SCI2S Group

Departamento de Ciencias de la Computación e

Inteligencia Artificial

Escuela Técnica Superior de Ingeniería Informática

Universidad de Granada, Granada, Spain

Towards Modeling the Gene Regulation Problem

From static to dynamic … from bacterial

to human models

Groisman LabMicrobiology Department

Howard Hughes Medical Institute

Washington University School of

Medicine

St. Louis, Missouri USA

Cobb LabCellular Injury and Adaptation

Laboratory

Washington University in St.

Louis

St. Louis, Missouri USA

Computational Biology Group

Departamento de Ciencias de la

Computación

Facultad de Ciencias Exactas y Naturales

Universidad de Buenos Aires, Buenos

Aires, Argentina

AN EXPERIMENTAL-ORIENTED APPROACH TO

BIOINFORMATICS

Genotype

PhenotypeSydney Brenner

Science 287: 2173

“The analysis of genome sequences gives us a comprehensive

protein parts list…But there is one important piece of information

that is almost totally missing: the sequence information that

specifies when and where and for how long a gene is turned on or

off.”

SALMONELLA: A GRAM-NEGATIVE PATHOGEN

WITH A VARIED LIFESTYLE

Signal

Effectors

Regulator

Sensor

Response

SIGNAL TRANSDUCTION CASCADE

BY TWO-COMPONENT REGULATORY SYSTEMS

mgtB

Mg2+ transport

mgtA

Mg2+ transport

PhoQ��

PhoP

low Mg2+

-PO3

THE PHOP/PHOQ REGULATORY SYSTEM

IS ACTIVATED IN LOW Mg2+ ENVIRONMENTS

mgtB

Mg2+ transport

mgtA

Mg2+ transport

PhoQ��

PhoP

low Mg2+

-PO3

LOW Mg2+ INDUCES PMRA-ACTIVATED GENES

VIA THE PHOP/PHOQ-ACTIVATED PMRD PROTEIN

pmrDPmrD

low Mg2+

PhoQ��

PhoP -PO3

PmrB��

-PO3PmrA

pbgP

LPS modification

Fe3+ REPRESSES pmrD EXPRESSION

VIA THE PMRA/PMRB SYSTEM

high Fe3+

pmrDPmrD

low Mg2+

PhoQ��

PhoP -PO3

PmrB��

-PO3PmrA

pbgP

LPS modification

-10

mig-14

-10-35 +1PhoP-box

ATTGTTTATTTACTGTTTAGCGCGCGCGCTTGACAATTTTTATAAT

TRANSCRIPTIONAL REGULATION:

RECRUITING AND COOPERATING

THE PMRD PROTEINS OF SALMONELLA AND E. COLI

EXHIBIT UNUSUALLY LOW AMINO ACID IDENTITY

PmrD

PhoQ��

PhoP -PO3

PmrB��

PmrA -PO3

85.4% 93.3% 84.0% 90.5%55.3%

(the median amino acid identity between Salmonella and E. coli proteins is 90%)

-10+1

-35RcsB-box

PmrA-box

-10+1

-35RcsB-box

PhoP-box

PmrA-box

Salmonella

E. coli

THE ugd PROMOTER OF SALMONELLA,

BUT NOT OF E. COLI, HARBORS A PHOP BOX

THE E. COLI PMRA/PMRB SYSTEM

RESPONDS TO Fe3+ BUT NOT TO LOW Mg2+

high Fe3+

pmrDPmrD

low Mg2+

PhoQ��

PhoP -PO3

PmrB��

-PO3PmrA

pbgP

LPS modification

THE SALMONELLA PMRA/PMRB SYSTEM

RESPONDS TO Fe3+ AND LOW Mg2+

high Fe3+

pmrDPmrD

low Mg2+

PhoQ��

PhoP -PO3

PmrB��

-PO3PmrA

pbgP

LPS modification

-10+1

PhoP-boxPmrA-box

Salmonella

-10+1

PhoP-box

E. coli

THE pmrD PROMOTER OF SALMONELLA,

BUT NOT OF E. COLI, HARBORS A PMRA BOX

phoPphoQ

mgtA

Mg2+ transport

PhoQ

PhoP

PmrB

PmrA

SEQUENTIAL ACTIVATION OF THE PHOP REGULON

PmrD

mig-14 pmrD

pbgP

mgtCB pagP

pmrC pmrA pmrB

LPS modificationvirulence

PmrAPhoP

DNA

AT DIFFERENT TIMES,ADD CROSSLINKER,

LYSE CELLS, ANDFRAGMENT DNA

IMMUNOPRECIPITATION

ANTI-PhoP ANTI-PmrA

CROSS-LINKING REVERSAL AND DNA

PURIFICATION

CHROMATIN IMMUNOPRECIPITATION (ChIP) STUDY

OF PHOP- AND PMRA-REGULATED PROMOTERS

high Mg2+ low Mg2+

PCR SPECIFIC FORPhoP- OR PmrA-

REGULATED PROMOTERS

HMg2+

phoP

mgtA

pmrD

mgtC

pagP

pmrC

pbgP

promoters

IPtotal

IPtotal

IPtotal

IPtotal

IPtotal

IPtotal

IPtotal

0 5 10 20 30 60 90-time (min)L

ORDERED BINDING OF THE PHOP AND PMRA

PROTEINS TO THEIR TARGET PROMOTERS

-5 0 5 10 20 30 60 90

mgtC

pcgL

pagC

mig-14

pagP

pmrD

slyB

phoP

mgtA

rstA

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

TEMPORAL ORDER OF BINDING

ANATOMY OF BACTERIAL PROMOTERS

-35 -10+1

Class I promoter

-35 -10+1Class II promoter

-10+1

THE PHOP PROTEIN CONTROLS TRANSCRIPTION

FROM DIFFERENT CLASSES OF PROMOTERS

mgtAPhoP-box

-10+1

PhoP-boxmgtC

-35

-10+1

PhoP-boxPmrA-boxpmrD

ugd-10

+1-35RcsB-

boxPhoP-box

PmrA-box

. Quality of the binding site for the transcription factor

. Distance and orientation of the binding site relative to the

RNA polymerase binding site

. Quality of the binding site for RNA polymerase

. Presence of binding sites for other regulatory proteins

FACTORS DETERMINING TRANSCRIPTION

FROM A BACTERIAL PROMOTER

THE PHOP/PHOQ SYSTEM REGULATES EXPRESSION

OF ACID pH RESISTANCE GENES IN E. COLI

yhiWYhiW

low Mg2+

PhoQ��

PhoP -PO3

gadA

yhiE YhiE

hdeA

Gene Promoter Scan (GPS)

