A Transcriptional Regulatory Network Discovery System

1
A Transcriptional Regulatory Network Discovery
System   
    P. Ortoleva, L. Ensman, K. Qu, F. Stanley,
J. Sun, M. Trelinski and K. Tuncay Center for
Cell and Virus Theory, Indiana University http//b
iodynamics.indiana.edu  
Overview
Application to B Cell
The method is applied to a comprehensive set of
expression data on B cell and a preliminary TRN
that included 1,335 genes, 443 transcription
factors (TFs) and 4032 gene/TF interactions.
Predictions were obtained for 443 TFs and 9,589
genes. 14,616 of 4,247,927 possible gene/TF
interactions scored higher than the imposed
threshold. Results for three TFs, E2F-4, p130 and
c-Myc, were examined in more detail to assess the
accuracy of the integrated methodology. Although
the training sets for E2F-4 and p130 were rather
limited, the activities of these two TFs were
found to be highly correlated and a large set of
co-regulated genes is predicted. These
predictions were confirmed with published
experimental results not used in the training
set. A similar test was run for the c-Myc TF
using the comprehensive resource
www.myccancergene.org. In addition, correlations
between expression of genes that encode TFs and
TF activities were calculated and showed that the
assumption of TF activity correlates with
encoding gene expression might be misleading. The
constructed B cell TRN, and scores for individual
methodologies and the integrated approach are
available at systemsbiology.indiana.edu/trndresult
s.
Discovering the network of biochemical processes
underlying the behavior of Geobacteria and other
microbes is obtained by creating a suite of
interoperable systems biology modules. The
workflow takes multiplex bioanalytical data as
input, discovers the transcriptional regulatory
network (TRN) and other process networks, and
then uses cell simulation to derive microbial
behavior, notably the biotechnical
characteristics in the context of environmental
remediation and energy production.  To attain
this goal we integrate a number of
bioinformatics, cell modeling, and multiplex
data/model integration tools. We have started
this project with a TRN discovery system (a
preliminary version is at http//systemsbiology.in
diana.edu). Input to this system is microarray
data on gene expression profiles generated by the
bacterium in response to thermal, chemical, or
gene insertion/deletion perturbations. A database
provides a preliminary TRN which provides serves
as a training set for the systems biology
modules.
Comparison of the probability distributions of GO
similarity scores of the training set (triangle
markers) and the random set (square markers). The
training set consists of all known gene/TF
interactions for those genes with GO terms
assigned. The random set consists of all possible
gene/TF interactions for those genes with GO
terms assigned. It is seen that higher GO
similarity score implies higher likelihood of a
gene/TF interaction, particularly when the GO
similarity score is larger than 9.
Probability density functions of combined scores
for the training set (solid) and the random set
(dashed). It is seen that higher combined score
implies higher likelihood of a gene/TF
interaction.
Scatter graph of c-myc expression level and the
predicted activity of c-Myc TF. The linear
correlation coefficient is 0.49. The c-Myc
activity was constructed using a training set of
44 genes.
Responsive genes from a gene-expression
experiment initiate a query to extract an a
priori TRN (training set) from our database. This
preliminary TRN is used by our transcription
factor-based microarray interpreters and
bioinformatics modules as a training set. The
results of the individual modules are integrated
via a Bayesian approach to discover TF/gene
regulatory interactions.
a) Scatter graph of E2F-4 and RBL2 expression
levels. The linear correlation coefficient is
-0.36. Clearly, there is little relationship
between the two sets of expression data. b)
Scatter graph of the predicted E2F-4 and p130 TF
activities. The linear correlation coefficient is
found to be -0.80. The training sets of E2F-4 and
p130 included 12 and 43 interactions,
respectively. Only three of the genes were
coregulated by both TFs.
Application to E.coli
We apply the methodology to E.coli as it is
believed to have the most well understood TRN
therefore it serves as an excellent test case.
However, out of roughly 4300 genes and around 300
predicted TFs, the current E.coli TRN (from
EcoCyc and RegulonDB) includes only 984 genes and
144 TFs. Hence, it is clear that we only know a
fraction of the network. Out of 2007 gene/TF
interactions, 1124 were up regulation, 766 were
down regulation, 5 were uncertain, and 112 were
dual regulation (both up/down). The probability
distributions of the integrated confidence score
for the training and complete gene/TF sets are
shown below.The suggested TRN includes 3694 new
gene/TF interactions. After we performed the
calculations we found 206 more gene/TF
interactions in the RegulonDB and EcoCyc
databases that were not included in the training
set. 44 out of 206 regulatory interactions were
predicted by our methodology. We obtained the
p-value for predicting at least 44 out of 206
gene/TF interactions to be less than 1.0e-50
(expected proportion3.5e-04, number observed44,
sample size3694).
Probability distributions for the number of
gene/TF interactions per gene. Like most
biological interaction networks, the E.coli
network seems to follow a power law (scale free)
distribution, suggesting that TRNs tend to be
connected among high-degree nodes and low-degree
ones Although the suggested TRN is denser, the
overall shape of the probability distribution
remains the same.
TF Activity-Based Expression Data Analysis
Network inference using a similarity measure
assumes that the activity of a transcription
factor (TF) is represented by the expression of
the gene that makes it. Failure to observe high
correlation between mRNA level and TF activity in
E.coli shows that this assumption does not hold.
Therefore, in order to use expression data, we
estimate the TF activities independent of
expression level of the mRNA that translates into
the TF. To accomplish this, we developed a novel
algorithm to predict TF activities from
expression levels of all genes that the TF
regulates.