PDF电子书：Yeast Systems Biology

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' G' z% {, K0 }$ UYeast Systems Biology
$ K  Z2 Q9 l% t/ ~Methods and Protocols
- P5 |+ q% X/ x% U! CEdited by
0 h- [# @/ [) X: j7 q2 p* O" D& ?, PJuan I. Castrillo" ~) T0 {5 c6 u7 \

( D! Y1 ^# N# d# FContents+ H/ O" O, C' E7 S3 y0 D
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
8 w/ U# e) H4 NContributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
3 A" L  R1 t* k2 E# H4 K( ^  n/ qSECTION I: YEAST SYSTEMS BIOLOGY; e" @7 ?& ?8 r" V" w: n
1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3+ k3 R3 V- ^3 D( d/ O( F
Juan I. Castrillo and Stephen G. Oliver
# i8 F4 C: {( }4 j& E. ZSECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE$ _& ^* {# C9 a5 x/ ^/ l
AND MOLECULAR STUDIES) F8 G: U3 K$ c7 ]! e$ j7 Z
2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
/ {- J4 a; I: E" [  oof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31  C+ Q* v. i$ e8 b  |0 f/ t$ T  C
Ed Louis8 P* \" D  ~3 J& C+ x$ V
3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41) e& B3 |2 g& N! k4 \
Oliver J. Rando- g, W) M# J$ A) h7 Z* N. V
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
6 [$ U: u; l) }2 x1 Q; u) pDustin E. Schones, Kairong Cui, and Suresh Cuddapah
8 |4 u( A- @) ?" j' O5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73
) _; n2 R9 Z  ^6 `2 x4 c% h# uAndrew Hayes, Bharat M. Rash, and Leo A.H. Zeef
+ B) x8 p" I. o1 F6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide
0 h' \) T" ]* E7 b; cIdentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
0 g7 H8 W* ?, |+ w( S& EHelen Neil and Alain Jacquier
9 f! ]  D' {* Z# K7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density0 ?9 f- u; s, c
Tiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107+ Q" P4 Q8 U0 z, b$ C3 S
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz" Q* E/ b% r. K
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
1 ]1 Q6 |( X$ e2 p% G: y/ I5 YKarl Waern, Ugrappa Nagalakshmi, and Michael Snyder
6 N  }( y; p  N9 u4 |9 V& y9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
. ?$ `' p& G1 T0 `Traude H. Beilharz and Thomas Preiss5 E8 G( b# F- L
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149/ w* t' J5 P. i" i: F: @! ~
Johanna Rees and Kathryn Lilley; e/ K" l& ~* {" c
11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 1797 X* k5 w* F, _- p$ j) a" A+ s9 l
Amy J. Claydon and Robert J. Beynon' Q6 x. R9 ?) m1 O/ B2 D
12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
: l; i" Z* @- E$ D7 j: iBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
- m" ^' Z* R* rDavid E. Hill, and Marc Vidal8 ^$ ^: e, t! ?. X( s5 N) `6 X; L# S% L8 e
13. Use of Proteome Arrays to Globally Identify Substrates for E3
6 K" I1 h2 d& Z6 ^Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
! `! R! s& ]+ W! V9 J0 SAvinash Persaud and Daniela Rotin% \2 H  ~  z" ~' @. f
14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic  i* C' Q+ I7 t
Profiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225
" Y4 c9 ?- Y+ Q0 y/ c  y$ }& xCatherine L. Winder and Warwick B. Dunn
% P. b& ~* ^" P15. The Automated Cell: Compound and Environment Screening System
5 q. S; ]7 l9 f8 I- T/ n& _# J(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239, {% E! w9 |( I0 M( R- b& K
Michael Proctor, Malene L. Urbanus, Eula L. Fung,5 W; f8 S0 d" W1 a% C6 e
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,
& d. d0 o7 w1 h3 m2 g" Xand Guri Giaever1 T: f1 y3 N  I+ f2 z! T
16. Competition Experiments Coupled with High-Throughput Analyses for* b% ^2 a, K, j0 K
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 2717 _5 H5 {; K- I
Daniela Delneri, P; @1 ?! }- q1 S$ d- ^+ A6 d; y
17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for" f9 R9 o8 r1 e- J0 [) n
Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 2833 F8 N$ T( |# v# v8 F' [  }, g/ m
Brian D. Slaughter, Jay R. Unruh, and Rong Li
$ j( ?( U" H- H+ V0 f- U  O0 H18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307
: O* ]& s" c' {# SYuehua Wei and X.F. Steven Zheng, C5 N& E6 \4 B; y
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES7 M! i* g2 q2 z' z- U1 c2 Z9 f
AND ANALYSES
/ x& @" _9 t& _/ v. m19. Computational Yeast Systems Biology: A Case Study for the MAP! |8 L" C4 F5 Q8 A7 d3 O. E/ i
Kinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
+ k6 ?' c5 V6 [4 B& E% IEdda Klipp  X5 T! B) g0 ~  O( q) }: u
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
$ g: `! K/ r" c& _5 l: kAxel Kowald and Christoph Wierling
0 ~( @3 L1 z# m% H( }21. A Computational Method to Search for DNA Structural Motifs in
6 d  M: J2 D( E' Y- T" s* T7 O+ mFunctional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367& {9 I8 y, Q& I! K2 O& U
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius# O# b" X' I; p/ ~8 L
22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381  k  p! r+ L3 C7 Z
Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu
3 Q0 O5 d: G$ u/ T7 M' \23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407
4 x8 y, h4 T8 O+ A  F8 v6 _- `Yitzhak Pilpel% r: Z3 b2 b0 I- d  a
24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic, ^( O" U! Z' h$ H) Y, {
Processes in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
3 O, y( w  Y, d+ }! SJean-Marc Schwartz and Claire Gaugain
+ x2 b/ v6 `/ w% W0 j6 G. M8 T$ K25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445
; }& f: ^% N# P; o* \4 j' GIntawat Nookaew, Roberto Olivares-Hernández, Sakarindr+ s2 J8 s& B& ^; [; P- u
Bhumiratana, and Jens Nielsen
2 G+ K+ ?- Q1 r  |& U26. Representation, Simulation, and Hypothesis Generation in Graph& {& s  q4 r/ A7 p! Y- y5 W. l
and Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465
/ L$ y, x  |4 p6 i# D, ~Ken Whelan, Oliver Ray, and Ross D. King
& Z4 c' B/ y! y/ f6 X27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483
2 v3 L% t* u9 d4 r6 ]3 {! a/ RBalázs Papp, Balázs Szappanos, and Richard A. Notebaart' ~$ @/ _. B! l9 ~" r; Q% I
SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE
3 w5 m7 E! L' PAS A TOOL FOR MAMMALIAN STUDIES' m: y3 h# V5 Z, O7 ]% h+ A* Q
28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
# c1 J2 g8 ^- g1 WGene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
0 _+ Y8 P2 T# Z: L3 g8 `. c, }5 y0 PNianshu Zhang and Elizabeth Bilsland
1 ?: `3 v( w; ?4 a: Z0 ]Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525
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