•Identifying regulatory profiles that summarize the behavior of the PhoP regulon

•Understanding the subjacent biological properties of the regulatory profiles

•Uncovering interactions with other regulatory systems and proteins

OUR ULTIMATE GOAL IS TO DEFINE THE

MOLECULAR LOGIC OF PHOP REGULATION BY …

REGULATORY FEATURES CONSIDERED INCLUDE …

TFS location & length

Genechip

PhoP-boxGene strand

NOT

PhoP-boxTFS-boxes

High down- regulated

Upnregulated

Low down-regulated

Direct repeat PhoP-motif

First-repeat damaged motif

Second-repeat damaged motif

etc…

Binding sites-learning methods

D

Clustering/Proto-typing methods

E. coli

Salmonella

Others

d

bc

a

etc…

PhoP-boxSigma 70

D

Expression

&

Expression

Sigma 70 sequences

e

Expression

0 20 40 60 80 100 120 140 160 180 2000

5

10

15

20

25

f

g

-200 -150 -100 -50 0 50 1000

10

20

30

40

50

60

70

80

90

-100 -90 -80 -70 -60 -50 -40 -30 -200

5

10

15

20

25

30

35

40

Promoter-learning methods

PhoP-box

Expression&

PhoP-boxPhoP-box

Sigma 70

D &

Orientation&TAtaaT

TTGaca

+1

DD/ORPhoP-box

Sigma 70D

wt1 wt2 wt3 wt4 PhoP-1 PhoP-2 PhoP-3 PhoP-4 0

0.5

1

1.5

2

2.5

3x 10

4

0.5

1

1.5

2

2.5

3x 10 4

wt1 wt2 wt3 wt4 PhoP-1 PhoP-2 PhoP-3 PhoP-4 0 0

0.5

1

1.5

2

2.5x 10

4

wt1 wt2 wt3 wt4 PhoP-1 PhoP-2 PhoP-3 PhoP-4

0.83 0.6 0.0

0.5

1

1.5

2

2.5

3x 10 4

wt1 wt2 wt3 wt4 PhoP-1 PhoP-2 PhoP-3 PhoP-4 0

Similarity to Average (rank)0 200 400 600 800 1000

0.2

0.4

0.6

0.8

1

1.2

1.4

Similarity to Average (rank)0 200 400 600 800 1000

0.2

0.4

0.6

0.8

1

1.2

1.4

Similarity to Average (rank)0 200 400 600 800 1000

0.2

0.4

0.6

0.8

1

GENE EXPRESSION FEATURES

T TGTTGATAATT TGTTTATM4

AT

ATGGTTTAC

TAATAAG

TGTTTAAM3

A TGTTTATGATTTGTCGTTTAAM2

T TGTTTACGTTC TATTGTATM1

TGTTTA_ _ _ _ _ TGTTTA

#5 TTGTTTATGATTTGTTTAA | 0 6 3 1 0 21 25 25 25 25 25 5 4 1 1 6 21C | 3 0 0 3 1 3 25 25 25 25 25 1 0 2 0 0 2G | 6 19 2 0 4 0 25 25 25 25 25 9 19 0 4 4 2T | 16 0 20 21 20 1 25 25 25 25 25 10 2 22 20 15 0

BINDING SITES SUBMOTIFS

mig-14-10PhoP-box -10-35 +1-10-35 +1-10-35 +1

PhoP-box

mig-14

-10-35 +1+1PhoP-box

PROMOTER FEATURES: RNA POLYMERASE, CLASS

AND LOCATION

1.0

0.8

0.5

0.0

-200 -150 -100 -50 0 50 100

Distance from binding sites to

+1

-100 -90 -80 -70 -60 -50 -40 -30 -200

5

10

15

20

25

30

35

40

Distance from binding sites to

+1

-200 -150 -100 -50 0 50 1000

10

20

30

40

50

60

70

80

90

Distance from binding sites to

+1

PROMOTER CONDITION: ACTIVATED/REPRESSED

AND DISTANCE RELATIONSHIPS

-100 -90 -80 -70 -60 -50 -40 -30

-200

5

10

15

20

25

30

35

40

- 100 - 90 - 80 - 70 - 60 - 50 - 40 - 30 - 20

0

-100 -90 -80 -70 -60 -50 -40 -30 -20

0

5

10

15

20

25

30

35

40

0.83 0.6 0.0

1.0

0.8

0.5

distance to the binding site

+1Binding-site -10-35 +1

-100 -90 -80 -70 -60 -50 -40 -30

- 100 - 90 - 80 - 70 - 60 - 50 - 40 - 30 - 20

-100 -90 -80 -70 -60 -50 -40 -30

0

5

10

15

20

25

30

35

40

0

5

10

15

20

25

30

35

40

CLOSE, MEDIUM AND REMOTE PROMOTERS

RNAPalpha

CRP

RNAPalpha

CRP

RNAPalpha

CRP

I II

RNAPalpha

CRP

III

BINDING SITE ORIENTATION AND POSSIBLE

TOPOLOGIES

mig-14-10PhoP-box -10-35 +1Fis-box MalT-box

Distances among transcription factor binding sites

0 20 40 60

80 100 120 140 160 180 2000

5

10

15

20

25

TRANSCRIPTION FACTOR INTERACTIONS

Gene X

Y-binding siteClass II Promoter

Class I Promoter

Gene Z

Y-binding site

Class II Promoter

W-binding site

high

expression

remote

close

close

medium distance

both tandem motifs

first tandem motif

both tandem motifs

A CONCEPTUAL CLUSTERING APROACH FOR

DISCOVERY OF REGULATORY PROFILES

ADVANTAGES OF CONCEPTUAL CLUSTERING

(Michalsky, Chesseman, Cook, Ruspini, etc.)

•Hypothesis generation – data exploration – model fitting – finding true topologies

•Deal with structural data

•Iterative and incremental search of interesting repetitive substructures

•Attributes and relations belong to more than one substructure

•Attributes and relations are flexible

•Ability to deal with missing values

•DISCOVER DESCRIBE -> PREDICT

direccionclose... clo... me... me... rem... remot... exp1-phop, ... exp2-gad...exp3-pyr, S c...

TGGTATTCACGAAAAGTTTATGTgttTAGTGACGTTCTGTTTAagtACATAGTTAGGCGCTGTTTAACTACATAGTTAGGCGCTGTTTAACTACATATTGCTCCACTGTTTATATcgcTATTGCCGTTTTGTTTAtccAGATATATAACGTCGGTTTATAATGTTATTTTTAGCCGGTTTAAATCACTATTGATGTTTGGTTAAGATctgTATTGACGATTGGTTAAtgtTGATATTTCGTTGAAGTTAATGATGCTATTTACAAGCTGATAACAActtTATTGAGGTTGTATTGAtaacttTATTGAGGTTGTATTGAtaaTGCCGTTGATAAAGAGTTTATCTcgcCATTGATAAACTGTTTAacatttCGTTTAGGTTTTGTTTAagtCAGCGTATAGCTTATGTTTATAACAGCGTATAGCTTATGTTTATAAtacGGTTTACTTTCTGGTTAaaaTCTGGTTTATCGTTGGTTTAGTTtctGGTTTATCGTTGGTTTAattGCTGGTTTATTTAATGTTTACCCtctGGTTTATTAACTGTTTAtccATTTGTTTAAGGAATGATTAATTCAGTTTTTAAAAACTGTTTAAAGCATTGTTTAGGTTTTGTTTAAGTCACTGTTTATATTTTGTTTAGTACATTGTTTAGGGTTTGTTTAATTtatTATTTACGGTGTGTTTAaactatTATTTACGGTGTGTTTAaacctaCAGTTACTCCTGGTTTAagttctCGTTTAGAAAAGATTTAtggcttCGTTTAAGATTGGTTAAttacttCGTTTAAGATTGGTTAAttaCATTAATTATGCAAAATTTATGGACCTGATGAAAACTTGTTTAGAAtcaTGATTATAGATTGCTTAttataaTGTTTCCTTATATTTTAaattcaTGTTTAAACACGCTTTAtttgttTGTTTAGATACGGTTTActtaaaTGTTTAAGCCCGGTTTAataaaaTGTTTAGCTTGTATTTAatgctgTGTTTAGAGAGAATTTAcatTTCTGTATATGTCATGTTGATGGTTCTGTATATGTCATGTTGATGGTTCTGTATATGTCATGTTGATGGctcTGTTTATAGTTTGTTAAgatctcTGTTTATAGTTTGTTAAgatcctCGTTTAACAATGGTTGAggaTTTTGTTTATAATTGGTTGATCCttcTGTTTAAGTTTGTTTGAtatACTTGTTTAGAAACGATTGATAGACTTGTTTAGAAACGATTGATAGGCTGGTTGAGCATTTGTTGAAAAggcGGTTGAGGGTTCGTTGAaaaggcGGTTGAGGGTTCGTTGAaaaTTTTGTTGATATTTCGTTGAAGTTTTTGTTGATATTTCGTTGAAGTcgaTGTTGTTAAACAGTTTAtcaATTTGTTGTTTCATTGTTAAAAAATTTGTTGTTTCATTGTTAAAAAtcgGGTTTATATGTTTTTAAcatcacGGTTGAGCAACTATTTActt

