Transcript Biochip Medicine and Pathway Biology

Pathway Medicine
Computational thinking series
Peter Ghazal
University of Edinburgh
Medical School
Outline of Talk
• The post-genomics challenge
1. Pathway Biology approach – notation system
2. Logic Interaction Diagram – Interferon pathway
3. Analysis of HTP data using pathway diagram.
4. Future perspective - Sensors for pathway logic
Central Dogma +
GENETICS
EPI-GENETICS
DNA
METABOLITE
RNA
PROTEIN
Pre-Genomic
• Reductionist (DNA or RNA or Protein)
Organism
• Observational/ phenomenological
• Generally qualitative and non-numeric
• Hypothesis driven
Cellular
Molecular
Post-Genomic
• Global/ holistic
Molecular
• Systems approach (DNA and RNA and Protein)
• Quantitative and highly numeric
• Not only hypothesis driven but also data driven
Cellular
Organism
Variation and Scale in Biomolecules
Scale
Entity
Dynamic Range
0
= 10
104 Genes
DNA
105 Transcripts
RNA
=
101 - 102
105 Molecules Metabolite
=
101 - 104
106 Forms
=
101 - 106
Protein
16probe
probe
pairs
representing
1 gene
3,200
features
representing
100
genes
400,000
pairs
representing
12,000
genes
Patient Variation – RNA profiling
The Need
• Increasing use of molecular profiling (clinical or basic) in
biology and medicine
• Can expression signatures teach us about physiological
systems response ?
• Genes/proteins do not act alone but have behaviourally
complex set of interactions
• Challenge is to focus on pathways rather than individual
genes
Approaches
• Network biology – A formal study of interrelations
– grounded in graph theory
• Systems biology – A study of the functional
assemblage of elements comprising a unified
whole (cell/organ/organism)
• Pathway biology – A sub-system study of cause
effect relationships with a defined start (input)
and end (output).
Pathway Biology
Pathway biology is an approach for understanding a
biological process through the functional association of
multiple gene products and metabolites defining networks
of cause-effect relationships.
• Mapping the molecular circuitry of a biological process
– Who talks to who and when?
• Key questions:
- What is a biological pathway?
- Does it have logic?
- What are its boundaries?
- How do pathways cross-talk?
Selectivity
Specificty
Why do we need them?
Dimensional complexity and scale
How do we tackle this?
• A sub-systems level approach
• Integration of experimental and in silico
approaches
• A diagram of a pathway would help:
•
•
•
•
Has to be:
Expressive and extensible
Semantically and visually unambiguous:
Mathematical translation
Software support
A simple protein interaction
dependency
• An adaptor protein (A) in the X signaling
pathway is known to interact with the activator of
X (B) requiring phosphorylation by kinase (C)
upon recruitment by A for activation.
– Here 3 conditions need to be satisfied for B to
activate factor X
Conditions
A the adaptor protein is present
B requires phosphorylation by C for activation
C binds A to act on B
Logic gate and block diagram
• In the example P=1 only when A and B
and C are all equal to 1
– Describes Boolian function called AND
• Can be notated as a block diagram
(electronic circuits)
A
B
C
P
X
Caution: Engineered versus Evolved
Systems
• Engineered Circuits
– Follow a defined set of rules
– Boundaries well defined
– Well understood knowledge base
• Evolved Pathways
– Rules are sketchy
– Boundaries not clear
– Knowledge clearly incomplete
Requires biological Notation
• Kohn in 1999 proposed a notation system based on
electronic circuit diagrams
– Highly complex and not supported by software
• Kitano in 2003 proposed simpler process/block
diagram notation
– Separate diagrams for process flow, entity states, and
regulatory networks – software compatible
• We have adapted the Kitano approach but emphasis
is on logic interactions and integrated cell-signalling,
interaction and regulatory networks.
– Integrated diagrams combining cause-effect relationships
with cellular compartment context
– Importantly the notation has and is evolving with use (since
2003)
Aims of Notation
•
•
•
•
•
•
Accessible to biologists
Compact
Show localisation clearly
Understandable for both wet & dry scientists
Tolerant of incomplete knowledge
Computable (Edinburgh Pathway Editor –
Igor Goryanin)
– Software tools
– Maps to SBML
www.SBGN.org : A graphical standard for notation of biological pathways
Notation Symbols
States, Transitions and Processes
Symbol
Name
Description
Protein state
transition
Conversion of a protein from one state to another. Details of the state
transition can be described in a sub diagram using the Kitano process
notation.
