PDF电子书：Yeast Systems Biology

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Yeast Systems Biology. V9 W: K# D- e, ?) z& ~4 y. r
Methods and Protocols$ y5 @3 ]+ L8 w" ]7 H' U
Edited by
: r: Z) k1 G& u) [" qJuan I. Castrillo
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Contents
$ }# F. |' L6 {7 ~* _: c% gPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
- t$ E2 X+ x& m! a( H! C9 ], @Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
# ?- A9 B, }! q3 n5 y% ISECTION I: YEAST SYSTEMS BIOLOGY1 ^/ R% _8 b" e6 }% R
1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3* `, j9 {& T2 ]) p' H9 ]) c) G3 K9 s
Juan I. Castrillo and Stephen G. Oliver
1 y# k& c1 D- q  F7 TSECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE
6 s+ K! |+ f+ y$ p' P+ ]& U$ pAND MOLECULAR STUDIES; _3 s& o2 f/ j8 p
2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State4 G4 a9 _$ q/ C6 \3 s) i
of the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31* C) I) a* N  C- v! s4 t2 f5 g/ D: L
Ed Louis
5 n' V1 o' R6 u  k2 a  G3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41' N& f  n3 h2 P$ o5 K
Oliver J. Rando
6 Y: I: y" A$ {" j' f) Q/ w8 I1 o4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
0 H+ G# B5 J6 i+ {1 ~" `8 p, ~Dustin E. Schones, Kairong Cui, and Suresh Cuddapah
: ^( ~. P" f6 |7 U5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73
( v- e0 F& `1 W6 KAndrew Hayes, Bharat M. Rash, and Leo A.H. Zeef7 \& u% U$ M4 `/ S+ E/ \
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide) V$ Y' f, ?  e, A5 E# Y- E
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87; n1 L9 B$ s- h! M; Q
Helen Neil and Alain Jacquier( W% O* L% {! [' f, k
7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density3 ~4 b5 z  a; X0 ?& |
Tiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107. m! U& n0 P% Q& |0 Q: W
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz- Z& {. N: B8 I
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
' y* Y! v8 r( E: e2 ]Karl Waern, Ugrappa Nagalakshmi, and Michael Snyder6 g7 @! h2 Q2 `+ S
9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133' P* V3 M! @$ m$ B* S2 E
Traude H. Beilharz and Thomas Preiss. A4 J: m, R, `! A- _
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149
! Y( K" d3 N( B1 CJohanna Rees and Kathryn Lilley
& k+ }# @" V! Z& ]11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179
. w5 g3 J# H: Y6 O) [5 `7 K( ZAmy J. Claydon and Robert J. Beynon
, x/ f% J$ S; v; B12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
# i1 A5 [% K/ H4 I" MBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
: U; r) d7 q( G/ F. d- R8 L, ^David E. Hill, and Marc Vidal" u1 t. \% _# d, O" K9 F$ R& h
13. Use of Proteome Arrays to Globally Identify Substrates for E3! e$ w0 y0 o( O  A: S! ^4 L4 s
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
/ ^& [: e! G9 l5 F- lAvinash Persaud and Daniela Rotin
" x, D2 n6 ]% q2 U. Q14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic
! c3 z0 t% b2 m, P  HProfiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225
' ]) o& i& y7 L' F# N( g5 ]$ a" n2 eCatherine L. Winder and Warwick B. Dunn
0 R  E$ F9 [0 M, |15. The Automated Cell: Compound and Environment Screening System
; M& Q8 O. v: n' V% g& a& J- R(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239
9 I% Q. n; M* @Michael Proctor, Malene L. Urbanus, Eula L. Fung,& U) Z/ N" d0 M% A; P1 E" m! a8 N9 U
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,
' h! V3 P* ]8 G5 Aand Guri Giaever
0 b8 \0 ^# z( |% p! X0 y16. Competition Experiments Coupled with High-Throughput Analyses for' r' L: m8 T5 g) Y9 ~7 `
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
' J$ D, J% K) q; G& z% }2 ZDaniela Delneri
1 ^( j' V; r+ b5 z8 M  o! P% d; f+ E( c! ]17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
! l4 C' {5 n2 l2 V) q9 KExamination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283
2 V1 n% e% t( u9 BBrian D. Slaughter, Jay R. Unruh, and Rong Li0 }- N; I1 @( H: @
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307: a3 v- W- T7 W& P7 {2 J0 q
Yuehua Wei and X.F. Steven Zheng
2 M7 q: D' y- Z, J' t: {SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES
% O. }8 r4 p! F: T: T' o; YAND ANALYSES
  G, T; b* H' o+ s0 P19. Computational Yeast Systems Biology: A Case Study for the MAP
8 I: `+ d9 l+ P3 W8 TKinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323, i; r: F0 d  ]
Edda Klipp6 Y/ ?+ f& C* s4 l
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 3458 [, d" ?" P* z! L8 `& S: q
Axel Kowald and Christoph Wierling
6 y; ?0 \4 O8 R" N8 }; ^1 W21. A Computational Method to Search for DNA Structural Motifs in# O$ Z: Y. y: ^7 S/ [. X, ]
Functional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367
3 s5 K2 s. Y  v: I( M. ]" ^4 `Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius
- |6 K: M0 S3 N22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381! Z( U* i6 m, u& }9 X
Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu5 m5 o8 K" O, M3 q
23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 4079 B) l5 }) }7 H, u
Yitzhak Pilpel$ u$ L0 K+ `' Z, t5 \
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
9 o8 J/ s/ N( A* m6 h$ f1 T5 R+ GProcesses in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427+ H  K3 W, [, E6 v; l& G7 u
Jean-Marc Schwartz and Claire Gaugain" _6 _' t- l+ S3 S+ L! ]
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
2 P+ x# @9 b: s. ?# [# S0 oIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr
% y0 Q( j0 u. v/ Z2 d5 BBhumiratana, and Jens Nielsen# w* o' Y7 g9 }4 b& y
26. Representation, Simulation, and Hypothesis Generation in Graph
; Z7 k. a( N  v4 K: gand Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465
7 }4 f- g: {! |2 ^Ken Whelan, Oliver Ray, and Ross D. King9 @" b7 g5 l( O( \
27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483( A2 i% {  x: T- g
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart
/ b: P) ^6 Q2 L) dSECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE
* O" F, m' @" Z3 r1 p' eAS A TOOL FOR MAMMALIAN STUDIES
) Z1 _+ e9 ?( w, x9 P28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian0 x( s4 d. Y* u
Gene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
4 i' n) ^+ H2 `8 R. I+ @Nianshu Zhang and Elizabeth Bilsland% b2 _. L) N7 J* ^
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5252 \4 L: |7 C$ m8 x" b( M8 \0 |
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