PDF电子书：Yeast Systems Biology

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Yeast Systems Biology
, A& T6 P# M6 s# VMethods and Protocols: N6 Z. J. g" ~2 i' X
Edited by
+ r* X* s+ Y; E# m& JJuan I. Castrillo, L/ c) c  w( H. A( M; ?0 @- ?

- _& ]& [* ]) c' ^- T. RContents  p/ M2 h7 `+ z6 M+ w
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
3 j- |6 O; B7 VContributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
/ r) `+ H+ A0 z) z( k9 iSECTION I: YEAST SYSTEMS BIOLOGY
% Z9 @- e! p9 D) U/ `% n1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3
* K8 v  ]2 ]6 Z! xJuan I. Castrillo and Stephen G. Oliver7 M! @8 h- K# J) Y  l
SECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE& W7 V/ O7 O$ Z( N# Q
AND MOLECULAR STUDIES
3 ~! ]; Q$ i) d. F( L7 x2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
6 E( Y- h$ E7 l1 Q3 f5 Hof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31
9 H0 ~/ `2 a$ }3 B+ q* AEd Louis
7 |7 Y. h! Z2 i5 V* D3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41& B+ E0 j! A" X1 w
Oliver J. Rando. ^" }* g6 T! V# z
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61; t; k+ E* ^$ @) L
Dustin E. Schones, Kairong Cui, and Suresh Cuddapah  C. u, L* F& B9 E6 w
5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73
7 i- F" y6 |+ T1 pAndrew Hayes, Bharat M. Rash, and Leo A.H. Zeef$ w5 R" L# h* S
6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide; @! \( T+ N6 M$ S" W- f
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874 p6 H$ _: F. P) ]. O1 ?
Helen Neil and Alain Jacquier
* I# C; _& x8 h6 \; M- S+ ~7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density6 [; @1 ?: T% r- T/ ]! s. z
Tiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1077 q' e% k9 p- Z' u
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz
) l7 h+ o- _3 z! T3 ~% q8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125  f- s+ ^* Y  E5 J/ R! Y
Karl Waern, Ugrappa Nagalakshmi, and Michael Snyder
1 \2 E  D' X' j/ L  [3 ^1 E) ~2 w9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
! a( z- f% X0 BTraude H. Beilharz and Thomas Preiss, u2 |* n+ L3 k) B8 f  {
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149
7 V3 {* A9 R7 m, r5 k5 j& PJohanna Rees and Kathryn Lilley; X  P' h' M( Y: u
11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 1793 b  z3 \/ ?5 T* G* F  s) g) e
Amy J. Claydon and Robert J. Beynon2 N1 r4 Q  I8 Y6 v7 o; B& ^
12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
6 F6 S3 B' R% |2 D6 Z. k% NBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
: }( h# m6 ?4 w' A1 PDavid E. Hill, and Marc Vidal
+ U( y7 o# n$ F6 t/ ?- q13. Use of Proteome Arrays to Globally Identify Substrates for E3/ w; P& c, N  T+ I
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2155 L" L& J- e& r$ |# m. G
Avinash Persaud and Daniela Rotin
) [7 h+ D6 s, v2 b; P14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic5 J2 b, n% t1 M0 `/ ?" `
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225% i: d# u4 f8 _; Z
Catherine L. Winder and Warwick B. Dunn
; g* ~0 X! R5 n) a3 h9 ]1 k15. The Automated Cell: Compound and Environment Screening System
! |% F  e  W" W(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 2394 S5 Q3 A4 t* D  t4 ^. e: W
Michael Proctor, Malene L. Urbanus, Eula L. Fung," Z0 g$ X0 x% L5 h$ P
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,+ M  u3 y* Q  y& u
and Guri Giaever! w6 m4 L- B) h- i. D5 L1 h( Y$ f
16. Competition Experiments Coupled with High-Throughput Analyses for: m4 l4 h& g$ b. M% n6 [
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
* B3 ^8 L; F  c( d" K: _Daniela Delneri$ p! J' c# V# v  h* D
17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for
6 T4 W" c6 v4 H/ h7 JExamination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283  |' f* j/ w7 t! C9 q) P
Brian D. Slaughter, Jay R. Unruh, and Rong Li
! g& b  T- f8 i+ [( \3 y9 O18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307
! l3 A+ T8 }) @( x* B# t' eYuehua Wei and X.F. Steven Zheng
! v9 f8 }0 H4 ]6 T/ [SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES% M: i+ a( M7 c1 x+ L- |" g
AND ANALYSES/ r, ^4 Z& L0 n. N3 U
19. Computational Yeast Systems Biology: A Case Study for the MAP# W! R& L5 O, l# k" e
Kinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
& \8 ~' m2 D) v# [Edda Klipp
: B/ p0 j0 Z$ B20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
$ X9 a5 f" \! G% H4 oAxel Kowald and Christoph Wierling& u$ n; o$ E0 ^9 ?9 z! V3 g, b
21. A Computational Method to Search for DNA Structural Motifs in
' E; j; t8 O* O" ]- X: EFunctional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367+ p: G, V2 @* D" [% K0 z* E, H
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius
$ G& ^6 v& q* o: J& w1 q5 P# }  x22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381
/ y/ q* ^5 h; {Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu
* P2 s7 ]% Y& i8 K23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407
8 M# {# e# @7 g7 i2 v1 iYitzhak Pilpel
) C! n/ a0 w2 `4 }* [; [" Y, }24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
# _$ j. h# V, y8 X; \Processes in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4272 W0 |6 o- Z& ^
Jean-Marc Schwartz and Claire Gaugain
: W- M! u4 e% n$ E" {25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
- F1 I" \, h$ c+ x0 bIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr- ^2 A1 ?  Q3 H+ ?+ O4 p# [
Bhumiratana, and Jens Nielsen, y' R, B; U0 W
26. Representation, Simulation, and Hypothesis Generation in Graph
6 c# ~2 [& Q. Q: V9 Dand Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 4656 c& ]3 k7 t4 ]* z: P0 V
Ken Whelan, Oliver Ray, and Ross D. King
* g  G0 C5 J, y+ m27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483: F! w, m  c$ V& ~$ g9 V* K% E+ ]! s
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart
% r) y! p# g: d" ]  l9 i; n* oSECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE
  N+ h( n: Y1 t! P* \: r2 eAS A TOOL FOR MAMMALIAN STUDIES
# y/ B8 s# z$ Z: @) ?  t28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
/ r7 c, {, i8 MGene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5019 d) Q3 f# T) W- K  {% @
Nianshu Zhang and Elizabeth Bilsland
, V5 j8 q1 S# X! a- T7 t4 |Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525" M8 ~7 y9 _) `3 u& y) L1 _# I
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