Complex
formation
Two or more proteins (or complexes) aggregating to form a complex.
Dissociation
A complex breaking apart to form two or more proteins or complexes.
Translocation
Movement of a protein or complex from one subcellular location to another.
State
The state of a complex or protein. If unlabeled the default or baseline state
is indicated. Other states such as phosphorylated, acetylated, or “activated”
are indicated by a suitable label. Details of the state are provided by a
suitable sub diagram (using the Kitano process notation).
Notation Symbols
Activation, Dependencies and Logic gates
Concepts
• State transition
• Activation
• Logic Gate
PKC delta
A13
PS727
Stat1
Mapk1
I
A14
Concepts
• Localisation
C7 (Stat1:Stat1)
Cytosol
Nucleus
A12
C7 (Stat1:Stat1)
Put it together…
Extracellular Space
Ifng
2
A1
Cell Membrane
C5(Ifng:Ifng)
C3(Ifnrg1:Ifnrg1)
Ifngr1
2
A3
C4(Ifng1:Ifnrg1:Ifng:
Ifng)
Cytosol
Camk2a
Ifngr2
2
C6 (Jak2:Ifngr2:Ifngr2)
PKC delta
A5
A15
A13
PS727
A4
Mapk1
A14
I
Stat1
C2(Ifngr2:IfngHD:Ifnrg1HD:Jak2:Stat1)
Ifgr1P
Jak2P
T3
Jak1
P
A9
Ifgr1P
Jak2
T1
T2
2PStat
C1(Ifng1:Ifng1:Ifngr1:Ifngr1:Ifngr2:Ifngr2:Jak2)
C7 (Stat1:Stat1)
Nucleus
A12
C7 (Stat1:Stat1)
A12
WP6.2 Genomics and microbiology
Interferon Signalling
> A family of related proteins
> Bind to specific receptors
Activation of Interferon signalling results in a defined gene expression:
Development of an
Increased
Inhibition of
inflammatory response
antigen presentation
cellular proliferation
Immune activation
Anti-infective
Anti-cancer
A logic diagram of the interferon
pathway
• Shown in the next slide is a subsystem
logic interaction diagram of the interferon
pathway.
• Includes spatial cellular organisation
• Generate a systems level informational
pathway diagram.
WP6.2interaction
Genomics and
microbiology
Logic
diagram
of interferon pathway
Figure II. Integrated Molecular Interaction Map for IFN-gamma functional network
C1
Extracellular space
Ifna
Ifng
Ifnb
C2
Ifng
A1
A
2
Extracellular
Antigen
=
Endocyt.
Ag-pep
Cytosolic
Ag-pep
HLA-DRA
Cell
membrane
A3
Ifngr1
Ifnar1
Ifnar2
Trail
Tnf-a
C7
C5
P
B2m
P
Y440
A16
Fnrsf6
C8
Trail-R1/2
C64
Tnfr1
A1
0
A5
Ingoing
endosome
Tnfsf6
A4
Ifngr2
Y466
HLA-A/B/C
HLA-DRB
*
C6
&
C65
|
Endocytosed
Antigen
Late outgoing
endosome membrane
D6
8
HLADRA
HLADRB
Clip
HLADRB
H2-DMa:
H2-DMb
Late outgoing
endosome
lumen
D7
0
Endocyt.
Ag-polypep
D6
7
HLADRB
H2-Oa:
H2-Ob
D6
9
D6
6
B2m
HLADRB
Endocyt.
Ag-pep
HLAA/B/C
Cytosolic
Ag-pep
D7
0
Endocyt.
