reconstructing dynamic regulatory networks in multiple species02710/lectures/dremreg.pdfmethods for...
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Reconstructing dynamic regulatory
networks in multiple species
02-710
Computational Genomics
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Methods for reconstructing
networks in cells
Amit et al
Science 2009
Gerstein et al Science 2010
SLT2
CRH1
YPS3 YPS1
SLR3
Pe’er et al Recomb 2001, Segal
et al Nature Genetics 2003
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Key problem: Most high-
throughput data is static
DNA
motif CHIP-chip
PPI microarray
Static data sources Time-series measurements
Time
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Method: Integrating time series
expression and static protein-DNA
interaction data
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TF C
time
Expression
Level
Model Structure
time
1
0.1
0.9
1
0.95
0.05
Expression
Level
Time Series Expression Data Static TF-DNA Binding Data
IOHMM Model
TF A
TF B
TF D
?
?
a b
c d
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Things are a bit more
complicated: Real data
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A Hidden Markov Model
T
t
tt
n
i
T
t
tt iHiHpiHiOpOHL2
1
1 1
))(|)(())(|)(();,(
Hidden States
Observed outputs
(expression levels)
t=0 t=1 t=2 t=3
H0 H1 H2 H3
O0 O1 O2 O3
Schliep et al Bioinformatics 2003
1
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Sum over
all genes
Sum over
all paths Q
Product over all
Gaussian emission
density values on path
Product over all transition probabilities on path
Input – Output Hidden
Markov Model Input (Static transcription factor-
gene interactions)
Hidden States Variables (We constrain transitions
between states to form a
tree structure)
Output State Variables (Gaussian distribution for
expression values)
Ig
t=0 t=1 t=2 t=3
H0 H1 H2 H3
O0 O1 O2 O3
Bengio and Frasconi, NIPS 1995
Log Likelihood
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Results: Yeast response
pathways
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Application to AA starvation in
yeast with condition specific data Expression data: Gasch et al Mol. Bio. Cell. 2000,
Chip-chip data: Harbison et al Nature 2004
Ernst et al Nature MSB 2007
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Application to AA starvation in
yeast with condition specific data
Amino acid transport 10-9
Ribosome biogenesis and assembly 4*10-21
Protein biosynthesis 3*10-72
Cellular Carbohydrate Metabolism 4*10-13
Nitrogen Compound Metabolism 5*10-23
Nucleotide Biosynthesis 2*10-12
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Application to AA starvation in
yeast with general binding
and motif data
new predictions
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Validating predicted interactions
for Ino4 Ino4 Occupancy in Gene Promoter Region at 0h and
4h
0
2
4
6
8
10
12
YDR497C YNL169C YGR196C YHR123W
Gene
Inte
ns
ity
SCD
AA Starvation
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Validating predicted interactions
for Ino4 AA vs. SCD Binding for Ino4 Bound Genes on
Response Path (Repeat 1)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 1 2 3 4 5
SCD Repeat 1: -log base 10 p-value
AA
Rep
eat
1:
-lo
g b
ase 1
0
p-v
alu
e
• Ino4 regulates phospholipid biosynthesis
• Many genes in the path are known lipid metabolism genes (GO p-value
6*10-5).
