis Generation in Graph% t7 l' f' N7 I1 Z1 o5 B
and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465
/ K& ~+ a( l/ B7 @6 `. G6 ]Ken Whelan, Oliver Ray, and Ross D. King
, ~  x# P4 ]0 q6 s1 G; M4 r! {27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 4832 Z8 U8 N# H& l8 B+ q' c
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart
8 v( e, J* x2 o/ _/ G( m( [SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE3 j9 e. w1 ?7 Z/ }7 q! D2 O# U
AS A TOOL FOR MAMMALIAN STUDIES
* H1 y4 t9 U: [3 A4 P28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
: `4 X3 Q4 b; E' xGene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501- i1 g% D' h9 i8 m* v1 r
Nianshu Zhang and Elizabeth Bilsland
/ |4 c6 W! r: o7 C  ISubject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5253 l* W- h1 i6 C! L6 [6 X0 f
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