Ag-pep
+
Ctss
Ctsb
Ctsd
A60
A8
P
Jak1
Jak2
P
P
Psmb8
P
Psmb9
Pias1
Psmb9
Psmb(1-4):
Psmb(1-4):
Psma(1-7)
D73
Ikbke
A13
Tbk1
B58
Psmb(1-4):
Psmb10
Y701
D72
Psmb(1-4):
B59
Prkcd
Psmb10
Arg31
Psmb(1-7)
A1
P1
S727
P
+
P
B73
P
B60
SH2
domain
B74
Ser
Psme2
Ser
Psme1
Psme2
Irf3
P
Camk2a
Stat2
Irf3
P
A15
|
Psme2
GDP GTP
A59
Rac1
D77
Psmb8
D76
Psma(1-7):
Psmb(1-4):
Psmb(1-4):
Psma(1-7)
+
Psmb10
Psmb9
P
Irf3:Irf3
Psmb9
Irf7
Irf7
B77
P
IAD
B78
P
IAD
|
C53
A14
A57
Mapk14
Ser51
P
eIF2-a
Casp8
C15
Casp9
C24
Casp3
Stat1
C25
C20
C16
C54
C56
I-kB
C48
Cebpb
Oas
P
C23
C21
C22
Prkr
C35
C3
A58
A18
Kpna1
Casp9
A19
A22
&
&
NF-kB Complex
Isgf3g
C52
C57
Casp7
C34
2,5A
C36
Ran
Stat1
Prkr
C66
&
2,5A System C49
&
&
Stat1
Casp1
Caspases Initiators
C50
NLS
B61
Irf3:Irf3
C56
Cytosolic
Ag-pep
C31
Inhibit Release of Cyt.C
C30
Bak
Cause Release of Cyt.C
Bax
C29
C33
C32
C28
Mitochondrial
membrane
Apaf-1
Cyt.C
Bax
NF-kB Complex
C2ta
Cytosolic
Antigen
Casp8
Prkr
B79
Psme2
D78
Caspases Effectors
C18
C62
Irf5
P
Psme1 (7)
C17
A17
Irf3
Psmb8
C4
C55
Mapk1
Mapk3
A34
P
Irf1
B64
Sat1
P
P
A62
A20
P
Psmb10
C13
Stat1
Stat1
P
IAD
B76
D75
Tradd
C14
Map2k2
C19
Irf7
Psme1 (7)
Ifng
P
Map2k1
|
A64
Cytosol
P
MAPKK
(Identity not
Known)
A65
+
P
IAD
B75
C11
C12
NLS
SH2
domain
&
Y690
D74
Psme1 (7)
C10
Pact
|
Vav
A1
2 =
A21
&
M
Ikbke
Tbk1
dsRNA
Fadd
A29
P
Psma(1-7):
Psma(1-7):
Map2k1/
Map3k1
Ptk2b
A7
Au P
A6
Hrmt1l2
Irf3
Psmb8
D71
C9
A67
Tyk2
Prkr
Ii
Clip
HLADRB
Mitochondiral
lumen
Bcl-xL
C26
C27
Bcl-2
Cyt.C
Lysosome membrane
Ii
HLADRB
B2m
H2-DMa:
H2-DMb
HLADRB
Ctsd
H2-Oa:
H2-Ob
Endocytosed
Antigen
+
D65
Lysosome
lumen
D64
Early outgoing
endosome
membrane HLADRB
+
HLADRB
Ii
Ctss
Ctsh
B2m
H2-DMa:
H2-DMb
H2-Oa:
H2-Ob
Early outgoing
endosome lumen
HLAA/B/C
Cytosolic
Ag-pep
Ctsb
Endocytosed
Antigen
+
HLAA/B/C
Cytosolic
Ag-pep
Ctsb
D63
Ctss
Golgi
membrane
Isgf3g
B2m
H2-DMa:
H2-DMb
H2-Oa:
H2-Ob
Golgi
lumen
ERcytosol
membrane
HLAA/B/C
Cytosolic
Ag-pep
Ctss
D60
D79
HLADRA
HLADRB
Ii
D61
ER
Lumen
HLA-DRA
B2m
H2-DMa:
H2-DMb
D61
Ii
HLA-DRB
&
Ctss
B2m
HLA-C
HLA-B
HLA-A
&
Tap1:
Tap2
HLAA/B/C
HLAA/B/C
Cytosolic
Ag-pep
H2-Oa:
H2-Ob
D62
D81 D80
Tap1
Tap2
Cytosolic
Ag-pep
Isgf3g
T
Cytosolic
Ag-pep
B2m
XPO1
P
A23
P
Nuclear
membrane
| A24
Stat1
+
A23
A23
Kpna3
B63
HLA-DRB
HLA-DRB
HLA-C
Ctss
Ctss
T
D24
HLA-A
D33
HLA-C
T
HLA-B
HLA-A
T
& +
T
D38
D39
B2m
D44
D45
HLA-B
T
Tap1
Tap2
Tap1