Ara...C... D... Fr... IH... M... Ox...YgiX-... a ctiva ted_repre ssed (clus ter)

P1

P2

P3

P4

E1

E2

E3

I1

I2

I3

I4

I5

yqjEyhdGygiW yaiNpyrIgadB gadAb3100dpsybjXslyBybjXmgtCompXpipDpagCpmrDyrbLhdeDyeaFnmpCybjXrstAyiaGpagPybjXybcUybjXyhiWphoPb2833pagChilApagDpagPmgtApgtEhdeDompXompXyrbLrstAhdeAslyBslyBmgtAudgb1826pmrDrstAphoPtrs5_8rstAybjXnmpCyobGpipDompTompTugtLmgtCybjXvirKvirKmig-14mgtCmgtCyeaFslyBb1825yiaGyhiWyhiE

pipD yhiWmgtC pagC mgtC mgtC virK virK pipD pagP pagD pagP mig-14 ugtL mgtC pagC yhiW phoP phoP yrbL rstA rstA rstA rstA yrbL ybjX ybjX ybjX slyB slyB pgtE mgtA mgtA yqjE ybjX ybjX ybjX ybjX b1826 b2833 slyB ompX ompX ompX yobG ompT ybcU slyB b3100 ompT hilA pmrD b1825 hdeD yiaG yhiE dps pmrD gadA yiaG gadB hdeD hdeA ygiW udg trs5_8 yhdG yaiN pyrI yeaF nmpC nmpC yeaF

yqjE yhdG ygiW yaiN pyrI gadB gadA b3100 dps slyB mgtC b1825 b1826 ompT pmrD b2833 nmpC ybjX ybjX ybjX yhiE pipD pipD pmrD virK yrbL yhiW yhiW ybjX mgtA mgtA phoP phoP yeaF trs5_8 yrbL ompT ybcU pagC pagC yobG rstA slyB slyB yeaF rstA pgtE mgtC mgtC ybjX udg mig-14 pagD hdeD hdeA hdeD pagP pagP nmpC slyB mgtC rstA rstA ompX ompX ompX ybjXybjX virK yiaG yiaG hilA ugtL

yqjE rstA rstA rstAb3100 pyrI gadB gadA ompX ompX ompX ygiW dps yhdG b1825 yaiN b1826 pagC pagC virK virK mig-14 yiaG yiaG rstA b2833 slyB slyB yhiE mgtC mgtC mgtC mgtC ybjX ybjX ybjX pgtE mgtA nmpC yhiW yhiW pipD pipD pmrD pagD ybjX ybjX ybjX ybjX slyB slyB ompT ompT ybcU hilA ugtL nmpC phoP phoP yobG pagP pagP hdeD hdeD hdeA mgtA trs5_8 yeaF yeaF yrbL udg pmrD

pyrIb3100gadAyhdGyaiNdpsygiWgadByqjEyeaFnmpChilAyhiWmgtCpagCyeaFpipDnmpCudgybjXybjXybcUmig-14ybjXybjXybjXybjXyhiWvirKvirKmgtCyhiEphoPpagPmgtAybjXpmrDyrbLyobGyiaGyiaGpgtEmgtCpagDmgtCslyBslyBphoPompXompTslyBmgtAyrbLrstApipDrstApmrDpagCompXrstApagPhdeDslyBb1826hdeArstAb2833hdeDompTb1825ompXugtLtrs5_8

M1

M2

M3

M4

M5

Expression Binding-site motifs Interactions Orientation Activated/Repressed

A1

A2

A3

A4

yqjEyhdGygiWyaiNpyrIgadBgadAb3100dpspagCpipDpagCugtLpipDhilAyhiWmgtCmgtCyhiWvirKmig-14yeaFybjXybjXybjXybjXybjXtrs5_8slyBompThdeAompTyrbLpagPyrbLmgtCyobGyiaGyiaGudgpgtEpagDvirKmgtCyhiEphoPpagPmgtApmrDyeaFybjXybjXybcUslyBslyBslyBrstArstArstArstAphoPompXompXompXnmpCnmpCmgtAhdeDhdeDpmrDb2833b1825

O1

O2

O3

THE BUILDING BLOCK PROFILES OF PHOP-

REGULATED GENES

Sigma 70 Promoters

THE COMBINED PROFILES OF PHOP-REGULATED

GENES

Learn features of profiles

Combine profiles

Predict and adapt profiles

Group Prototype Search

Group Prototype Search

Group Prototype Search

Evaluating profiles

Gene Promoter Scan: ALGORITHM

Small number of clusters

Large coverage

Good generality

Minimal overlapBetween clusters

More distinct clusters

Better defined concepts

Big cluster descriptions

More features

More inferential power

AN EVALUATION OF THE GENERATED COMBINED

PROFILES

Class I Promoter

medium medium

close

Class I Promoter

close

Class II Promoter

remote

Class I PromoterClass II Promoter

Class II Promoter

mgtC

Class II Promoterremote

close

Class I Promoterclose

Class II Promoter

remote

Class I PromoterClass II Promoter

Class I Promoter

remote

Oposite

mig-14

PhoP-binding site

PhoP-binding site

Oposite

f1 f2 f3 f4

f1,f2 f3,f4

f1,f2,f3 f1,f2,f4 f1,f3,f4 f2,f3,f4

f1,f3,f3,f4

PI=0.4SI=0.2

PI=0.2SI=0.4

PI=0.1SI=0.1

GPS: A CONCEPTUAL CLUSTERING METHOD

P0 P1 P2 P3

O0

O1

O2

promoters (cluster)

0.0002

47E-12

0.97 0.19

0.66 0.97

0.007

mig-14 pagC yeaF slyB yhiW virK ybjX mgtC

P0-O0

b3100 pyrIdps yaiNgadA ygiWgadB yhdGyqjE

P1-O2

b2833

ybjX

mgtA

phoP

ybcU

yeaF

yrbL

rstA

hdeD

ompX

rstA

slyB

yiaG

P1-O1

ybjX ybjXyhiW pipDhdeA pagCybjX pagChilA

P3-O1

slyB

yhiW

yeaF

mgtC

ybjX

mig-14

pagC

virK

mgtC

P3- O2

b1825yhiE

yhiWyiaGvirKmgtC

promoters (cluster)

orientation (cluster)

P2-O1

ompT

ompT

trs5_8

pipD

ybjX

ugtL

P2-O2

b1826

nmpC

pmrD

rstA

rstA

phoP

yobG

ybjX

pmrD

slyB

mgtA

yrbL

pagD

pgtE

udg

mgtC

nmpC

ompX

pagP

O2

O1

O0

P3

P2

P1

P0

9.04E-11 … 0.9729

Color by probability of interaction

0 … 29

Size by records count Line with by count

GPS EVALUATION: PROBABILITY OF

INTERSECTION

0.35 0.49 0.95 0.85 0.75 0.69

remoteclass I

close class II

close medium medium class I class II class I

remoteclass II

RNA Polymerase Features

00.050.10.150.20.250.30.350.40.450.50.550.60.650.70.750.80.850.90.95

remoteclass I

close class II

close medium medium class I class II class I

remoteclass II

slyB

ybjX

yeaF

mig-14

virK

yhiW

mgtC

mgtC

pagC

0

0.050.10.150.20.250.30.350.40.450.50.550.60.650.70.750.80.850.90.95

1

GPS EVALUATION: SIMILARITY OF

INTERSECTION

•Class II

•PhoP-binding sites consensus TGTTTANNNNNTGTTTA

•Direct binding motif repeat

THE PUBLISHED PHOP-REGULATED PROMOTER

(Yamamoto et al., 2002; Minagawa et al., 2003)

