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Transcript of An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational...
![Page 1: An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational and Systems Biology University of Pittsburgh School.](https://reader035.fdocuments.net/reader035/viewer/2022081602/551c00a8550346a34f8b4bda/html5/thumbnails/1.jpg)
An Introduction to Modeling Biochemical Signal Transduction
Jim Faeder Department of Computational and Systems BiologyUniversity of Pittsburgh School of Medicine
2014 CMACS Winter WorkshopLehman College
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Cell as Information Processor
http://en.wikipedia.org/wiki/Cell_signaling
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The cellular brain
http://www.biochemweb.org/fenteany/research/cell_migration/neutrophil.html
Original film from David Rogers (Vanderbuilt University)
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Organization of Signaling Networks
Yarden & Sliwkowski, Nature Rev. Mol. Cell Biol. 02: 127-137 (2001).
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Ras in network context
The Biology of Cancer (© Garland Science 2007)
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Figure 5.15 The Biology of Cancer (© Garland Science 2007)
Initiating Events: Receptor Aggregation
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Figure 6.12 The Biology of Cancer (© Garland Science 2007)
Initiating Events: Complex Formation “Effector” Activation
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Ras at Multiple Scales
The Biology of Cancer (© Garland Science 2007)
>20% human tumors carry Ras point mutations.
>90% in pancreatic cancer.
Transformed
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Ras Structure to Model
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Ras Structure to Model
Ras
pi3k ral
gn
sos raf~GDP ~GTP
Sos RasGAP Raf PI3K Ral
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Ras Biochemistry to RulesRas bound to GDP binds to Sos
nuc
Ras
eff
+
Sos
catRasGEF
RasSos
Sos binding catalyzes GDP/GTP exchange
RasSos RasSos
RasGTP binds Raf
Ras
+
Raf Ras Raf
RBD
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BioNetGen Language Formalizes Object-Oriented Description of Biochemistry
RasSos
Ras Raf
Molecules
Species Patterns
Raf
Sos(RasGEF) Ras(cat,nuc~GDP~GTP,eff) Raf(RBD)
RasSos
Sos(RasGEF!1).Ras(cat!1,nuc~GTP) Ras(nuc~GTP,eff!1).Raf(RBD!1)
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BioNetGen Language Formalizes Object-Oriented Description of Biochemistry
RasSos
Ras Raf
Molecules
Species Patterns
Raf
Sos(RasGEF) Ras(cat,nuc~GDP~GTP,eff) Raf(RBD)
RasSos
By leaving out a component this graph becomes a selector for multiple graphs.
Sos(RasGEF!1).Ras(cat!1,nuc~GTP) Ras(nuc~GTP,eff!1).Raf(RBD!1)
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BioNetGen Language Formalizes Object-Oriented Description of Biochemistry
RulesSos binding catalyzes GDP/GTP exchange
RasSos RasSos
RasGTP binds RafRas
+
Raf Ras Raf
Sos(RasGEF!1).Ras(cat!1,nuc~GDP,eff)-> \Sos(RasGEF!1).Ras(cat!1,nuc~GTP,eff) k2
Ras(nuc~GTP,eff)+Raf(RBD)<->Ras(nuc~GTP,eff!1).Raf(RBD!1) kp3,km3
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“Object-Oriented” Representation of Signaling Molecules
IgE(a,a)FceRI(a,b~U~P,g2~U~P)Lyn(U,SH2)Syk(tSH2,lY~U~P,aY~U~P)
BIONETGEN Language
Faeder et al., Meth. Mol. Biol. (2009) http://bionetgen.org
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Concise and Precise Description of Biochemical Knowledge
Transphosphorylation
component state change
Lyn(U!1).FceRI(b!1).FceRI(b~U)-> \Lyn(U!1).FceRI(b!1).FceRI(b~P)
Rules can query the local environment.
Transformation only takes place when conditions are favorable.