&
B2m
& +
Ep300
D5
3
D5
1
D5
2
0
8
D4
9D5
2D4
3
6D3
7
D3
5D3
2
D4
0
D4
1D4
D3
0
T
&
P
Irf7
Irf3:Irf3
P
Irf1
P
Irf7
Crebbp
Irf1
MCM5
P
B62
P
& &
Stat1
+
+
A25
B72
Irf3:Irf3
T
P
Stat2
A32
A27 A32 Y701
P
Nmi
Tap2
T
D29
D3
1D3
D28
D3
4
D27
Stat2
ISR
E
T
& +
D26
D4
6
D4
7D4
Ii
T
&
D25
Isgf3g
NE
S
Ii
HLA-DRA
HLA-DRA
Kpna4
P
Irf3
P
P
P
P
Irf7
Irf7
Irf7
Irf5
A26
|
P
P
+
P
P
Y701
A2
7
+ |
A27
Y701
& +
& Stat1
+
&
Stat1
&
+ +
A27
A33
A32
Tradd
A22
A33
NF-kB complex
Sp1
A28
Irf4
Icsbp1
BRCA1
Stat1:Stat1
B80
D5
4
Psmb9
B65
+
B55
D13
D14
D22
Usf1
D11
D17
C2ta
Psmb10
Psmb10
T
D56
Rfx5
Psme1
D18
Creb1
D58
S133
D6
T
Nfkb1
Nfkb1-Rela
B83 B89
&
T
B84
Rela
Psme2
T
Psme2
T
TFII-I
Ifna14
T
T
Ifna4
Ifna11
Ifna7
Gtf2i
T
A66
Tnfsf6
C44
B25
IRF7
ISRE
C47
T
B22
ISRE
T
Tnfsf6
C60
Oas
B56
B30
P
Ser
P
T
Casp1
B6
(=C43)
|
T
Tnfrsf6
T
Prkr
B8
C45
C46
T
B5
Oas
T
Irf2
Irf2
Ty r220
Ty r226
&
Irf1
Irf1
Irf1
Irf1
Ac
B10
P
Icsbp1
Icsbp1
B37
B34
B26
B12
B40
B42
B41
Stat6
Isgf3g
GAS
B13i
i
B3
Myc
B50
MSE
B33
B43
B46
CD23
b
GAS
T
B45
*
Il1b
B38
T
B52
B29
ISRE
Itgam
T
G1p2
B14
IRF-E
Cdkn1a
B49
T
Cybb
B24
T
B16
ET
S
+
T
B13i
B15
Fcer2a
T
B48
Sfpi1
B44
Stat1
Isgf3g
B47i
Fkbp4
B2
B27
B35
B36
GAT
E
D4
D5
PES
T
PES
T
&
B9
(=C42)
B32
(=C41)
Prkr
Tnfrsf6
Irf1
GAS
T
C40
Irf1
Ac
B39
Casp1
T
Ep300
Irf1
B17
T
B31
Icsbp1
D1
D2
D3
Casp7
Casp7
B1
GAS
T
Pcaf
D7
D8
T
C2ta
T
Casp8
Casp3
Stat2
B47i
i
Irf4
Casp7
Casp3
T
Casp8
Casp8
B4
Ifna1
B11
T
Casp8
T
Bcl-xl
C39
Stat1
A31
A31
T
*
Bcl-xL
T
C38
C37
Stat1
Stat1
B86
B84
T
Crebbp
D59
Crebbp
Bcl-2
Bak
C58
C57
Pcaf
K141 Ac
K144 Ac
C2ta
Bcl-2
T
C51
B86
B85
B67
PRDI
B21
|
B23
D12
P
PRDI
I
PRDIII
B57
D57
Psme1
D10
D20
D21
Psmb8
Psmb8
Usf1
Rfxap
D16
Nfyc
D19
Nfyb
Ccl5
Usf2
Rfxank
D9
PRDIV
D23
D15
Nfya
B82
ISRE
&
B88 B66
B81
B54
Atf2:Jun
|
Stat1
T
Hist4h4
T
IFNg
A30
B87
Hmga1
Psmb9
D55
Nucleoplasm
Bak
RnaseL
+
|
Tnfrsf6
Irf2
Tnfsf6
HLADRB
Ii
D8
2
HLADRB
*
Il12b
T
Il12a
DNA Degradation, ,Cell shrinkage and
Membrane blebbing etc
T
Protein Synthesis Inhibition
Apoptosis
RNA Degradation
Network Topology of IFN Pathway
Ct2a
Stat1
Irf1
Stat2
Irf3
Irf7
Paul Lacaze
Scale-Free Network - robustness
• Probability that each
protein/gene contributes to k
interactions follows a powerlaw distribution - value γ ~2
Degree distribution for undirected links,  =-1.6629
0
10
– consistent with the network
exhibiting scale-free
properties.
