Target Discovery and Validation Reviews and Protocols

More documents

Recommendations

Info

Gene Networks 47 Table 2 Druggable Genes of MAL33 and CDC6 Druggable Drug-affected Parents a Grandparents a MAL33 (YBR297W): HSP82 (YPL240C): BAR1 (YIL015W): Maltose fermentation heat shock protein barrierpepsin precursor regulatory protein SRB4 (YER022W): GPA1 (YHR005C): DNA-directed RNA GTP-binding protein α polymerase II holoenzyme subunit of the pheromone and Kornberg’s mediator pathway (SRB) subcomplex KAR2 (YJL034W): subunit nuclear fusion protein CDC6 (YJL194W): ARP7 (YPR034W): GAL11 (YOL051W): cell division control component of SWI-SNF DNA-directed RNA protein global transcription polymerase II holoenzyme activator complex and and Kornberg’s mediator RSC chromatin (SRB) subcomplex subunit remodeling complex FAR1 (YJL157C): BAR1 (YIL015W): cyclin-dependent kinase barrierpepsin precursor inhibitor (CKI) SLA2 (YNL243W): cytoskeleton assembly control protein a “Parents” means these genes connected directly to the drug-affected genes. “Grandparents” means there is one intermediary gene between these genes and the drug-affected genes (11). 3.3. Time-Expanded Network Method for Identifying Drug-Active Pathways 3.3.1. Time-Expanded Network Method Figure 2 shows the conceptual view of our strategy. As the first step (Fig. 2a), we use the Bayesian network and nonparametric regression model for estimating a gene network from disruptant microarray data. This model can even capture nonlinear relationships between genes and is appropriate to estimate the structure of the gene network. However, the probabilistic inference using the nonparametric regression models has several problems. One problem is that we need to consider the extrapolation for handling the drug response data in the estimated gene network. The other problem is that we need to consider the calibration that is a probabilistic inference from the value of the target gene to the distribution of its parent genes. The nonparametric regression in our Bayesian network model may not be suitable to consider the extrapolation problem and the calibration.
48 Imoto et al. Fig. 8. Strategy to identify the drug active pathways. The red nodes represent drugactive genes considered to be independent on their parent genes. The green nodes are parent-active genes considered to be affected by their parent genes. The red arrows represent the active paths (13). To avoid these problems, we use Bayesian networks for discrete data for the probabilistic inference after estimating the structure of the gene network. We convert the estimated Bayesian network and nonparametric regression model into the Bayesian network for discrete data (Fig. 2b). The drug-active pathways are identified for every time-point (Fig. 2c). This step is based on the identification of affected genes directly and indirectly by the drug. However, it is possible that several genes might still be falsely determined as the affected genes. Therefore, we define time-expanded network (Fig. 8) and consider the time-dependent relationships among the estimated affected genes to reduce the number of false positives. The definition of the time-dependent network is given below (see Subheading 3.2.2.). The connected components in the timeexpanded network are selected as the estimated drug-active pathways (Fig. 2d). This step also can reduce the number of falsely identified pathways. The identification of drug-active pathways is done based on the probabilistic inference on the Bayesian networks for discrete data. For this purpose, we convert the Bayesian network and nonparametric regression model that was used for structural learning of the gene network into the Bayesian network for discrete data. At first, we need to discretize the expression values into three categories. We regard expression values that are greater than a certain threshold h (or less than h –1 ) as “overexpreesed” (or “suppressed”) and otherwise as “unchanged.” The threshold h is determined so that the network estimated by the Bayesian network for discrete data with h becomes the closest to the network from the Bayesian
