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Transcript of Bayesian network models of Biological signaling pathways [email protected].
![Page 2: Bayesian network models of Biological signaling pathways karensachs@stanford.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062716/56649dd15503460f94ac661e/html5/thumbnails/2.jpg)
K. Sachs2
From Phospho-molecular profiling to Signaling pathways
High throughput dataR
af
Erk
p38
PKA
PKC
Jnk
PIP2
PIP3
Plc
Akt
...
Cell1
Cell2
Cell3
Cell4
Cell600
Signaling Pathways
Flow Measurments
Picture: John Albeck
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K. Sachs
Outline
What are signaling pathways?
What kind of data is available study them?
How do we use Bayesian networks to learn their structure?
Two extensions: Markov
neighborhood algorithm
Bayesian network based cyclic networks (BBCs)
3
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K. Sachs
Outline
What are signaling pathways?
What kind of data is available study them?
How do we use Bayesian networks to learn their structure?
Two extensions: Markov
neighborhood algorithm
Bayesian network based cyclic networks (BBCs)
4
![Page 5: Bayesian network models of Biological signaling pathways karensachs@stanford.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062716/56649dd15503460f94ac661e/html5/thumbnails/5.jpg)
K. Sachs5
Cell death ProliferationSecrete cytokines
Cells respond to their environment
Inside each cell is a molecular network
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K. Sachs6
“Central Dogma”
Translation
ProteinDNA
Transcription
mRNA
Modification
Modified Protein
‘Blueprint’- instructions
for production
of all proteins
Delivers instruction
s for specific gene
Ribosome: Protein-
production factory
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K. Sachs7
Signaling & Genetic pathways
A
B
C
A
BTF
DNA
RNA
C
Cell response
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K. Sachs
Outline
What are signaling pathways?
What kind of data is available study them?
How do we use Bayesian networks to learn their structure?
Two extensions: Markov
neighborhood algorithm
Bayesian network based cyclic networks (BBCs)
8
![Page 9: Bayesian network models of Biological signaling pathways karensachs@stanford.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062716/56649dd15503460f94ac661e/html5/thumbnails/9.jpg)
K. Sachs9
d[R]dt k1[LR]
k2[R][L]
...
Spectrum of Modeling Tools in Systems Biology
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K. Sachs10
Graph
Node: Measured level/activity of protein
Edge: Influence (dependency) between proteins
Conditional probability distributions
Each node has a conditional probability given its parents
Protein A
Protein B
Protein C Protein D
Protein E
Bayesian Networks
P(B|A=‘On’)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
-1 0 10 1 2
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K. Sachs
How do we use Bayesian Networks to infer pathways?
11
The Technical Details
BayesianScore (S) logP(S D)
logP(S) logP(D S) c
Score candidate models
Use a heuristic search to find high scoring models
... P(D,S)P( S)dn
1
... P(D, S)dn
1
P(DS)
(analytical solution!)
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K. Sachs12
Protein data
Western blot
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K. Sachs13
Protein data
Protein arrays
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K. Sachs14
Protein data
Mass Spectrometry
All of these lysate approaches give 1
measurement per protein for 10^3-10^7 cells
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K. Sachs15
Flow Cytometry: Single Cell Analysis
Thousands of datapoints
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K. Sachs16
MEK3/6
MAPKKK
PLC
Erk1/2
Mek1/2
Raf
PKC
p38
Akt
MAPKKK
MEK4/7
JNK
L
A
TLck
VAVSLP-76
RAS
PKA
1 2 3
CD28CD3
PI3K
LFA-1
Cytohesin
Zap70
PIP3
PIP2
JAB-1
Activators
1.-CD3
2.-CD28
3. ICAM-2
4. PMA
5. 2cAMP
Inhibitors
6. G06976
7. AKT inh
8. Psitect
9. U0126
10. LY294002
10
5
46
7
9
8
Stimulations and perturbations
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K. Sachs17
Datasets of cells• condition ‘a’• condition ‘b’•condition…‘n’
Raf
Mek
1/2
Erk
p38
PK
AP
KC
Jnk
PIP
2P
IP3
Plc
Akt
12 Color Flow Cytometry