-1
10
P(k)
– Calculated average diameter
of the consensus pathway is
5.2 (average number of links
connecting each node)
-2
10
-3
10
0
10
1
10
Node degree
2
10
– Compactness of the pathway
diameter is open for network
segregation affording both a
level of flexibility and
adaptability in the pathway.
MΦ response to interferonγ
activation versus viral infection
Interferon
I Interferon+virus I
virus
Active sub-network determination
The p-value representing the significance of expression change pg for
gene g, which is converted into a z-score
z g   1 1  pg 
Φ = normal cumulative distribution function
The aggregate z-score zG of an entire sub-network G is obtained by
summing the individual z-scores of all genes in the sub-network where K
is the cardinality of G :
zG 
1
K
z
gG
g
The z-score of a randomly selected gene set of size K is computed and the
procedure repeated for different random sets, giving the mean μk and standard
deviation σK. Computation is repeated for all possible cardinalities K. Using
these estimates, the calibrated sub-network score is defined as
xG 
zG   K
K
A high score of xG indicates a biologically active sub-network
Ideker et al (2002) Bioinformatics, 18, 233-240.
psmb8 psmb10
il12a
bak
bcl2
tnfrsf6
bcl-xl
State-variant properties = Infected
Subnetwork 1
stat2
fkbp4
il1b
irf1
irf4
stat1
psmb9
tap1
sfpi1
stat6
irf2
casp8
casp1
fcer2a
isgf3g
prkr
irf3
casp7
c2ta
ctss
oas1
casp9
irf7
cdkn1a
lcsbp1
tnfsf6
irf5
g1p2
hla-c
hla-b
hla-a
hla-drb hla-dra
b2m
il12b
ii
itgam
cybb
tap2
casp3
hist4h4 ifna1 ifna11 lfnb
ifna14 ifna4 psme1 psme2 ccl5
psmb8 psmb10
il12a
bak
bcl2
tnfrsf6
State-variant property 3 = IFN treated
bcl-xl
Subnetwork 3
stat2
fkbp4
il1b
irf1
irf4
stat1
psmb9
tap1
sfpi1
stat6
irf2
casp8
casp1
fcer2a
isgf3g
prkr
irf3
casp7
c2ta
ctss
oas1
casp9
irf7
cdkn1a
lcsbp1
tnfsf6
irf5
g1p2
hla-c
hla-b
hla-a
hla-drb hla-dra
b2m
il12b
ii
itgam
cybb
tap2
casp3
hist4h4 ifna1 ifna11 lfnb
ifna14 ifna4 psme1 psme2 ccl5
psmb8 psmb10
il12a
bak
bcl2
tnfrsf6
State variant property 2 =
Infected+ IFN treated
bcl-xl
stat2
fkbp4
il1b
irf1
irf4
stat1
psmb9
tap1
sfpi1
stat6
irf2
casp8
casp1
fcer2a
isgf3g
prkr
irf3
casp7
c2ta
ctss
oas1
casp9
irf7
cdkn1a
lcsbp1
tnfsf6
irf5
g1p2
hla-c
hla-b
hla-a
hla-drb hla-dra
b2m
il12b
ii
itgam
cybb
tap2
casp3
hist4h4 ifna1 ifna11 lfnb
ifna14 ifna4 psme1 psme2 ccl5
Viral activated
Sub-network
Genes in Subnetworks
Infected Infected+treated treated
stat1
stat1
stat1
irf1
irf1
irf1
tap1
tap1
tap1
b2m
b2m
b2m
psmb10
psmb10
psmb10
casp1
casp1
casp1
psmb9
psmb9
psmb9
psmb8
psmb8
psmb8
psme1
psme1
psme1
psme2
psme2
psme2
ii
ii
ii
tap2
tap2
g1p2
g1p2
irf7
irf7
ccl5
ccl5
casp7
prkr
cdk1a
casp8
ctss
c2ta
c2ta
lcsbp1
cybb
Shared
sub-network
Figure II. Integrated Molecular Interaction Map for IFN-gamma functional network
C1
Extracellular space
Ifna
Ifng
Ifnb
C2
Ifng
A1
A
2
Extracellular
Antigen
=
Endocyt.