OUR ANALYSIS RECOVERED THE TYPICAL PHOP-

REGULATED PROMOTER

High Expression

High ExpressionClass II Promoter

mgtA Directorientation

good close

PhoP-binding site

phoP

Directboth

repeats

Class II Promoter

PhoP-binding siteboth repeats

PhoP-boxphoP

mgtA

+1

-10

-10

+1PhoP-box

mgtC

mig-14-10

-10 +1PhoP-box

OmpR-box PhoP-box -10-10-10 -35 +1+1+1

PhoP-box -10-10-35 +1+1Fis-box

pagC-10 +1PhoP-boxPhoP-box -10-10-35 +1+1OmpR-LPR-

IHF

OUR ANALYSIS DISCOVERED ATIPYCAL PHOP-

REGULATED PROMOTERS

Class II Promoter

orientationvery goodmedium

PhoP-binding site

Opposite

Class II Promotervery goodmedium

bad close

Class I Promoter

bad close

Class II Promoter

Class II Promotergoodremote

Class I Promoter

goodremote

phoPphoQ

mgtA

Mg2+ transport

PhoQ

PhoP

PmrB

PmrA

SEQUENTIAL ACTIVATION OF THE PHOP REGULON

pmrC pmrA pmrB

pbgP

LPS modificationvirulence

mgtCB pagP

PmrD

mig-14 pmrD

PROMOTER –EXPRESSION –ORIENTATION

PROFILES

E1

E1

P3

O1

Close class I

Medium class II

Mediumclass I

Remoteclass II

Remote.class I

Close class II

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

E1 E2 E30

0.10.2

0.3

0.40.50.6

0.7

0.80.9

1

mgtCybjXmig-14slyBpagCyhiWvirK

PhoP-binding site

T TGTTTA TAATT TGTTGA T

THE MINIMAL PROFILES THAT DESCRIBES THE

SEQUENTIAL ACTIVATION OF THE PHOP REGULON

A GT

CGTTTA TGATTT TGTTTA A

mgtA

phoP

T TATTGTA CGTTCTGTTTA T

pmrD

A TGTTTA AGAA

TACT ATGGTTTA AT

mig-14mgtCB pagP

Class II Promoter

mig-14

Class II Promoter

Oposite

Class II Promotergood first repeat

Class I Promoter

good remote

orientation goodmedium

pagC

Direct

LRP-binding sitePhoP-binding site

how affinity

high affinity

close

close

OmpR-binding site

IHF-binding site

low affinity

OpositeClass I Promoter

distance

good both repeats

orientationhigh affinity

Opposite

Class II Promoter

distance

Repressed

close

PhoP-binding site

high affinity

Activated

Activated

Activated

distance

distance

goodmedium

orientation

Oposite

orientation

goodmedium

goodmedium

poorclose

Activated

good first repeat

high affinity

OmpR-binding site close

Class II Promoter

Class I Promoter

Activated

distance

distancePhoP-binding site

Activated

excellentmedium

goodmedium

good remote

distance

distance

distancedistance

PhoP-binding site

orientation

low affinityhigh affinity

mgtCclose

distanceActivatedActivated

distance

Activated

Class I Promoter

Class I Promoter

orientation

high affinity

PhoP-binding sitedistance

Repressed

Class II Promotergoodclose

distance

close

good first repeat

Fis-binding site

THE MAXIMAL PROFILES THAT DEFINES THE

SEQUENTIAL ACTIVATION OF PHOP

Imperfect second PhoP-box tandem

Opposite orientation

Medium distance promoters

Activated

THE PHOP/PHOQ SYSTEM REGULATES EXPRESSION

OF ACID pH RESISTANCE GENES IN E. COLI

yhiWYhiW

low Mg2+

PhoQ��

PhoP -PO3

gadA

yhiE YhiE

hdeA

EXPRESSION AND EXPRESSION-PROMOTER

PROFILES

Expression E1 E2 E3

E1 E2) E300.10.20.30.40.50.60.70.80.91

E1

E2

E3

00.10.20.30.40.50.60.70.80.9

1

00.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Close class I

Medium class II

Mediumclass I

Remoteclass II

Remote.class I

Close class II

b1825yhiEyhiWyiaG

dpsgadAgadBygiW

PromoterP0 P1 P2 P3

E1

E2

E3

Expression-Promoter

E1 E2 E3

00.10.20.30.40.50.60.70.80.9

1

Promoter

Motif

Interactions

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

AraC

.

ArcA ArgR CRPCytR

DeoR

DnaA FIS FN

RFru

R Fur

GlpR IHF Lrp MalT MelR NarLOmpR OxyR RcsU Rha

S

PmrA-YgiX YhiW

-0.050

0.050.1

0.150.2

0.250.3

0.350.4

0.45

AraC

. ArcA ArgR CRP

CytR DeoR DnaA FIS FNRFru

R Fur GlpR IHF Lrp MalT MelR NarLOmpR OxyR RcsU RhaS

PmrA-YgiX YhiW

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

AraC

.

ArcA ArgR CRPCytR

DeoR

DnaA FIS

FNR

FruR Fu

rGlpR IH

F Lrp MalTMelR

NarLOmpR

OxyR

RcsU

RhaS

PmrA-Y

giX YhiW

0

0.10.2

0.30.4

0.5

0.6

0.7

0.8

Close class I

Medium class II

Mediumclass I

Remoteclass II

Remote.class I

Close class II

00.10.20.30.40.50.60.70.8

Close class I

Medium class II

Mediumclass I

Remoteclass II

Remote.class I

Close class II

Close class I

Medium class II

Mediumclass I

Remoteclass II

Remote.class I

Close class II

0

0.1

0.2

0.3

0.4

0.50.6

0.7

0.8

hilA hdeAnmpC hdeBpagP hdeDybjX

slyB phoPybcUyhiWmgtA

slyB yhiE

MOTIFS-PROMOTER-INTERACTION PROFILE

THE PROFILE THAT DEFINES THE PHOP/PHOQ

REGULATION OF ACID pH RESISTANCE GENES

Class II Promoter

very goodmedium

PhoP-binding site Class II Promotervery goodmedium

bad close

Class I Promoter

bad close

Class II Promoter

Class II Promotergoodremote

Class I Promoter

goodremotegood

closeClass II Promoter

Class I Promoter

Class II Promoter

Class I Promoter

Class II Promoter

bad medium

bad medium

bad medium

bad medium

gene

Low Expression

YhiE

YhiW

PhoP -PO3

yhiE

yhiW

YhiE

YhiW

gadA

yhiE

yhiW

hdeA

YhiE

YhiW

PhoP -PO3

yhiE

yhiW

ADVANTAGES OF CONCEPTUAL CLUSTERING

(Michalsky, Chesseman, Cook, Ruspini, etc.)

•Hypothesis generation – data exploration – model fitting – finding true topologies

•Deal with structural data

•Iterative and incremental search of interesting repetitive substructures

•Attributes and relations belong to more than one substructure

•Attributes and relations are flexible

•Ability to deal with missing values

•DISCOVER DESCRIBE -> PREDICT

Conceptual Clustering: Substructure Discovery

Input Problem Input Database

R1

C1

T1

S1

T2

S2

T3 T4

S3 S4

Compressed Database

S1 C1

R1

S1 S1S1

Recognize repetitive relations among attributes that describe

objects in structural databases (substructure discovery)

Model fittingHypothesis generation

and testingFinding true topologies

Data exploration Group predictions Data reduction

Substructure

object

object

triangle

squareshape

shape

on

Substructure Discovery and Evaluation Strategy by MOGA CC

Root

MOGA CC: Local and Global Evaluation

TTTTTGTCATAAGAAAAAATATCAAACAAACTT

O2

O1

Hybrid Promoter Analysis Methodology (HPAM)

Time Delayed Neural Network (TDDN)+

Multi-Objective Scatter Search genetic algorithm (MOSS)

T TGTTGATAATT TGTTTATM4

AT

ATGGTTTAC

TAATAAG

TGTTTAAM3

A TGTTTATGATTTGTCGTTTAAM2

T TGTTTACGTTC TATTGTATM1

TGTTTA_ _ _ _ _ TGTTTA

#5 TTGTTTATGATTTGTTTAA | 0 6 3 1 0 21 25 25 25 25 25 5 4 1 1 6 21C | 3 0 0 3 1 3 25 25 25 25 25 1 0 2 0 0 2G | 6 19 2 0 4 0 25 25 25 25 25 9 19 0 4 4 2T | 16 0 20 21 20 1 25 25 25 25 25 10 2 22 20 15 0

BINDING SITES SUBMOTIFS

-10-10-35 +1

PROMOTER FEATURES: RNA POLYMERASE, CLASS

AND LOCATION

capa de entrada capas ocultas

capa de salida

“pesoscorrespondientes”(valen lo mismo)

neuronas “time shifted”(corridas en el tiempo)

reconocen la misma subsecuencia de la

secuencia de entrada.