Page 1 and 2:
METHODS IN MOLECULAR BIOLOGY 360 T
Page 3 and 4:
M E T H O D S I N M O L E C U L A R
Page 5 and 6:
© 2007 Humana Press Inc. 999 River
Page 7 and 8:
vi Preface get. Approaches to targe
Page 9 and 10: viii Contents 10 Transgenic Animal
Page 11 and 12: x Contributors SAHOHIME MATSUMOTO
Page 13 and 14: xii Contents of Volume 2 12 Validat
Page 15 and 16: 2 Sioud disease pathogenesis and pe
Page 17 and 18: 4 Sioud A more fruitful approach fo
Page 19 and 20: 6 Sioud both transient and permanen
Page 21 and 22: 8 Sioud serum biomarkers. This syne
Page 23 and 24: 10 Sioud Fig. 2. Schematic of targe
Page 25 and 26: 12 Sioud 13. Magin, T. M. (1998) Le
Page 28 and 29: Harnessing the Human Genome 15 The
Page 30 and 31: Harnessing the Human Genome 17 Fig.
Page 36 and 37: Harnessing the Human Genome 23 The
Page 40 and 41: Harnessing the Human Genome 27 repr
Page 42 and 43: Harnessing the Human Genome 29 14.
Page 44: Harnessing the Human Genome 31 45.
Page 47 and 48: 34 Imoto et al. (4,5), and Bayesian
Page 49 and 50: 36 Imoto et al. 2. Materials Two ty
Page 51 and 52: 38 Imoto et al. Fig. 3. Graphical v
Page 53 and 54: 40 Imoto et al. Fig. 5. Result of t
Page 55 and 56: 42 Imoto et al. p(D |G) = ∫ p(D,
Page 57 and 58: 44 Imoto et al. β k (k = 1,…,q j
Page 59: 46 Imoto et al. Fig. 7. Partial res
Page 63 and 64: 50 Imoto et al. Fig. 9. (continued)
Page 65 and 66: 52 Imoto et al. Table 3 List of Roo
Page 67 and 68: 54 Imoto et al. 5. De Hoon, M. J. L
Page 69 and 70: 56 Imoto et al. 39. Kamimura, T., S
Page 71 and 72: 58 Beaty et al. cytotoxic drugs and
Page 73 and 74: 60 Beaty et al. Fig. 1. Principles
Page 75 and 76: 62 Beaty et al. oligonucleotide mic
Page 77 and 78: 64 Beaty et al. 3. The 12% polyacry
Page 79 and 80: 66 Beaty et al. 2.3.3. Protein Stai
Page 81 and 82: 68 Beaty et al. 3.1.3. Labeling of
Page 83 and 84: 70 Beaty et al. 1 µL of the anneal
Page 85 and 86: 72 Beaty et al. 4. Phenol:cholorfor
Page 87 and 88: 74 Beaty et al. 19. Wash twice with
Page 89 and 90: 76 Beaty et al. The Following Volum
Page 91 and 92: 78 Beaty et al. using a protein ass
Page 93 and 94: 80 Beaty et al. 6. Shake the gel fo
Page 95 and 96: 82 Beaty et al. Fig. 2. Preparation
Page 97 and 98: 84 Beaty et al. search in. A widely
Page 99 and 100: 86 Beaty et al. 3. Kern, S. E., Hru
Page 101 and 102: 88 Beaty et al. 34. Peters, D. G.,
Page 104 and 105: 5 Molecular Classification of Breas
Page 106 and 107: Molecular Profiling of Breast Cance
Page 108 and 109: Molecular Profiling of Breast Cance
Page 110 and 111:
Fig. 2. (Continued) the right ident
Page 112 and 113:
Molecular Profiling of Breast Cance
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
6 Discovery of Differentially Expre
Page 130 and 131:
Gene Target Discovery 117 In the fi
Page 132 and 133:
Gene Target Discovery 119 sequences
Page 134 and 135:
Gene Target Discovery 121 There is
Page 136 and 137:
Gene Target Discovery 123 digestion
Page 138 and 139:
Gene Target Discovery 125 Fig. 1. P
Page 140 and 141:
Gene Target Discovery 127 5. Sargen
Page 142:
Gene Target Discovery 129 41. Berry
Page 145 and 146:
132 Matsumoto, Miyagishi, and Taira
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
144 Fig. 1. The ribozyme-expression
Page 159 and 160:
146 Sano and Taira 5. 10X L (low-sa
Page 161 and 162:
148 Sano and Taira ribozymes may cl
Page 163 and 164:
150 Fig. 3. A system for the identi
Page 165 and 166:
152 Sano and Taira 4. Notes 1. We r
Page 168 and 169:
9 Production of siRNA- and cDNA-Tra
Page 170 and 171:
Transfected Cell Arrays 157 Fig. 1.
Page 172 and 173:
Transfected Cell Arrays 159 2. The
Page 174:
Transfected Cell Arrays 161 5. Simp
Page 177 and 178:
164 Houdebine Fig. 1. Different met
Page 179 and 180:
166 Houdebine concentrations become
Page 181 and 182:
168 Houdebine Fig. 2. Major methods
Page 183 and 184:
170 Houdebine integrate into the me
Page 185 and 186:
172 Houdebine 2.2.3. Knock-In Into
Page 187 and 188:
174 Houdebine as insulators. The co
Page 189 and 190:
176 Houdebine Fig. 5. Different met
Page 191 and 192:
178 Houdebine systems. Moreover, st
Page 193 and 194:
180 Houdebine contribute to generat
Page 195 and 196:
182 Houdebine Fig.7. Example of a v
Page 197 and 198:
184 Houdebine more extensively. Tra
Page 199 and 200:
186 Houdebine and stroma. Several o
Page 201 and 202:
188 Houdebine explained at the mole
Page 203 and 204:
190 Houdebine of the intestinal epi
Page 205 and 206:
192 Houdebine interference of the g
Page 207 and 208:
194 Houdebine 17. Whitelaw, C. B. A
Page 209 and 210:
196 Houdebine 51. Bell, A. C., West
Page 211 and 212:
198 Houdebine 86. Carmichael, G. G.
Page 213 and 214:
200 Houdebine 121. Pailhoux, E., Vi
Page 215 and 216:
202 Houdebine 156. Auerbach, A. B.,
Page 217 and 218:
204 Vijayaraj, Söhl, and Magin 1.
Page 219 and 220:
206 Vijayaraj, Söhl, and Magin Fig
Page 221 and 222:
208 Vijayaraj, Söhl, and Magin fil
Page 223 and 224:
210 Table 1 Orthologous Human and M
Page 225 and 226:
212 Table 2 Orthologous Human and M
Page 227 and 228:
214 Vijayaraj, Söhl, and Magin ulc
Page 229 and 230:
216 Vijayaraj, Söhl, and Magin of
Page 231 and 232:
218 Vijayaraj, Söhl, and Magin sug
Page 233 and 234:
220 Vijayaraj, Söhl, and Magin abs
Page 235 and 236:
222 Vijayaraj, Söhl, and Magin 1.2
Page 237 and 238:
224 Vijayaraj, Söhl, and Magin Fig
Page 239 and 240:
226 Vijayaraj, Söhl, and Magin gen
Page 241 and 242:
228 Vijayaraj, Söhl, and Magin the
Page 243 and 244:
230 Vijayaraj, Söhl, and Magin f.
Page 245 and 246:
Page 247 and 248:
234 Vijayaraj, Söhl, and Magin b.
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
240 Vijayaraj, Söhl, and Magin alt
Page 255 and 256:
242 Table 3 Time Schedule For Aggre
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
250 Vijayaraj, Söhl, and Magin 101
Page 266 and 267:
12 The HUVEC/Matrigel Assay An In V
Page 269 and 270:
256 Skovseth, Küchler, and Haralds
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
Page 283 and 284:
270 Iversen and Sørensen witnessed
Page 285 and 286:
272 Iversen and Sørensen Fig. 1. P
Page 287 and 288:
274 Iversen and Sørensen the core
Page 290 and 291:
14 An Overview of the Immune System
Page 292 and 293:
Overview of Immune System 279 diffe
Page 294 and 295:
Overview of Immune System 281 Fig.
Page 296 and 297:
Overview of Immune System 283 then
Page 298 and 299:
Overview of Immune System 285 expre
Page 300 and 301:
Overview of Immune System 287 Once
Page 302 and 303:
Overview of Immune System 289 solub
Page 304 and 305:
Overview of Immune System 291 amino
Page 306 and 307:
Overview of Immune System 293 (unre
Page 308 and 309:
Page 310 and 311:
Overview of Immune System 297 the c
Page 312 and 313:
Overview of Immune System 299 (44).
Page 314 and 315:
Overview of Immune System 301 demon
Page 316 and 317:
Page 318 and 319:
Page 320 and 321:
Overview of Immune System 307 some
Page 322 and 323:
Overview of Immune System 309 sera
Page 324 and 325:
Page 326 and 327:
Overview of Immune System 313 measu
Page 328 and 329:
Overview of Immune System 315 42. H
Page 330 and 331:
Overview of Immune System 317 74. D
Page 332 and 333:
15 Potential Target Antigens for Im
Page 334 and 335:
Tumor Antigen Discovery 321 develop
Page 336 and 337:
Tumor Antigen Discovery 323 panel b
Page 338 and 339:
Tumor Antigen Discovery 325 16. Joc
Page 340 and 341:
16 Identification of Tumor Antigens
Page 342 and 343:
Immunoproteomics 329 by “shot gun
Page 344 and 345:
Immunoproteomics 331 isoelectric fo
Page 346 and 347:
Immunoproteomics 333 3. 2D-PAGE is
Page 348 and 349:
17 Protein Arrays A Versatile Toolb
Page 350 and 351:
Protein Arrays 337 Table 1 Classifi
Page 352 and 353:
Protein Arrays 339 fractions from c
Page 354 and 355:
Protein Arrays 341 combinatorial sc
Page 356 and 357:
Protein Arrays 343 The ideal proteo
Page 358 and 359:
Protein Arrays 345 18. Cekaite, H.
Page 360 and 361:
Protein Arrays 347 49. Yuko Kawahas
Page 362 and 363:
Index 349 Index A Acetyl-CoA carbox
Page 364 and 365:
Index 351 conditional by using FRT
Page 366 and 367:
Index 353 Recombinant tumor antigen
show all

Target Discovery and Validation Reviews and Protocols

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?