Ag-pep
Cytosolic
Ag-pep
HLA-DRA
Cell
membrane
A3
Ifngr1
Ifnar1
Ifnar2
Trail
Tnf-a
C7
C5
P
B2m
P
Y440
A16
Fnrsf6
C8
Trail-R1/2
C64
Tnfr1
A1
0
A5
Ingoing
endosome
Tnfsf6
A4
Ifngr2
Y466
HLA-A/B/C
HLA-DRB
*
C6
&
C65
|
Endocytosed
Antigen
Late outgoing
endosome membrane
D6
8
HLADRA
HLADRB
Clip
HLADRB
H2-DMa:
H2-DMb
Late outgoing
endosome
lumen
D7
0
Endocyt.
Ag-polypep
D6
7
HLADRB
H2-Oa:
H2-Ob
D6
9
D6
6
B2m
HLADRB
Endocyt.
Ag-pep
HLAA/B/C
Cytosolic
Ag-pep
D7
0
Endocyt.
Ag-pep
+
Ctss
Ctsb
Ctsd
A60
A8
P
Jak1
Jak2
P
P
Psmb8
P
Psmb9
Pias1
Psmb9
Psmb(1-4):
Psmb(1-4):
Psma(1-7)
D73
Ikbke
A13
Tbk1
B58
Psmb(1-4):
Psmb10
Y701
D72
Psmb(1-4):
B59
Prkcd
Psmb10
Arg31
Psmb(1-7)
A1
P1
S727
P
+
P
B73
P
B60
SH2
domain
B74
Ser
Psme2
Ser
Psme1
Psme2
Irf3
P
Camk2a
Stat2
Irf3
P
A15
|
Psme2
GDP GTP
A59
Rac1
D77
Psmb8
D76
Psma(1-7):
Psmb(1-4):
Psmb(1-4):
Psma(1-7)
+
Psmb10
Psmb9
P
Irf3:Irf3
Psmb9
Irf7
Irf7
B77
P
IAD
B78
P
IAD
|
C53
A14
A57
Mapk14
Ser51
P
eIF2-a
Casp8
C15
Casp9
C24
Casp3
Stat1
C25
C20
C16
C54
C56
I-kB
C48
Cebpb
Oas
P
C23
C21
C22
Prkr
C35
C3
A58
A18
Kpna1
Casp9
A19
A22
&
&
NF-kB Complex
Isgf3g
C52
C57
Casp7
C34
2,5A
C36
Ran
Stat1
Prkr
C66
&
2,5A System C49
&
&
Stat1
Casp1
Caspases Initiators
C50
NLS
B61
Irf3:Irf3
C56
Cytosolic
Ag-pep
C31
Inhibit Release of Cyt.C
C30
Bak
Cause Release of Cyt.C
Bax
C29
C33
C32
C28
Mitochondrial
membrane
Apaf-1
Cyt.C
Bax
NF-kB Complex
C2ta
Cytosolic
Antigen
Casp8
Prkr
B79
Psme2
D78
Caspases Effectors
C18
C62
Irf5
P
Psme1 (7)
C17
A17
Irf3
Psmb8
C4
C55
Mapk1
Mapk3
A34
P
Irf1
B64
Sat1
P
P
A62
A20
P
Psmb10
C13
Stat1
Stat1
P
IAD
B76
D75
Tradd
C14
Map2k2
C19
Irf7
Psme1 (7)
Ifng
P
Map2k1
|
A64
Cytosol
P
MAPKK
(Identity not
Known)
A65
+
P
IAD
B75
C11
C12
NLS
SH2
domain
&
Y690
D74
Psme1 (7)
C10
Pact
|
Vav
A1
2 =
A21
&
M
Ikbke
Tbk1
dsRNA
Fadd
A29
P
Psma(1-7):
Psma(1-7):
Map2k1/
Map3k1
Ptk2b
A7
Au P
A6
Hrmt1l2
Irf3
Psmb8
D71
C9
A67
Tyk2
Prkr
Ii
Clip
HLADRB
Mitochondiral
lumen
Bcl-xL
C26
C27
Bcl-2
Cyt.C
Lysosome membrane
Ii
HLADRB
B2m
H2-DMa:
H2-DMb
HLADRB
Ctsd
H2-Oa:
H2-Ob
Endocytosed
Antigen
+
D65
Lysosome
lumen
D64
Early outgoing
endosome
membrane HLADRB
+
HLADRB
Ii
Ctss
Ctsh
B2m
H2-DMa:
H2-DMb
H2-Oa:
H2-Ob
Early outgoing
endosome lumen
HLAA/B/C
Cytosolic
Ag-pep
Ctsb
Endocytosed
Antigen
+
HLAA/B/C
Cytosolic
Ag-pep
Ctsb
D63
Ctss
Golgi
membrane
Isgf3g
B2m
H2-DMa:
H2-DMb
H2-Oa:
H2-Ob
Golgi
lumen
ERcytosol
membrane
HLAA/B/C
Cytosolic
Ag-pep
Ctss
D60
D79
HLADRA
HLADRB
Ii
D61
ER
Lumen
HLA-DRA
B2m
H2-DMa:
H2-DMb
D61
Ii
HLA-DRB
&
Ctss
B2m
HLA-C
HLA-B
HLA-A
&
Tap1:
Tap2
HLAA/B/C
HLAA/B/C
Cytosolic
Ag-pep
H2-Oa:
H2-Ob
D62
D81 D80
Tap1
Tap2
Cytosolic
Ag-pep
Isgf3g
T
Cytosolic
Ag-pep
B2m
XPO1
P
A23
P
Nuclear
membrane
| A24
Stat1
+
A23
A23
Kpna3
B63
HLA-DRB
HLA-DRB
HLA-C
Ctss
Ctss
T
D24
HLA-A
D33
HLA-C
T
HLA-B
HLA-A
T
& +
T
D38
D39
B2m
D44
D45
HLA-B
T
Tap1
Tap2
Tap1
&
B2m
& +
Ep300
D5
3
D5
1
D5
2
0
8
D4
9D5
2D4
3
6D3
7
D3
5D3
2
D4
0
D4
1D4
D3
0
T
&
P
Irf7
Irf3:Irf3
P
Irf1
P
Irf7
Crebbp
Irf1
MCM5
P
B62
P
& &
Stat1
+
+
A25
B72
Irf3:Irf3
T
P
Stat2
A32
A27 A32 Y701
P
Nmi
Tap2
T
D29
D3
1D3
D28
D3
4
D27
Stat2
ISR
E
T
& +
D26
D4
6
D4
7D4
Ii
T
&
D25
Isgf3g
NE
S
Ii
HLA-DRA
HLA-DRA
Kpna4
P
Irf3
P
P
P
P
Irf7
Irf7
Irf7
Irf5
A26
|
P
P
+
P
P
Y701
A2
7
+ |
A27
Y701
& +
& Stat1
+
&
Stat1
&
+ +
A27
A33
A32
Tradd
A22
A33
NF-kB complex
Sp1
A28
Irf4
Icsbp1
BRCA1
Stat1:Stat1
B80
D5
4
Psmb9
B65
+
B55
D13
D14
D22
Usf1
D11
D17
C2ta
Psmb10
Psmb10
T
D56
Rfx5
Psme1
D18
Creb1
D58
S133
D6
T
Nfkb1
Nfkb1-Rela
B83 B89
&
T
B84
Rela
Psme2
T
Psme2
T
TFII-I
Ifna14
T
T
Ifna4
Ifna11
Ifna7
Gtf2i
T
A66
Tnfsf6
C44
B25
IRF7
ISRE
C47
T
B22
ISRE
T
Tnfsf6
C60
Oas
B56
B30
P
Ser
P
T
Casp1
B6
(=C43)
|
T
Tnfrsf6
T
Prkr
B8
C45
C46
T
B5
Oas
T
Irf2
Irf2
Ty r220
Ty r226
&
Irf1
Irf1
Irf1
Irf1
Ac
B10
P
Icsbp1
Icsbp1
B37
B34
B26
B12
B40
B42
B41
Stat6
Isgf3g
GAS
B13i
i
B3
Myc
B50
MSE
B33
B43
B46
CD23
b
GAS
T
B45
*
Il1b
B38
T
B52
B29
ISRE
Itgam
T
G1p2
B14
IRF-E
Cdkn1a
B49
T
Cybb
B24
T
B16
ET
S
+
T
B13i
B15
Fcer2a
T
B48
Sfpi1
B44
Stat1
Isgf3g
B47i
Fkbp4
B2
B27
B35
B36
GAT
E
D4
D5
PES
T
PES
T
&
B9
(=C42)
B32
(=C41)
Prkr
Tnfrsf6
Irf1
GAS
T
C40
Irf1
Ac
B39
Casp1
T
Ep300
Irf1
B17
T
B31
Icsbp1
D1
D2
D3
Casp7
Casp7
B1
GAS
T
Pcaf
D7
D8
T
C2ta
T
Casp8
Casp3
Stat2
B47i
i
Irf4
Casp7
Casp3
T
Casp8
Casp8
B4
Ifna1
B11
T
Casp8
T
Bcl-xl
C39
Stat1
A31
A31
T
*
Bcl-xL
T
C38
C37
Stat1
Stat1
B86
B84
T
Crebbp
D59
Crebbp
Bcl-2
Bak
C58
C57
Pcaf
K141 Ac
K144 Ac
C2ta
Bcl-2
T
C51
B86
B85
B67
PRDI
B21
|
B23
D12
P
PRDI
I
PRDIII
B57
D57
Psme1
D10
D20
D21
Psmb8
Psmb8
Usf1
Rfxap
D16
Nfyc
D19
Nfyb
Ccl5
Usf2
Rfxank
D9
PRDIV
D23
D15
Nfya
B82
ISRE
&
B88 B66
B81
B54
Atf2:Jun
|
Stat1
T
Hist4h4
T
IFNg
A30
B87
Hmga1
Psmb9
D55
Nucleoplasm
Bak
RnaseL
+
|
Tnfrsf6
Irf2
Tnfsf6
HLADRB
Ii
D8
2
HLADRB
*
Il12b
T
Il12a
DNA Degradation, ,Cell shrinkage and
Membrane blebbing etc
T
Protein Synthesis Inhibition
Apoptosis
RNA Degradation
MHC class II surface expression
Linking wet to dry and back
• Knowledge discovery (data mining):
1. Mapping and finding patterns
2. Forming hypotheses
3. Test using in vivo/in vitro experiments
• Simulation-based analysis (modelling):
1. In silico hypothesis testing
2. Forming predictions