TDNN: A WEIGHT SHARING NEURAL NETWORK

MOSS: A MULTIOBJECTIVE GENETIC FUZZY SYSTEM

Gene Expression Networks Iterative Explorer (GENIE)

Promoter binding dynamics reveals architecture

of small bacterial regulatory networks

high Fe3+

pmrDPmrD

low Mg2+

PhoQ��

PhoP -PO3

PmrB��

-PO3PmrA

pbgP

LPS modification

-10PhoP-boxPmrA-box

-10PhoP-box

-10PmrA-box

-10PhoP-boxphoP

mgtA

pmrD

pmrA

GENERATING GENETIC CIRCUITS BY GPS

NAVIGATION

PmrB

P

PmrAPmrA

pbgP

PhoQ

P

PhoPPhoP

low Mg2+ high Fe3+

pmrDPmrD

THE USE OF CONCEPTUAL CLUSTERING FINDINGS

AS A HYPOTESIS GENERATOR

GENE PROMOTER ACTIVITY AS BUILDING BLOCK

INTERACTIONS

(Bower and Bolouri, 2001)

KINETIC MODEL OF TWO PROTEINS P AND Q

BINDING TO DNA

HMg2+

phoP

mgtA

pmrD

mgtC

pagP

pmrC

pbgP

promoters

IPtotal

IPtotal

IPtotal

IPtotal

IPtotal

IPtotal

IPtotal

0 5 10 20 30 60 90-time (min)L

ORDERED BINDING OF THE PHOP AND PMRA

PROTEINS TO THEIR TARGET PROMOTERS

SEQUENTIAL ACTIVATION OF THE PHOP REGULON

phoPphoQ

mgtA

Mg2+ transport

PhoQ

PhoP

PmrB

PmrA

pmrC pmrA pmrB

pbgP

LPS modificationvirulence

mgtCB pagP

PmrD

mig-14 pmrD

0

0.2

0.4

0.6

0.8

1.0

1.2

-10 10 30 50 70 90Time (min)

Rela

tive

IP

phoP

-5 0 5 10 20 30 60 90 -5 0 5 10 20 30 60 90

mgtA rstA

0

0.5

1.0

1.5

2.0

2.5

-5 0 5 10 20 30 60 90 (min) - IP

- input

0

0.2

0.4

0.6

0.8

1.0

1.2

-10 10 30 50 70 90Time (min)

-10 10 30 50 70 90Time (min)

pmrD

-5 0 5 10 20 30 60 90

0

0.2

0.4

0.6

0.8

1.0

1.2

Rela

tive

IP

-10 10 30 50 70 90Time (min)

slyB

-5 0 5 10 20 30 60 90

mig-14

-5 0 5 10 20 30 60 90 (min) - IP

- input

0

0.2

0.4

0.6

0.8

1.0

1.2

-10 10 30 50 70 90Time (min)

0

0.5

1.0

1.5

2.0

2.5

-10 10 30 50 70 90Time (min)

-5 0 5 10 20 30 60 90

mgtC pagP

-5 0 5 10 20 30 60 90

pagC

-5 0 5 10 20 30 60 90 (min)

- IP

- input

0

0.2

0.4

0.6

0.8

1.0

1.2

Rela

tive

IP

-10 10 30 50 70 90Time (min)

0

0.2

0.4

0.6

0.8

1.0

1.2

-10 10 30 50 70 90Time (min)

0

0.2

0.4

0.6

0.8

1.0

1.2

-10 10 30 50 70 90Time (min)

pcgL

-5 0 5 10 20 30 60 90

0

0.2

0.4

0.6

0.8

1.0

1.2

-10 10 30 50 70 90Time (min)

Rela

tive

IP

ORDERED PHOP BINDING AND TRANSIENT

OCCUPANCY OF PROMOTERS

0

0.5

1

1.5

2

2.5 rst

AmgtAphoPslyBpmrDpagPmig-14pagCpcgLmgtC

-5 0 5 10 20 30 60 90

Time

-5 0 5 10 20 30 60 90

mgtC

pcgL

pagC

mig-14

pagP

pmrD

slyB

phoP

mgtA

rstA

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

TEMPORAL ORDER OF BINDING

ij

ijiij ktA

ktAtX

)(1)(

)(

PROMOTER ACTIVITY AND PREDICTIONS FROM

MECHAELIS-MENTEN KINETIC MODEL

rate of the unrepressed promoter ithe effective affinity of the activator (concentration at half maximal activation) ikthe effective activation concentration in experiment j for all promoters )(tAj

0 50

0.5

1

phoP0 5

0

0.5

1

1.5

mgtA0 5

0

1

2

3

rstA

0 50

0.5

1

pmrD

0 50

0.5

1

slyB

0 50

0.2

0.4

mgtC

0 50

0.5

1

pagP

0 50

0.2

0.4

pagC

0 50

0.1

0.2

pcgL

0 50

0.5

1

1.5

mig-14

TEMPORAL ORDER OF BINDING WITH LEARNED

ACTIVATION CONCENTRATION

Promoters

rstA phoP pmrD mig-14 pcgL mgtA slyB pagP pagC mgtC

0

0.5

1

1.5

2

2.5

EFECTIVE PROMOTER RATE: ORDERED RESULTS

0

5

10

15

20

25

Time

-5 0 5 10 20 30 60 90

0

5

10

15

20

25

-5 0 5 10 20 30 60 90

EFFECTS OF THE ACTIVATION CONCENTRATION

0

0.5

1

1.5

2

2.5

Promoters

rstA phoP pmrD mig-14 pcgL mgtA slyB pagP pagC mgtC

0 50

0.5

1

phoP0 5

0

0.5

1

1.5

mgtA0 5

0

1

2

3

rstA

0 50

0.5

1

pmrD0 5

0

0.5

1

slyB

0 50

0.5

1

1.5

mig-14

0 50

0.2

0.4

mgtC

0 50

0.5

1

pagP

0 50

0.2

0.4

pagC0 5

0

0.1

0.2

pcgL

TEMPORAL ORDER OF BINDING WITH EXPERT-BASED

CONCENTRATION

pagK0 5

0

0.5

1

PhoP-box

PhoP-box -10-35

PmrA-box -10

+1

pmrD

mgtC

PhoP-box -10 mgtAtreR

+1

+1

-10 -35

TEMPORAL ORDER OF BINDING: PREDICTION BASED

ON REGULATORY FEATURES

Fe3+ REPRESSES pmrD EXPRESSION

VIA THE PMRA/PMRB SYSTEM

high Fe3+

pmrDPmrD

low Mg2+

PhoQ��

PhoP -PO3

PmrB��

-PO3PmrA

pbgP

LPS modification

)3(0

)2(0

)4(0

)1(

_

0

][][][

][][

][][_

_

_

PHOPPHOP

phopphopPHOPphop

HPHOPT

HphopT

dtPHOPd

HphopT

PHOPKPHOP

HT

dtphopd

phopPHOPphopPHOP

phopPHOP

Decaimiento espontáneo

(3)PHOPphop

Activación

Traducción

Decaimiento espontáneo

(1)

(2)

(4)