perturbation a
perturbation n
perturbation b
Conditions (multi-well format)
T-Lymphocyte Data
Primary human T-Cells
9 conditions (6 Specific
interventions)
9 phosphoproteins, 2 phospolipids
600 cells per condition 5400 data-points
Omar Perez
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K. Sachs18
Statistical Dependencies
A
B
C D
E
Phosp
ho A
Phospho B
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K. Sachs19
Statistical Dependencies
Edges can be directed (primarily) due to the use of
interventions
A
B
C D
E
Phosp
ho A
Phospho B
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K. Sachs20
Overview
Influence
diagram of
measured
variables
Bayesian Network Analysis
Datasets of cells• condition ‘a’• condition ‘b’•condition…‘n’
Raf
Mek
1/2
Erk
p38
PK
AP
KC
Jnk
PIP
2P
IP3
Plc
Akt
Multiparameter Flow Cytometry
perturbation a
perturbation n
perturbation b
Conditions (multi well format)
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K. Sachs21
PKC
Raf
P44/42
Mek
Plc
PKA
Akt
Jnk P38
PIP2
PIP3
Phospho-Proteins Phospho-Lipids Perturbed in data
Inferred Network
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K. Sachs22
PKC
Raf
P44/42
Mek
Plc
PKA
Akt
Jnk P38
PIP2
PIP3
Phospho-Proteins Phospho-Lipids Perturbed in data
How well did we do?
Direct phosphorylation
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K. Sachs23
Features of Approach
Direct phosphorylation:
Mek
Difficult to detect using other forms of high-throughput data:
-Protein-protein interaction data
-Microarrays
Erk
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K. Sachs24
PKC
Raf
P44/42
Mek
Plc
PKA
Akt
Jnk P38
PIP2
PIP3
Phospho-Proteins Phospho-Lipids Perturbed in data
How well did we do?
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K. Sachs25
PKC
Raf
P44/42
Mek
Plc
PKA
Akt
Jnk P38
PIP2
PIP3
Phospho-Proteins Phospho-Lipids Perturbed in data
How well did we do?
Indirect Signaling
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K. Sachs26
Indirect signaling
Dismissing edges
Raf Mek Erk
PKC Jnk PKC Mapkkk Jnk
Not measured
Mek4/7
Indirect connections can be found even when the intermediate molecule(s) are not
measured
Indirect signaling
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K. Sachs27
Indirect signaling - Complex example
Is this a mistake?
The real picture
Phoso-protein specific
More than one pathway of influence
PKC Raf Mek
PKC Rafs259 Mek
Rafs497
Ras
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K. Sachs28
PKC
Raf
P44/42
Mek
Plc
PKA
Akt
Jnk P38
PIP2
PIP3
Expected Pathway
15/17 Classic
Phospho-Proteins Phospho-Lipids Perturbed in data
How well did we do?
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K. Sachs29
PKC
Raf
Erk
Mek
Plc
PKA
Akt
Jnk P38
PIP2
PIP3
Expected Pathway
Reported
Missed
15/17 Classic
17/17 Reported
3 Missed
Reversed
Phospho-Proteins Phospho-Lipids Perturbed in data
Signaling pathway reconstruction
[Sachs et al 2005]
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K. Sachs
Caveats
Inhibitor specificity Binding site similar
across proteins
Reagent availability and specificity
Data quality
These are issues in many biological apps!
30
I think I’ll bind here
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K. Sachs
Outline
What are signaling pathways?
What kind of data is available study them?
How do we use Bayesian networks to learn their structure?
Two extensions: Markov
neighborhood algorithm
Bayesian network based cyclic networks (BBCs)
31
![Page 32: Bayesian network models of Biological signaling pathways karensachs@stanford.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062716/56649dd15503460f94ac661e/html5/thumbnails/32.jpg)
K. Sachs32
Markov Neighborhood Algorithm
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K. Sachs33
Building larger networks
12 color capability Model 50-100 variables
4 color capability Model 12 variables
PKC
Raf
P44/42
Mek
PlcPKA
Akt
Jnk P38
PIP2
PIP3
~80 proteins involved in
MAPK signaling
(11- at the cutting edge- is NOT enough!)
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K. Sachs34
Measured subsets = Incomplete dataset (Missing data)
Insufficient information for standard approaches (will perform poorly)
Use a set of biologically motivated assumptions to constrain search..