3. Confirm using in vivo/in vitro experiments
A Patient’s View
Challenge is to measure pathway
logic in situ
• Future need to
develop sensors that
register and interact
with pathway logic:
Genomic
nanoprocessors
• Aim to develop first
generation bioswitch as a building
block for biosensor
and future research
in bio-computing
Initial embodiment of a
genomic nanoprocessor
– a DNA molecule.....
- Allows genomic interactions
(biomolecular detection)
– which can switch between
two configurations.....
- Allows logic micro/nanoprocessing
- Holliday junction
– with switching controlled by
electronics
- Electrochemically controlled switching
Genomic Nanoprocessors
Solution
λ1
λ2
Attached bioswitches
Electrochemical
Film
Metal Electrode
Silicon
Dioxide
Branched DNA Intermediates – 4 Way HJs
• Screening by ions:
U (r)  k
q1q2
r
U (r)  k '
q1q2  r / D
e
r
• Collapses the branch point
• Helices stack in coaxial pairs
‘OPEN’ Conformation
‘CLOSED’ Conformation
Detecting HJ Switching with FRET
Donor
Acceptor
Emission
Excitation
Excitation
Emission
Light spectrum
>r0
λ1
D
fluorescence
λ1
λ2
A
A
D
w avelength / nm
‘OPEN’
λ3
High salt
FRET occurs
A
fluorescence
Low salt
No FRET
< r0
w avelength / nm
ro ~ 100Å
‘CLOSED’
Preliminary Device Demonstration
Electrochemical switching of HJ populations in a droplet
BIOMOLECULE
INSULATION
Thermal Oxide
METAL
ELECTRODE
Silicon Substrate
Normalised Intensity
120000
1 M HJ solution
After Al generation
1
100000
Counts
80000
60000
40000
20000
00
450
500
550
600
Wavelength / nm
650
700
Future promise: DNA computing
element as a potential medicine
• DNA switch as a computing/logic processor
• Integrated pathway discovery incorporating laboratory and
clinical research with DNA computing device
• Advanced intelligent biosensors
Programmed to interface with pathway logic
• AI analysis of molecular signatures
Challenge of detecting and monitoring of signature as part of a
closed system – bio-intelligent medication
Convergence of disciplines in
pathway medicine
Therapeutics
Biochips
Systems Biology
Informatics & AI
Microelectronics
In situ
In vitro
Pathway
Nanoprocessors
Diagnostics
Devices
In silico
Thank you
www.gti.ed.ac.uk
Peter Ghazal, Stuart Moodie, Alan J. Ross, Garwin Sing, Anatoly Sorokin, Adriano
Velasque Werhli, Lily Cho, Tobias Frankenberg, Li-kun Phng, Zhenjie Xu, Lee Chuin Yao
, Kevin Robertson, Paul Dickinson, Marie Craigon, Thorsten Forster, Douglas Roy, John
Beattie, Alex Selkov, Igor Goryanin, Dirk Husmeier. Amy Buck, Jon Terry, Chris
Mountford, Jason Crain, Anthony Walton, Colin Campbell, Andy Mount,