Boolean Networks and Boolean Networks and Experiment DesignExperiment Design

B-Cell Single Ligand ScreenB-Cell Single Ligand Screen

Stuart JohnsonBioinformatics and Data Analysis

LabUCSD

Outline

• Why Boolean networks? • Building/Displaying Boolean

Networks• Experiment design• Procedure• Some competing (sub)networks

from the B-Cell data• Conclusions

Why try Boolean

Networks?

Data•noisy•partial sampling

ModelBiochemical

system•lots of complexity•predictive •lots of meaning

doableforward

problem

very difficultinverse

problem

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

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

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

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.

Boolean data

Exp

eri

men

tal co

nd

itio

ns

TIME

P-P,2nd Msg

Node=Full column of data; all exp. cond.

Known ligand/ receptor interactions from AfCS ligand descriptions

Inputs, etc.E

xp

eri

men

tal C

on

dit

ion

sGq

Single ligand screen inputs

? ExtractingpatternsCa (.5 min)

ELC

LPA

AIG

Exp

eri

men

tal C

on

dit

ion

sconsistent?

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)

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

Constructing complete networks

I1 I2 I3

N1 N2 N3

5 7 3 = 105networksmaximum

x x

Input nodes

nodes with truth tables

Constructing complete networks

I1 I2 I3

N1 N2 N3

Constructing complete networks

I1 I2 I3

N1 N2 N3

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”

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

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

Dual-ligand experiment design

ligand 2

ligan

d 1

en

trop

y s

core

Dual-ligand experiment design

ligand 2

ligan

d 1

en

trop

y s

core

ELC + LPA

Procedure

BuildBoolean Networks

Do Experiments

DisplayBoolean

Networks

Score classof experiments

pick highest scoring exp.

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

• 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

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”

LIG

2M

PP

Early Calcium

vs ...

Early Calcium+ cAMP

ER1 -> ER2,P90

LIG

2M

PP

PP1

P90 -> AKT

ST6 -> ST3

RCP

LIG

2M

PP

Early Ca & Gqcontrol vs ...

Early Ca& G12

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