And to reduce the number of experiments
( )11
4= 330
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K. Sachs35
Constraining the search
Plus potential perturbation parents
Identify candidate parents
Using ‘Markov neighborhoods’
(for each variable)
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K. Sachs36
Bayesian Network Analysis
(Constrained search)
Raf
Mek
1/2
Erk p38
PK
AP
KC
Jnk
PIP
2P
IP3
Plc
Akt
Molecules 1, 3, 7, 9
Molecules 2, 4, 7, 10
Molecules 1, 2, 6, 11
Approach overview
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K. Sachs37
Neighborhood reduction
CB
E
DA
F
4 color capability
Conditional independencies in the
substructure?ABC
411
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K. Sachs38
Accurate Reproduction of Model ~15 experiments, 4-colors
Confidence value different from original
model
PKC
Raf
Erk
Mek
Plc
Akt
Jnk P38
PIP2
PIP3
PKA
![Page 39: Bayesian network models of Biological signaling pathways karensachs@stanford.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062716/56649dd15503460f94ac661e/html5/thumbnails/39.jpg)
K. Sachs39
Raf
Mek
1/2
Erk p38
PK
AP
KC
Jnk
PIP
2P
IP3
Plc
Akt
Active learning approach
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K. Sachs
Outline
What are signaling pathways?
What kind of data is available study them?
How do we use Bayesian networks to learn their structure?
Two extensions: Markov
neighborhood algorithm
Bayesian network based cyclic networks (BBCs)
40
![Page 41: Bayesian network models of Biological signaling pathways karensachs@stanford.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062716/56649dd15503460f94ac661e/html5/thumbnails/41.jpg)
K. Sachs41
Learning cyclic structures with Bayesian networks
Biological networks contain many loops
Bayesian networks are constrained to be acyclic
So…
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K. Sachs
Overcoming acyclicity
Signaling pathways contain many cycles
Bayesian networks are constrained to be acyclic
How can we accurately model pathways with cycles?
42
GRB2/SOSGRB2/SOS
RafRaf
MEKMEK
ErkErk
RasRas
Develop a new, Bayesian network derived algorithm that models
cycles…
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K. Sachs
Bayesian Network Based Cyclic Networks (BBNs)
I. Break loops with molecule inhibitors
II. Use BN to learn the structure (now not cyclic!)
III. Close loops
43
GRB2/SOSGRB2/SOS
RafRaf
MEKMEK
ErkErk
RasRas
Mek inhibitor
Solomon Itani
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K. Sachs44
GRB2/SOSGRB2/SOS
RafRaf
MEKMEK
ErkErk
RasRas
I. Break loops with molecule inhibitors Detect loops P(A)A* ~= P(A)
II. Use BN to learn the structure (now not cyclic!)
III. Close loops
P(B|Pa(B)) A* ~= P(B|Pa(B))
AB
Bayesian Network Based Cyclic Networks (BBNs)
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K. Sachs45
Future work
Larger network from overlapping sets (Markov neighborhood)
Dynamic models over time
Differences in signaling (sub-populations, treatment conditions, cell types, disease states)
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K. Sachs46
Acknowledgements
Shigeru Okumur
a
Funding
LLS post doctoral fellowship
Solomon Itani
Garry Nolan
Dana Pe’er
Doug Lauffenburge
r
Omar Perez
Dennis Mitchell
Mesrob Ohannessia
n
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Extra slides
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Mathematical Intuition
BB CC C is independent of A given B.
AA
AA BB
CCDD
C independent of A given B and D
1) No need to introduce time!!!
2) When loops are broken, the result is a BN!!!
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K. Sachs49
Prediction: ErkAktErk1/2 unperturbed Erk Akt not well established
in literature
Predictions:
Erk1/2 influences Akt
While correlated, Erk1/2 does not influence PKA
PKC
Raf
Erk1/2
Mek
PKA
Akt
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K. Sachs50
Validation
control, stimulated
Erk1 siRNA, stimulated
SiRNA on Erk1/Erk2 Select transfected cells Measure Akt and PKA
100 101 102 103 104
APC-A: p-akt-647 APC-A100 101 102 103 104
PE-A: p-pka-546 PE-A
P-Akt P-PKA
P=9.4e-5 P=0.28