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Unraveling condition specific gene
transcriptional regulatory networks in
Saccharomyces cerevisiae
Speaker: Chunhui Cai
Background
Gene expression and transcription factors binding data
have been used to reveal gene transcription regulatory
networks. Existing knowledge of gene regulation can be
presented using gene connectivity networks. However,
these composite connectivity networks do not specify the
range of biological conditions of the activity of each link
in the network.
Therefore, it would be highly advantageous to develop a
computational approach for predicting the biological
conditions under which each regulator-target gene link in
the known regulator network is active.
Background
The author presents an approach based on a
variant of the signature algorithm to extend
known transcriptional regulatory network.
United Signature Algorithm – USA
Integrates binding and expression information, enables
us not only to identify new condition-specific
transcriptional and regulatory interactions, but also
predict the conditions in which each link in the known
regulatory network is active.
• Infer the activity status of every link in the composite network at different biological states
• Predict new regulatory links not present in the composite network at different biological states
Key issue – Predicting novel and condition-specific
transcriptional regulatory interactions
The author integrates two complementary local network
models for predicting: (a) new transcriptional regulatory
interactions, (b) specific conditions in which experimental
known and predicted interactions take place.
• LINK model – based on the assumption that there is a
correlation between the mRNA levels of a TF and its target
gene under certain condition;
• STAR model – deduce conditions of activity or inactivity of a
TF from the correlation among its target genes.
Numerical result (Alon’s TR network)
LINK
STAR
overlap
New interaction
predicted
160
252
53
Predictions overlap
with ChIP-on-chip
data at binding pvalues<0.005(0.001)**
64(41)
126(77)
X
Predictions have other
literature support
6
14
X
** -- only for the binding sites that are conserved in at least three related yeast species.
Extracting condition-specific transcriptional
regulatory networks
 The authors aggregated the conditions from the
gene expression compendium in to four categories:
1.
2.
3.
4.
Cell cycle;
Amino acid starvation
Rapamycin treated
Alpha-factor treated
Data types and sources

S. cerevisiae gene expression data
J. Ihmels, S. Bergmann, and N. Barkai, "Defining transcription modules using large-scale gene expression data," Bioinformatics,
vol. 20, pp. 1993-2003, 2004.
The number of experimental conditions in this compendium is 1,011 of which we selected the 387 conditions associated with
diploid cell.

S. cerevisiae transcriptional regulatory network (Derived from YPD database)
R. Milo, et al, "Networkmotifs: simple building blocks of complex networks," Science, vol. 298, pp. 824-7, 2002.
M. C. Costanzo, et al, "YPD, PombePD and WormPD: model organism volumes of the BioKnowledge library, an integrated
resource for protein information," Nucleic Acids Res, vol. 29, pp. 75-9, 2001.
The TF-target pairs whose regulatory relationships alternate between stimulatory and inhibitory control are excluded. Genes
missing from the expression data were also excluded, leaving us with 6,206 genes of which 115 are TFs.

Transcription regulatory binding motifs and probe sequences extracted from ChIP-on-chip experiments (to predict transcriptional
regulatory interactions by calculating binding ratio and the associated P-value for each TF)
C. T. Harbison, et al, "Transcriptional regulatory code of a eukaryotic genome," Nature, vol. 431, pp. 99-104, 2004.
T. I. Lee, et al, "Transcriptional regulatory networks in Saccharomyces cerevisiae," Science, vol. 298, pp. 799-804, 2002.
T. R. Hughes, et al, "Functional discovery via a compendium of expression profiles," Cell, vol. 102, pp. 109-26, 2000.

