Target Discovery and Validation Reviews and Protocols

Gene Networks 37
Table 1
List of 120 Disrupted Genes
ACE2, ACH1, ADR1, ARGR2, ASG7, ASH1, BAR1, BEM4, BGL2, BOI1, BTT1,
BUD4, BUD6, BUD8, BUD9, CAD1, CAD1, CAT8, CIN5, CNB1, CPR6, CUP9,
CWP1, DAL82, DOT6, DST1, ECM22, EGD1, EGD2, FIR1, FZF1, GAL4, GAL80,
GAT3, GCN4, GCS1, GZF3, HAC1, HAP2, HAP3, HAT2, HMS2, HMS2, HPA2,
HPA3, HST2, HST4, HTA2, HTB2, INO2, INO4, KRE1, LEU3, LSM1, LYS14,
MAK31, MBR1, MET28, MIG2, MRS1, MUC1, NRG1, OPI1, OSH1, PIP2, RCK2,
RGM1, RGT1, RIC1, RLM1, RME1, RMS1, RPD3, RSC1, RTG1, SCW10, SCW11,
SCW4, SDS3, SFL1, SIP2, SKI8, SKN7, SMP1, SPS18, SPT23, SPT8, STB1, STB3,
STB4, STB6, STD1, SUM1, SWI5, SWI6, SWR1, TEA1, THI2, THI20, TPK1,
TSP1, TUB3, UME1, UME6, YAP7, YAR003W, YBL036C, YBL054W, YDR340W,
YER028C, YER130C, YFL052W, YLL054C, YLR266C, YML076C, YML081W,
YNR063W, YPL060W, YPR125W, YPR196W
bias, and so on. For extracting reliable information from microarray data, we
need to perform procedures called normalization on such biased data to remove
any systematic and artificial biases. We performed the lowess normalization
method to expression values in each pen group and then also globally normalized
the microarray-transformed intensities by the lowess method. The former
method aims at removing the position-specific bias, and the latter method aims
at removing the intensity-dependent bias. For microarray data normalization,
Quackenbush (15) gives an excellent overview.
3. Methods
3.1. Virtual Gene Technique for Identifying Drug-Affected Genes
3.1.1. Virtual Gene Technique
The first task is to find genes directly affected by the drug (Fig. 1, step 1).
For this purpose, the most straightforward approach might be the fold-change
analysis of the drug response microarray data. However, to perform more accurate
screening of the candidate drug-affected genes, we use another method,
virtual gene technique, which is more suited for inferring qualitative relations
between genes. We regard the drug as a “virtual gene,” and we consider that the
state of this virtual gene is 1 (ON) if the drug is dosed; otherwise, it is 0 (OFF).
The idealistic approach for identifying the genes directly affected by the virtual
gene may be to use the Boolean network model and to apply the method
developed by Akutsu et al. (1) for inferring Boolean network model that can
suggest a series of mutants for identifying the network. However, it requires
multiple disruptions and overexpressions for one mutant, and the number of
mutants required for identifying the network is not realistic, even for a small
network. Therefore, this approach is not in our scope, because we can deal with