PDF电子书：Yeast Systems Biology

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Yeast Systems Biology
: V) ~+ K0 L/ u5 F0 r- oMethods and Protocols
* q! v" a; _! M! CEdited by) M/ [2 X; ~/ S' Y7 h# g
Juan I. Castrillo7 x0 F1 z, I! e$ v9 r. d- u( x( ~

+ @) E' x( }* m/ E  l$ H- {  k4 R- @Contents5 Y, S$ Y" `! j* |. C  V
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v# O9 w% r+ d( m4 t" o# K
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
) c) x4 V8 [" J6 D8 ^6 uSECTION I: YEAST SYSTEMS BIOLOGY
5 Z* i; D6 P! o2 }- D; U* f1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3- W, s$ t3 c% s) U# q6 H& \3 q
Juan I. Castrillo and Stephen G. Oliver* c* S' `: b# v4 r( g& \
SECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE8 |6 E3 u" b* V$ P
AND MOLECULAR STUDIES
3 c' x: Q9 k8 C2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
% j5 u) `3 H4 tof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31! r$ M9 ^+ B6 F' M
Ed Louis/ P1 J  c; k* z$ v( R" D
3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 410 j5 ^; r$ B6 z* x, U. p0 ~
Oliver J. Rando% D" _2 C5 ~/ U8 }2 Z, B
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 610 ]. m6 [9 F, i" @7 v" r1 I& Q3 L
Dustin E. Schones, Kairong Cui, and Suresh Cuddapah& |0 E" [4 Q0 v; u" y2 R% a
5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73' `7 m9 v6 _9 p4 ]# G6 v/ v
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef6 n9 b& x* P6 h0 E" y2 i% q
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide
, @- Q$ u" l6 _- ZIdentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
" L# I1 c& V# h0 f: o- DHelen Neil and Alain Jacquier- w1 Z2 ^6 }* X' T
7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
( S2 S8 p) u! P4 a5 O8 S$ C! y# STiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107% n4 z: y2 {- N6 j
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz$ Y0 S5 R& B4 O$ N) D& t  b4 l
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1252 c1 _0 u$ P+ U5 W& O# Z; X
Karl Waern, Ugrappa Nagalakshmi, and Michael Snyder
4 Z; G# E7 }) Z% A. B) I9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133* z" g9 ]) g4 T' Y
Traude H. Beilharz and Thomas Preiss8 z9 F3 ^0 z2 N# P
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149; \  r0 Z/ G4 K
Johanna Rees and Kathryn Lilley2 J$ T1 F6 j' `. k
11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 1797 c. Y/ m. S  ~
Amy J. Claydon and Robert J. Beynon  m6 C# ]4 ?2 n1 R, ]
12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
; g# S9 a$ ~; j2 i% rBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,* H: f& [4 t  _" X4 H3 V! C
David E. Hill, and Marc Vidal/ Z  W4 B4 j$ ~9 N& y2 ]. |6 b0 X) d
13. Use of Proteome Arrays to Globally Identify Substrates for E36 p2 E. R/ R" |! B- b" w
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
5 F: Q4 I3 h3 o; t* ]. B  t- lAvinash Persaud and Daniela Rotin
2 j) n- @- a  g( i- e7 m7 ]' R14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic
  f1 j; |. J& `6 \Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225  \0 h+ n7 T/ S# E
Catherine L. Winder and Warwick B. Dunn
2 v/ ?/ o( m9 V$ A6 `15. The Automated Cell: Compound and Environment Screening System7 _( Q( x6 `6 H$ @& P0 k
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239% w6 w2 W) w! n! y/ l
Michael Proctor, Malene L. Urbanus, Eula L. Fung,
2 v- g8 h- Q% X6 bDaniel F. Jaramillo, Ronald W. Davis, Corey Nislow,
* b1 T. V0 l/ i, ^. x) A' [% eand Guri Giaever) \" w6 v& M2 W$ i, ^  `, c  ^/ O
16. Competition Experiments Coupled with High-Throughput Analyses for
5 i/ L% Q; _! K0 X4 `! fFunctional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
: j+ p* |5 _; z" m; I  W3 uDaniela Delneri, t( E/ j9 o$ ]9 p- ^/ Z3 I, \: @
17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
, M; t0 G  V; O- M9 Y- @Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283
6 Q1 c3 h1 f6 V% E; qBrian D. Slaughter, Jay R. Unruh, and Rong Li4 k3 j" t5 q# m8 f
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307- {  u5 j* i/ N, h, Y
Yuehua Wei and X.F. Steven Zheng
$ F9 s) I/ u' d/ vSECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES
5 r8 U: A, D. q; fAND ANALYSES& O5 t, @1 ]# J9 L; ?4 R' ?! V3 N
19. Computational Yeast Systems Biology: A Case Study for the MAP
* }  U. W7 k3 FKinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
+ t: q7 a4 ]! W7 Y$ \* |/ yEdda Klipp9 w9 j! b# A8 m; i( {( [* n3 |
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345$ O* X8 V# q9 c( e* N
Axel Kowald and Christoph Wierling
2 Q& o% |; d" F, f* T7 U0 R21. A Computational Method to Search for DNA Structural Motifs in8 w0 z4 O+ R, ]* r, S$ I; @
Functional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 3675 q" {" \# M, U9 d; M* e3 U
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius
8 |" a2 n2 k: u22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 3819 y9 ^; a: a% b% g1 k+ [. G
Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu: g/ B2 d+ k1 x+ H0 h
23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407+ i1 R; m: ]" H( j) U
Yitzhak Pilpel7 O* j1 o4 L4 X! k
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic: C: u6 f/ x/ A+ o3 V
Processes in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
- }, Q: x4 a7 ~9 G' w2 uJean-Marc Schwartz and Claire Gaugain- X4 y  U* Y1 B1 u
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
- K" x2 T0 X0 t& [* uIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr; Y) N  Y) R  p/ k8 Q; A
Bhumiratana, and Jens Nielsen3 K6 c: V) G( k; ~( m3 c
26. Representation, Simulation, and Hypothesis Generation in Graph: Y$ v6 d1 P2 B% n9 x
and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 4651 Z9 L7 K& z) s' G: t
Ken Whelan, Oliver Ray, and Ross D. King
( e5 i9 u9 a4 ]7 f" d27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483' c. ^" E% {  D1 E& S& i" E
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart5 x9 J" c  T- G& x# @" S
SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE2 ^: O+ Z8 q, L  l4 J1 A
AS A TOOL FOR MAMMALIAN STUDIES# P9 C9 j# d$ F$ I
28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
3 G% O7 f( w7 \" Y2 R8 P- [Gene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
/ Q' e" \4 T7 \- |Nianshu Zhang and Elizabeth Bilsland
: b9 W/ X- B; P: h+ ZSubject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525
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