Standard symbols and aliases for genes
H. W. Mewes, et al, "MIPS: analysis and annotation of proteins from whole genomes," Nucleic Acids Res, vol. 32 Database issue,
pp. D41-4, 2004.
MIPS Comprehensive Yeast Genome Database

Yeast promoter sequences data
C. T. Harbison, et al, "Transcriptional regulatory code of a eukaryotic genome," Nature, vol. 431, pp. 99-104, 2004.
Method – USA
 The United Signature algorithm (USA)
-- This algorithm is designed to find a subset
of conditions in which the input genes are coregulated, and to identify additional genes in that
are potentially co-regulated under the same subset
of conditions.
United Signature Algorithm
The order of the procedures performed in the USA:
1.
bi-normalization and log
transformation of the raw
expression data (Egc);
2. selection of an input set of gene expression profiles consisting of the
target gene and its TF(LINK MODEL), or the expression profiles
of the target gene and all the other known regulated genes
controlled by the TF(STAR MODEL);
For a TF target gene pair (e.g. TF 
Gene2) input expression profiles of
the STAR or LINK model:
i) all the known genes regulated by
this TF (e.g. Gene2,4,6)
ii) the TF – target gene pair
3. Invert normalized expression profiles of input genes with inhibitory
relationship to the TF;
Egc’ = wgEgc’
where wg is -1 if the regulatory relationship between the input gene g and
the TF is inhibitory or +1 if it is stimulatory.
4. calculation of condition (column) scores by summing (or averaging)
the columns of a sub-matrix, whose rows represent the normalized
expression profiles of the input genes across all conditions;
5. Derive a united transcriptional module condition set – Ω.
where tc is a condition threshold value (tc = 1.5)
6. calculation of gene (row) defined as the weighted row average sg
across the selected conditions c;
7. determination of a sub-matrix of genes and conditions, termed the
united transcriptional module (UTM);
The genes are selected which satisfying the constraint:
where tg is a gene threshold value
(LINK MODEL – 4.0; STAR MODLE – 4.0)
8. Retain the genes whose pearson correlation coefficient with the
target gene or the regulator satisfy |R| > α across the experimental
conditions of the UTM.
α = 0.5 (LINK MODEL); α = 0.7 (STAR MODEL)
9. If needed (STAR MODEL), iterate steps 2 – 8 until all the genes in
the final UTM are highly correlated with the target gene associated
with the link of interest
LINK model
INPUT:
This model involves application of
USA to each link in the known
regulatory network, such that the
list of input genes consists of the
regulator and its target gene.
METHOD – USA:
To generate UTMs associated with
each link we used condition and
gene threshold of tc=1.5 and of
tg=4.0 respectively.
FILTERING:
1. The gene list is further filtered by keeping only those genes whose correlation
coefficient with the regulator (or target gene of interest) satisfied |R|>0.5 across
the experimental conditions of the UTM.
2. The match between the position-specific weight matrix (PWM) of the regulator
with the up stream intergenic region of each putative target gene and retain ed
genes with a MATCH score equal or greater than a predetermined cutoff β (0.94).
STAR model
INPUT:
USA is applied to an input set of genes including the target gene of the LINK model
and all the other target genes known to be regulated by the same TF (This model
is only applicable when the number of target genes is equal or greater than 2).
METHOD – USA:
To generate UTMs associated with each link we used condition and gene threshold of
tc=1.5 and of tg in a range of (3,4) respectively.
In order to obtain a cluster of highly correlated target genes, the authors used the
following iterative elimination scheme: First, they obtained a UTM and
computed all the correlations between its member genes across the conditions
contained in the UTM. Then they identified a centroid gene that has the largest
number of highly correlated genes. Input genes that are weakly correlated with
the centroid gene were eliminated from the next iteration. USA is iteratively done
until all the genes in the final UTM were highly correlated with each other
(|R|>0.7).
FILTERING:
Genes that belong to the final UTMs that have a high matching score with their
respective PWMs(0.94) are predicted to be targets of the corresponding TFs.
Discussion
 De-composition of composite network to condition specific networks which
contains both positive and negative interactions.
 The predictions are based on the integration of a prior transcriptional regulatory
network information with gene expression data as well as matching TF binding
data.
 For each regulatory link, we have a unique subset of conditions in which it is
expected to active.
 Each condition-specific ChIP-on-chip experiment is matched to expression
profiles generated under the most similar experimental enviroment.
 The author tried to deconvolution model combinatorial regulation between TFs,
but the model led to results with low overlap with ChIP-on-chip data.