Boolean Networks and Experiment Design B-Cell Single Ligand Screen
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Transcript of Boolean Networks and Experiment Design B-Cell Single Ligand Screen
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Boolean Networks and Boolean Networks and Experiment DesignExperiment Design
B-Cell Single Ligand ScreenB-Cell Single Ligand Screen
Stuart JohnsonBioinformatics and Data Analysis
LabUCSD
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Outline
• Why Boolean networks? • Building/Displaying Boolean
Networks• Experiment design• Procedure• Some competing (sub)networks
from the B-Cell data• Conclusions
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Why try Boolean
Networks?
Data•noisy•partial sampling
ModelBiochemical
system•lots of complexity•predictive •lots of meaning
doableforward
problem
very difficultinverse
problem
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Why try Boolean
Networks?
Boolean data
Boolean networks•some complexity•predictive (exp. design)•data-like•meaning? consistency = causality; should tell us about connectivity
easy
easy
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Boolean data
Exp
eri
men
tal co
nd
itio
ns
TIME
P-P,2nd Msg
red=1at 99%confidence:P(d=NC)<.01
blue=0everythingelse
2nd msg / co-sampled Ca
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Boolean data
Exp
eri
men
tal co
nd
itio
ns
TIME
P-P,2nd Msg
red=1at 99%confidence:P(d=NC)<.01
blue=0everythingelse
Phosphoproteins
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Boolean data
Exp
eri
men
tal co
nd
itio
ns
TIME
P-P,2nd Msg
late resp.
Ca -> PP
early resp.
Ca,cAMP -> No PP
groups ofsiml. resp.
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Boolean data
Exp
eri
men
tal co
nd
itio
ns
TIME
P-P,2nd Msg
Node=Full column of data; all exp. cond.
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Known ligand/ receptor interactions from AfCS ligand descriptions
Inputs, etc.E
xp
eri
men
tal C
on
dit
ion
sGq
Single ligand screen inputs
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? ExtractingpatternsCa (.5 min)
ELC
LPA
AIG
Exp
eri
men
tal C
on
dit
ion
sconsistent?
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Graph
Time
Exp
eri
men
tal C
on
dit
ion
sdisplaying
and encodingpatterns
LPA0
Ca0.5
ER12.5
0 0 0
1 0 ?
0 1 1
1 1 0
TruthTable
ERK1 (2.5 min)
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all hypotheses:ER1(2.5 min)
H1,H2 & H3: Early calcium is associated with ER1
H1: LPA is special (causes an early Ca signal but no ER1)
H2: M3A is special (0.5 min Ca, no 1 min Ca, but ER1)
H3: no special ligands, ER1 consistent with Ca & cAMP
H1 H2
H3
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Constructing complete networks
I1 I2 I3
N1 N2 N3
5 7 3 = 105networksmaximum
x x
Input nodes
nodes with truth tables
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Constructing complete networks
I1 I2 I3
N1 N2 N3
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Constructing complete networks
I1 I2 I3
N1 N2 N3
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Constructing complete networks
I1 I2 I3
N1 N2 N3
•“Feedback” not allowed! a completely determined network can have multiple output states; forward and inverse problems no longer “easy”
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Experiment Design: networks reproduceresults of completed experiments
1 output state
All networks: 1 possible output state:
•For known inputs, every network simply reproduces results of completed experiments
•(Information) Entropy = score = 0
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Experiment Design: networks are predictive
3 output states
All networks: multiple possible output states:
•these multiple states correspond to unknown entries (?) in truth tables and the
different connectivity of the networks
•Entropy = score > 0
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Dual-ligand experiment design
ligand 2
ligan
d 1
en
trop
y s
core
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Dual-ligand experiment design
ligand 2
ligan
d 1
en
trop
y s
core
ELC + LPA
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Procedure
BuildBoolean Networks
Do Experiments
DisplayBoolean
Networks
Score classof experiments
pick highest scoring exp.
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Controlling Complexity: Constraint Graphs
• Graphs specify allowable inputs and hops
RCP
LIG
2M
PP
LIG
2M
PP
LIG
2M
PP
PP
1
RCP
LIG
2M
PP
PP
1
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• Graphs specify allowable inputs and hops
RCP
LIG
2M
PP
LIG
2M
PP
LIG
2M
PP
PP
1
RCP
LIG
2M
PP
PP
1
Controlling Complexity: Constraint Graphs
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RCP
LIG
2M
PP
PP1
Network display
All node rules
Can filter/cluster/display these rules to see:
•ligand classification (chemokines, cytokines, etc)
•clusters of similar control patterns
•etc. - “pathways”
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LIG
2M
PP
Early Calcium
vs ...
Early Calcium+ cAMP
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ER1 -> ER2,P90
LIG
2M
PP
PP1
P90 -> AKT
ST6 -> ST3
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RCP
LIG
2M
PP
Early Ca & Gqcontrol vs ...
Early Ca& G12
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Conclusions• This is a general method/implementation and
will extend to the RAW screens and FXM in some form
• Boolean network analysis has many interesting features:– learns from experiments/proposes new exp.– formalizes inclusion of known information as either
constraint graphs or hidden nodes– caveat 1: the BN have encoded any real meaning– caveat 2: you can control complexity and digest the
networks inferred
• http://dev.afcs.org:12057/ for the latest results, navigable/clickable networks and more background