• May be connected to the need of membrane used for the
autophagocytosis process which regulates equilibrium between proteins
and the diminishing set of amino acids
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1 2
3
4
5
6 7 8
9
Stress and hormone response
MSB 2007, PLoS
Comp. Bio. 2008,
eLife 2013
MSB 2011
IRF7
Fly development Science 2010
Genome Research 2010,
PLoS ONE 2011, Nature
Immunology 2013, ISMB
2013
Immune response
Stem cells differentiation
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microRNAs
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• Pearson-correlation (Cheng et al. 2008,
Huang et al. 2011)
• Regression with graphical models,
GenMiR++ (Huang et al. 2007)
expre
ssio
n
time
miRNA-target detection using expression data
expre
ssio
n
time
global association no association
miRNA
mRNA
time 1-2 time 2-3 time 3-4
miR1
mRNA1
expre
ssio
n
time
time-specific association
time is not implicitely modeled
no prediction of time-specific regulation
Disadvantages
methods often assume linear correlation
joint modeling of TF regulation seldom possible
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- Incorporate miRNA expression ratios with logistic function to generate
dynamic input map for miRNAs
-Enforce positivity constraints for miRNAs coefficients in the logistic
regression model (convex optimization, still global optimum)
Incorporating miRNAs into DREM
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Dynamic regulaotry models for
lung development in mice
- Down regulated miRNA
- Up regulated miRNA
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Validating miRNAs controling Week
1 – Week 2 transition miRNA sign. paths
opposite
direction
corrected
enrichment
p-values
miR-125a-5p A,B,C+E 0.0108,0.00008,0.0
3872
miR-337-5p B,C+E 0.0332, 0.00008
miR-467c D < 10-6
miR-466a-3p D 0.05152
miR-466d-3p D 0.03904
miR-30d H 0.01456
miR-30a - -
miR-23b - -
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From development to disease Analysis of significant miRNAs in
patients with Idiopathic Pulmonary
Fibrosis (IPF)
10 control
vs 10 IPF
(TissueBank
)
28
control
vs 33
IPF
(Tissue
Bank)
142 control vs
162 IPF
(LGRC)
bleomycin
at 14d ( 3
replicates)
Liu et al
data J. Ex.
Med. 2010 miR-125a down - - down miR-30a down down down down miR-30d down down down down miR-467c - - - - miR-337 - up up - miR-466a-
3p - - - down
miR-466d-
3p - - - down
Schulz et al PNAS 2013
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Analysis of significant miRNAs in
patients with Idiopathic Pulmonary
Fibrosis (IPF)
10 control
vs 10 IPF
(TissueBank
)
28
control
vs 33
IPF
(Tissue
Bank)
142 control vs
162 IPF
(LGRC)
bleomycin
at 14d ( 3
replicates)
Liu et al
data J. Ex.
Med. 2010 miR-125a down - - down miR-30a down down down down miR-30d down down down down miR-467c - - - - miR-337 - up up - miR-466a-
3p - - - down
miR-466d-
3p - - - down
Schulz et al PNAS 2013
From development to disease
Development
Disease
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E. coli
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Escherichia coli
Regulatory Network
• Comprehensive genome wide binding data is currently not available for
most transcription factors
• Key transcription factor activate some genes and repress others
– Direction of interaction should also be part of the input to DREM
• 25% of genes have at least one known regulator based on curated
small scale experiments
– No confirmed negative data
• Expression and motif data also available
E. coli Image from Wikipedia
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Using unlabeled data helps
supervised learning Consider setting:
• Set X of instances drawn from unknown distribution P(X)
• Wish to learn target function f: X Y
Given:
• iid labeled examples
• iid unlabeled examples
Determine:
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Binary Logistic
Regression Classifier 3-Way Logistic
Regression Classifier
P(activated)+P(repressed) P(regulated)
Motif Exp 1 Exp 2 … Exp p Label
Gene 1 8.0 1.2 -0.5 … 0.4 activated
Gene 2 6.2 -0.4 1.0 … 2.0 repressed
Gene 3 7.0 -0.8 1.2 3.2 unknown
… … … … … …
Gene N 2.2 0.4 1.4 … -1.4 unknown
Binary Logistic
Regression Classifier “Meta” Classifier
P(regulated)
Self-training rule to change „unknown‟ labels to either activated or repressed
P(regulated) > 2 x (#activated + #repressed)/N
SEmi-supervised REgulatory Network
Discoverer (SEREND)
Expression Data from (Faith et al, PloS Biology 2007); Curated Interactions from EcoCyc; Motifs from Regulon DB
Ernst et al., PLoS Comput Biol, 2008
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0
No Self-Training Input Labels
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Labels for Final
Classification
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After Self-Training
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Labels for Final
Classification
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Legend
The Self-Training Step
0
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Aerobic-anaerobic shift response in
E. coli
2 1
6
5 4 3
9 7 8
1 2
3
4
5
6 7
8
9
Time
Expression Level
(log base 10)
1 activator;
-1 repressor
Beg et al PNAS 2007, Ernst et al Plos Comp. Bio. 2008
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DREM is useful, but several
questions remain … Who controls the
master regulators?
Who controls the
master regulators?