MATEMATHICAL INTERPRETATION OF BUILDING

BLOCKS

COMPOSING BUILDING BLOCKS INTO GENE

NETWORKS

Funcionalidad Mg2+ Fe3+ mgta pmrd pbgp

1 1 1 1 0 12 1 0 1 1 13 0 1 0 0 14 0 0 0 0 0

Entrada Salida

0

0,2

0,4

0,6

0,8

1

1,2

0 50 100 150 200 250 300

Tiempo (mins)

Con

cent

raci

ón

pmrd

mgta

pbgp

CONSTRAINT-BASED LEARNING ALGORITHM

0

0,2

0,4

0,6

0,8

1

1,2

0 50 100 150 200 250 300

Tiempo (mins)

Con

cent

raci

ónPHOQPHOQ-ACTphop_phoqPHOPPHOP-PpmrdmgtaE-Mg2PMRBPMRB-ACTpmra_pmrbPMRAPMRA-PpbgpE-Fe3PMRDPMRA-P_PMRD

Funcionalidad Mg2+ Fe3+ mgta pmrd pbgp # Parámetros Proporción Probabilidad

1 1 1 1 0 1 68 0,000186349 0,8813570122 1 0 1 1 1 68 0,001092771 0,9045841133 0 1 0 0 1 68 0,000735495 0,8993325074 0 0 0 0 0 68 0,469708768 0,988949126

Entrada Salida

SIMULATIONS AND EVALUATION

0

0. 2

0. 4

0. 6

0 2 4 6 8 10 12

alpha_PM RA -P

0

0. 2

0. 4

0. 6

0 2 4 6 8 10 12

alpha_PM RA -P_PM RD

0. 2934

0. 2936

0. 2938

0. 294

0. 2942

0. 2944

0 20 40 60 80 100 120

H_mgta

0

0. 2

0. 4

0. 6

0 20 40 60 80 100 120

H_pbgp

0

0. 1

0. 2

0. 3

0. 4

0. 5

0 20 40 60 80 100 120

H_PHOP

0. 293

0. 294

0. 295

0. 296

0. 297

0. 298

0 20 40 60 80 100 120

H_phop_phoq

0

0. 2

0. 4

0. 6

0 20 40 60 80 100 120

H_PHOP-P

0

0. 2

0. 4

0. 6

0 20 40 60 80 100 120

H_PHOQ

0

0. 2

0. 4

0. 6

0 20 40 60 80 100 120

H_PHOQ-A CT

0

0. 1

0. 2

0. 3

0. 4

0. 5

0 20 40 60 80 100 120

H_PM RA

0

0. 2

0. 4

0. 6

0 20 40 60 80 100 120

H_pmra_pmrb

0

0. 2

0. 4

0. 6

0 20 40 60 80 100 120

H_PM RA-P

0

0. 2

0. 4

0. 6

0 20 40 60 80 100 120

H_PM RA -P_PM RD

0. 285

0. 29

0. 295

0. 3

0 2 0 4 0 60 80 1 00 1 20

H_PM RB

0

0. 1

0. 2

0. 3

0. 4

0 20 40 60 80 100 12 0

H_PM RB -A CT

PARAMETER SENSITIVITY

Simulaciones

0

0,2

0,4

0,6

0,8

1

1,2

0 10 20 30 40 50 60 70 80 90 100

Tiempo (mins)

Expr

esió

n

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

PREDICTIONS

Parámetros Modificados1 2 3 4 5 6 7 8H_PHOP-P 10 10 10 10 10 10 10 10H_mgta 20 20 20 20 20 20 20 20nu_mgta 1 1 1 1 1 5 5 5K_mgta 0,01 0,025 0,05 0,075 0,1 0,01 0,025 0,05

Parámetros Modificados9 10 11 12 13 14 15H_PHOP-P 10 10 10 10 10 10 10H_mgta 20 20 20 20 20 20 20nu_mgta 5 5 10 10 10 10 10K_mgta 0,075 0,1 0,01 0,025 0,05 0,075 0,1

Simulación

Binding relativo de PhoP al DNA de distintos genes

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

-20 0 20 40 60 80 100

Tiempo (mins)

Bind

ing

phoPmgtArstApmrDslyBmig-14mgtCpagPpagKpagCpcgL

-5 0 5 10 20 30 60 90

mgtC

pcgL

pagC

mig-14

pagP

pmrD

slyB

phoP

mgtA

rstA

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

EXPERIMENTAL VALIDATION

Correlación de Pearson

Simulación phoP mgtA rstA pmrD slyB mig-14 mgtC pagP pagK pagC pcgL1 0,619287092 0,857949781 0,339818737 0,829000283 0,264923353 0,482460587 0,277169074 0,556744029 0,5991426 0,180180349 0,3209791932 0,745769371 0,906526323 0,502007318 0,901528946 0,424242626 0,628335893 0,419452365 0,671094889 0,722414443 0,332324322 0,470990363 0,843852314 0,915138454 0,648375986 0,936323412 0,581156672 0,751659807 0,552810422 0,762873798 0,822053179 0,482872977 0,6086787584 0,886153114 0,898741294 0,725794354 0,936364865 0,671873682 0,811663401 0,625347639 0,803557401 0,867101681 0,569665865 0,6820788125 0,813971572 0,842706965 0,657161056 0,882303929 0,641101063 0,746960693 0,600671959 0,782555306 0,834827405 0,54888883 0,6499702796 0,484578386 0,784076325 0,182657971 0,735239947 0,119355869 0,334879341 0,142821565 0,436909899 0,470369998 0,042201854 0,1771080887 0,858977561 0,961379585 0,648559205 0,971032198 0,499422627 0,75927577 0,509215514 0,747911953 0,80498073 0,413593112 0,5830255818 0,843524871 0,499762374 0,946114321 0,626705145 0,922203925 0,92202348 0,895723654 0,835517001 0,868634095 0,900230693 0,9324808999 0,550703753 0,117924047 0,749545426 0,247775955 0,917814195 0,650777545 0,732942357 0,530287422 0,591071935 0,850412566 0,733320591

10 0,286592176 -0,150739857 0,545795459 -0,027344699 0,799582889 0,394582616 0,512804486 0,253380729 0,325408172 0,687623039 0,50931898811 0,468974213 0,772242027 0,167076499 0,722511526 0,107396064 0,319675582 0,132281945 0,426383442 0,458211028 0,031994059 0,16522581612 0,913010303 0,975698548 0,713560888 0,981413357 0,533762236 0,804706746 0,509096392 0,715888847 0,79386547 0,42468576 0,58693351213 0,801054535 0,429836349 0,915624862 0,563254584 0,913774241 0,894472406 0,919408323 0,83067834 0,842771907 0,917072159 0,93035610614 0,425722417 -0,007911581 0,639678643 0,113528727 0,886942645 0,524659984 0,647662009 0,409550397 0,485339495 0,810487256 0,63651177515 0,122722329 -0,291314081 0,403220158 -0,17977406 0,698132005 0,225771766 0,349213006 0,071990352 0,155319882 0,556525278 0,349257421

Max Val 0,913010303 0,975698548 0,946114321 0,981413357 0,922203925 0,92202348 0,919408323 0,835517001 0,868634095 0,917072159 0,932480899Simulación 12 12 8 12 8 8 13 8 8 13 82 Mejor 4 7 13 7 9 13 8 13 4 8 13

CORRELATION BETWEEN EXPERIMENTAL

VALIDATION AND PREDICTIONS

phoPphoQ

mgtA

Mg2+ transport

PhoQ

PhoP

PmrB

PmrA

SEQUENTIAL ACTIVATION OF THE PHOP REGULON

pmrC pmrA pmrB

pbgP

LPS modificationvirulence

mgtCB pagP

PmrD

mig-14 pmrD

INGENEUE ENVIRONMENT

Microarray Integrated Analysis (MIA)

The Raw Material Available for the Analysis Consist of …

Gene expression (GeneChips) derived from longitudinal blood expression profiles of human volunteers treated with intravenous endotoxin compared to placebo

8 patients, four treated, patients 1-4, and four control, patients 5-8

From each patient there are 6 samples taken at different time points: at hour 0, hour 2, 4, 6, 9, 24. For patient 6, hours 4 and 6 are missing

… identify molecular pathways that provide insight into the host response over time to systemic inflammatory insults, as part of a Large-scale Collaborative Research Project sponsored by the National Institute of General Medical Sciences (www.gluegrant.org).

Our Ultimate Goal is to …

… by Building Genetic Networks Using Reverse Engineering Techniques

Hierarchical approach to model the dynamics of genetic networks by doing reverse engineering

Need of gene expression preprocessing by clustering genes into profiles

Difficulties of typical statistical methods in detecting significant changes between treated and control gene profiles

Revisit the reverse engineering approach by using data mining tools

Gene Expression Patterns Detected by Clustering Methods

First Approach to Obtain Dynamic Patterns: Boolean Networks

expression

time

B

C A B

B A or C

C (A and B) or (B and C) (A and C)A

First Approach to Obtain Dynamic Patterns: Boolean Networks

time

expression

t0 t1 t0 t1 t0 t1

A B

B A or C

C (A and B) or (B and C) (A and C)

Time t Time t+1A B C A B C

0 0 0 0 0 10 0 1 0 1 00 1 0 1 0 01 0 0 1 1 11 0 0 0 1 01 0 1 0 1 11 1 0 1 1 11 1 1 1 1 1

Static Vs. Dynamic Clustering

Clustering

0011 -> A1

1100 -> A2

0101 -> B1

1010 -> B2

A

B

t0

t1

Inferencia de relaciones temporales

The relationship between B(t0) and B(t1) can be captured by grouping B1 and B2 as a negative correlation. However, relationships among A(t0), B(t0) and A(t1) could not be determined in the same way because they are derived from a temporal behavior.

Static clustering

A(t0) Or B(t0) = A(t1)

0 0 0

0 1 1

1 0 1

A(t1) = A(t0) OR B(t0)

Not B(t0) = B(t1)

0 1

1 0

0 1

B(t1) = NOT B(t0)

A

OR

B

NotDynamic models

t0 t1

001 011

010 101

Dynamic Patterns Learned by Doing Reverse Engineering

D

B

E

C

A

113

28

Not

56[1

99]

184[71]

Not

98

99

226

78

49[206]

14 135

248

Not

195

Not

2[253]

126[129]Not 64

[191]Not 32[223]

16[239]

8[247]

4[251]

160

[95]

144

132

[123

]

63 [192

]

130

224

Not

143

96

Not

79

Not

48 [207

]24

[231

]

136

68

93[162]

34 [221

]

46

22 116

11

Not 186[69]

Not 110[145]

17[238] Not 18

[237]9

[246]

Not 115[140]

12

13

6

198

Not 70 35[220]

3[2

52]

131[124]

65

193

1[2

54]

NotNot

10 122 66Not 94 175[80]

138

[117

]

5[2

50]

Not

194

Not

33 47 [208

]

OR

Not

21[234]

Not

Not

236 0[255]

128[127]

OR

Not

Not

OR

OR

OR

OR

OR

20[235]

Not

A More General Approach: Hierarchical Modeling

Preprocess gene expression by clustering genes into prototypes

Build Boolean circuits based on prototypes

Recognize differential patterns between experiments and control

Interpret differences

Select interesting genes

Go deep into continuous modeling

Boolean Modeling: Pattern Preprocessing

OBJECTIVE: Find Genes with Different Treatment-Control Profiles, e.g.

TREATMENT CONTROL TREATMENT

TREATMENT

TREATMENT

PAT5 PAT6 PAT7 PAT8PAT1 PAT2 PAT3 PAT4

h0 h2 h4 h6 h9 h24

h0 h2 h4 h6 h9 h24

CONTROL CONTROLTREATMENT

Representation Issues…

TREATMENT

CONTROL

A cluster profile from treatment might result in several different profiles in control and vice versa.

Variability in Microarray Data...

TOTAL VARIABILITY: Biological Variability (main interest) + Systematic Variability (technical) + Random Variability

The statistical methods for microarray data analysis try to extract the biological variability and set aside the systematic and random variability.

Steps in the Statistical Analysis

SCALING: conversion of fluorescence values to numeric values in an established rank. We use Li-Wong scaling model in the whole analysis process.

NORMALIZATION: The process of removing systematic variability(detection of outliers).

FILTERING: preliminary elimination of genes that show no expression change at all between treatment and control.

DIFFERENTIAL ANALYSIS: Select a set of genes that are differentially expressed from treatment to control (show biological variability). This analysis will be performed over treatment / control and also over time. Since we work with data over time series, it is interesting to notice the behaviour of a gene over time. Accordingly we will analyse the data over both treatment and time.

TREATMENT CONTROL TREATMENT CONTROL TREATMENT CONTROL

Summarizing...

Yes/No

Number of Genes Selected by each Method

table 1

Results: ANOVA over Treatment, 2895 GenesTREATMENT CONTROL

Results: ANOVA over Time, 1226 GenesTREATMENT CONTROL

Results: ANOVA over Time/Treatment, 2715 Genes

TREATMENT CONTROL

Results: 7217 Genes Selected as the Union set of All Previous Methods

TREATMENT CONTROL CONTROL

Results: Clustering

TREATMENT

CONTROL

From the 7217 genes 5367 do not show an explicit differential profile expressed from control to treatment. Clustering has been applied and these clusters are not further studied, at least at this point.

5367 Genes

Summarizing Results, 1850 Genes Selected as Differentially Expressed from All Methods

TREATMENT TREATMENT CONTROL

Method Contribution to the Final Gene Selection

The percentage shown between parenthesis is the proportion of genes from the original method selection prior to applying any clustering (table 1) that stay in this final selection.

table 2

Genes Found by Li-Wong Filtered + t- test Comparisons and not by SAM

TREATMENT CONTROL

All the gene patterns found by any of the Li-Wong variants have been also found by ANOVA over treatment, some particular genes do not coincide though, but a small number.

Genes Found by Li-Wong with Time-Blocks

TREATMENT CONTROL

This type of profile shows very differentiated gene behavior in a patient with respect to all the others. These differences are between patients in the same experimental group, which can be caused by gender, age, conditions of the experiment... or other more interesting causes. Finding these patterns gives extra information about the experiment and should be taken into account.

Genes Found by SAM and ANOVA over Time/Treatment but not by Li-Wong

TREATMENT CONTROL

Genes Found by ANOVA over Treatment and by Li-Wong but Not by SAM

TREATMENT CONTROL

Genes Not Found by ANOVA over Time

TREATMENT CONTROL

Genes Found by ANOVA over Time and over Time/Treatment but Not by ANOVA over

Treatment

TREATMENT CONTROL

Genes Found by ANOVA over Treatment but Not by ANOVA over Time

TREATMENT CONTROL

Genes Found by ANOVA over Treatment but Not by any Other Combination of Methods

TREATMENT CONTROL

Coincidence Statistics: Pairwise Coverage of Examples

table 3

ColumnColumnRow

#)(#

Cell values correspond to percentage

High values -> most genes from row method are included in those from column method

Low values -> few genes from row method are included in those from column method

Which ones are the Best Fitting Methods? The Problem of Longitudinal Data

Clustering Visualization and Decision Making

Dependent Longitudinal Data: Repeated Measures ANOVA

Data Interactions: Relationship Between Gene Expression Changes and Other Features

Profile Search by Using Similarity to Prototypes

Clustering Visualization and Decision Making

TREATMENT

CONTROL

From the 7217 genes 5367 do not show an explicit differential profile expressed from control to treatment. Clustering has been applied and these clusters are not further studied, at least at this point.

5367 Genes

Dependent Longitudinal Data: Repeated Measures ANOVA

In repeated measures design ...

sample members are measured on several occasions or trials

each trial represents the measurement of the same characteristic under a different condition

... but in multivariate design ...

sample members are measured on several occasions, or trials

each trial represents the measurement of a different characteristic

Data Interactions: Relationship Between Gene Expression Changes and …

Gene Ontology (Molecular functions and biological processes)

Binding sites and CIS-features

Physical locations

Maps of biological pathways

expression1 2

0

2

4

6

8

transporter translation regulator

transcription regulator structural molecule

signal transducer obsolete molecular

chaperone

catalytic

binding

Molecular Function Expression

expression1 2

0

1

2

3

4 physiological

obsolete

biological development

cellular

Biological Process

Data Interactions: Gene Ontology

Associations of Functions and Processes

transporter

catalytic

chaperone

chaperonegene X chaperone

gene Y

Combined Associations of Functions and Processes

transporter

catalytic

chaperone

gene X

catalytic

chaperone

transporter

gene Y

Going Back to the Dynamic Modeling Problem

Preprocess gene expression by clustering genes into prototypes

Build Boolean circuits based on prototypes

Recognize differential patterns between experiments and control

Interpret differences

Select interesting genes

Go deep into continuous modeling

Recognize Differential Patterns between Experiment and Control Representations

D

B

E

C

A

113

28

Not

56[1

99]

184[71]

Not

98

99

226

78

49[206]

14 135

248

Not

195

Not

2[253]

126[129]Not 64

[191]Not 32[223]

16[239]

8[247]

4[251]

160

[95]

144

132

[123

]

63 [192

]

130

224

Not

143

96

Not

79

Not

48 [207

]24

[231

]

136

68

93[162]

34 [221

]

46

22 116

11

Not 186[69]

Not 110[145]

17[238] Not 18

[237]9

[246]

Not 115[140]

12

13

6

198

Not 70 35[220]

3[2

52]

131[124]

65

193

1[2

54]

NotNot

10 122 66Not 94 175[80]

138

[117

]

5[2

50]

Not

194

Not

33 47 [208

]

OR

Not

21[234]

Not

Not

236 0[255]

128[127]

OR

Not

Not

OR

OR

OR

OR

OR

20[235]

Not

Recognize Differential Patterns between Experiment and Control Representations

Not

2 32[223]64126

[129]16

[239]8

[247]4

[251]Not

66

Not

134

AN

D

12 [243

]23

1

Not

48

Not

96

192

194Not

144

200

AN

D

119

[136

]

Not

196

Not

9878 [177

]

88

Not

216

Not

211

147

Not

100

Not

50 [205

]10

2

Not

179

30

225

Not

112Not

240

199[56]

242

[13]

114

198Not

70 Not 220

101

7236 [219

]

146

[109

]

189

[246

]

195

6

3 131

130

65

193

227

184[71]

Not

135

7

Not

224

115

94 [161

]

Not

33 [222

]

208

Not

80

40[215]

168[87] 84

212

213

85Not

148[107]

74

75OR

165 210[45] 82 214Not

20

10

138

69

197

AND

139

244

Not

49

93

Not

209

81

Not

Not68

160

[95]

226 241

1[254]

0[255]

128[127]

229

OR

OR Not

113

28

Not

56 [199

]

184[71]

Not

98

99

226

78

49[206]

14 135

248

Not

195

Not

2[253]

126[129]Not 64

[191]Not 32 [223]

16[239]

8[247]

4[251]

160

[95]

144

132

[123

]

63 [192

]

130

224

Not

143

Not

96

Not

79

Not

48 [207

]24 [231

]

136

68

93[162]

34 [221

]

46 Not 104[151] 180 90 Not 82

210 Not 22 116

11

Not 186[69]

Not 110[145]

17[238] Not 72

[183]36

[219]18

[237]9

[246]

Not 115[140]

12

AN

D13

6

198

Not 70 35[220]

3[2

52]

131[124] 65

193

1[2

54]

AND NotNot

10 122 66Not 94 175[80]

40[215]

20[235]

138

[117

]

5[2

50]

Not

194

Not

33 47 [208

]

Not

87 [168

]

Not

OR

107 Not

202

101

77205

NotNot

AN

D

Not

85[170]

Not

42[213]

21[234]

NotAND

AN

D

Not

204 102

Not

76

38

166

Not

211

147

236

73

201

Not

0[255]

128[127]

OR

Not

Find architectural differential sub-circuits

Reverse gene mapping

TREATMENT CONTROL

Find Architectural Differences as Sub-circuits, e.g., members of the nerve growth factor (NGF) and

neurotrophic factor (GDNF) families

GDNF

40921_at 35453_at

2448

NGF 32

18OR4

OR

Preserve Relationships Among Genes by Using Temporal Clustering

31648_at31982_at32025_at32368_at32617_at32643_at32775_r_at33018_at33742_f_at33854_at34133_at35085_r_at36246_at37356_r_at37532_at37881_at39760_at40534_at40987_at41119_f_at41480_at41703_r_at41767_r_at853_at

41703_r_at

31648_at37881_at

41480_at

35618_at781_at

32[223]

16[239]

8[247] 18 9

[246]OR4[251]

1013_at2092_s_at31421_at32388_at32743_at32982_at34687_at35241_at35371_at35662_at36800_at36820_r_at36903_at37378_r_at37481_at37884_f_at38137_at38886_i_at39626_s_at39851_at40449_at41012_r_at

33496_at36760_at

41685_at

39806_at

1599_at31377_r_at32184_at32367_at32489_at32683_at33276_at34198_at34453_at34554_at34666_at35878_at36269_at36868_at36999_at37120_at37534_at38430_at38684_at39063_at41260_at746_at863_g_at

1895_at

32090_at35752_s_at

1629_s_at2002_s_at31653_at31810_g_at32666_at32999_at33103_s_at35203_at35245_at35258_f_at35405_at35698_at35973_at36214_at37489_s_at37895_at38405_at39663_at39878_at40041_at40086_at40964_at41228_r_at

35405_at37895_at

31810_g_at40041_at

32200_at40678_at41669_at

36895_at38038_at41784_at

37073_at37812_at39532_at40726_at

32[223]

16[239]

8[247] 18 9

[246]OR4[251]

35453_at

1560_g_at34757_at34997_r_at35373_at36911_at36931_at37671_at37804_at38096_f_at39015_f_at39513_r_at40921_at

31648_at37881_at

39806_at 41703_r_at

31345_at34790_at38829_r_at41401_at969_s_at

41685_at

1527_s_at1915_s_at31409_at31723_at32140_at32568_at32571_at33601_at33686_at33756_at34470_at35072_at35139_at35495_at36013_at36448_at36525_at36693_at37215_at37480_at37572_at37701_at37989_at38575_at38848_at39289_at39732_at40453_s_at40601_at40735_at754_s_at

31810_g_at40041_at

32090_at35752_s_at

35405_at37895_at

31329_at31454_f_at33548_f_at34382_at34973_at35034_at35268_at35904_at36835_at36976_at37351_at37863_at38463_s_at38642_at39034_at949_s_at

[AFFX-HUMRGE/M10098_5_at]

35618_at781_at

33496_at36760_at

1895_at

1262_s_at238_at32635_at32769_at34249_at34297_at34720_at34807_at35171_at35546_at38001_at38134_at38958_at41701_at

41480_at

32200_at40678_at41669_at

GDNF NGF

32 16 8 4

41685_at

39806_at

1895_at

32090_at35752_s_at

35405_at37895_at

31810_g_at40041_at

32200_at40678_at41669_at

41480_at

35618_at781_at

1

2

3

45

32 16 8 4

41685_at

39806_at

1895_at

32090_at35752_s_at

35405_at37895_at

31810_g_at40041_at

32200_at40678_at41669_at

41480_at

35618_at781_at

1

2

3

45

GPS, for Gene Promoter Scan

HPAM, for Hybrid Promoter Analysis Methodology

GENIE, for Gene Expression Networks Iterative Explorer

MIA, for Microarray Integrated Analysis

http://gps-tools.wustl.edu.

SUMMARIZING …

“…organisms of the most different sorts are

constructed from the very same battery of

genes. The diversity of life forms results from

small changes in the regulatory systems that

govern expression of these genes.”

François JacobIn Of flies, mice and men