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Composition of a Rule-Based Model
Molecules Reaction Rulesbegin reaction_rules# Ligand-receptor binding 1 Rec(a) + Lig(l,l) <-> Rec(a!1).Lig(l!1,l) kp1, km1 Rec(a) + Lig(l,l) <-> Rec(a!1).Lig(l!1,l) kp1, km1
# Receptor-aggregation2 Rec(a) + Lig(l,l!1) <-> Rec(a!2).Lig(l!2,l!1) kp2,km2
# Constitutive Lyn-receptor binding3 Rec(b~Y) + Lyn(U,SH2) <-> Rec(b~Y!1).Lyn(U!1,SH2) kpL, kmL…
begin moleculesLig(l,l)Lyn(U,SH2)Syk(tSH2,l~U~P,a~U~P) Rec(a,b~U~P,g~U~P)end molecules
BioNetGen language
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AIM: Model the biochemical machinery by which cells process information (and respond to it).
Representation Simulation
Modeling cell signaling
How do we simulate dynamics of signaling networks?
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Standard Chemical Kinetics
Species Reactions
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Reaction Network Model of Signaling
Kholodenko et al., J. Biol. Chem. 274, 30169 (1999)
EGF
EGFR
GRB2
SOS
EGF
EGFR
GRB2
SOS
SHC
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Reaction Network Model of Signaling
Kholodenko et al., J. Biol. Chem. 274, 30169 (1999)
22 species 25 reactions
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General formulation of chemical kinetics (continuum limit)
x is vector of species concentrationsS is the “stoichiometry matrix”, Sij= number of molecules of species i consumed by reaction j.v is the “reaction flux vector”, vj is the rate of reaction j. For an elementary reaction,
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Representation Simulation
Modeling cell signaling
Reaction Network
How does set of Molecules and Rules get transformed into a Reaction Network of Species and Reactions?
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BioNetGen
Ab
Y1
B
A(b,Y1) B(a)
Molecules are structured objects (hierarchical graphs)
a
BNGL:
Faeder et al., In Methods in Molecular Biology: Systems Biology, Ed. I.V. Maly (2009)
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BioNetGen
Ab
Y1
B
A(b,Y1) B(a)
Molecules are structured objects (hierarchical graphs)
Rules define interactions (graph rewriting rules)
A B
+k+1
k-1
A B
A(b) + B(a) <-> A(b!1).B(a!1) kp1,km1
a bond between two components
a
Faeder et al., In Methods in Molecular Biology: Systems Biology, Ed. I.V. Maly (2009)
BNGL:
BNGL:
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Rules generate events
A B
+k+1
A BRule1
Ab
Y1
Ba+
Reaction1
1 2
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Rules generate events
A B
+k+1
A BRule1
Ab
Y1
Ba+
Reaction1
1 2
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Rules generate events
A B
+k+1
A BRule1
Ab
Y1
Ba
Ab
Y1
Ba
k+1
+
Reaction1
1 2 3
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Rules may specify contextual requirements
Ab
Y1
Rule2
p1
Ab
Y1 P
context not changed by rule
must be bound
Ab
Y1
Ba
3
Reaction2
A(b!+,Y1~U) -> A(b!+,Y1~P) p1BNGL:
context
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Rules may specify contextual requirements
Ab
Y1
Rule2
p1
Ab
Y1 P
context not changed by rule
must be bound
Ab
Y1
Ba
3
Reaction2
A(b!+,Y1~U) -> A(b!+,Y1~P) p1BNGL:
context
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Rules may specify contextual requirements
Ab
Y1
Rule2
p1
Ab
Y1 P
context not changed by rule
must be bound
Ab
Y1
Ba
3
Reaction2 p1
Ab
Y1
Ba
4
P
A(b!+,Y1~U) -> A(b!+,Y1~P) p1BNGL:
context
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Rules may generate multiple eventsSecond reaction generated by Rule 1
A B
+k+1
A BRule1
Ab
Y1
Ba
Ab
Y1
Ba
k+1
+
Reaction3
4 2 5
P
absence of context
P
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More complex rulesLyn FcεRI
γ2βPSH2
p*Lγ
P
P
Lyn FcεRI
Transphosphorylation of γ2 by SH2-bound Lyn
Generates 36 reactions (dimer model) with same rate constant
Lyn FcεRI
γ2
PSH2
p*Lγ Lyn FcεRI
γ2
PSH2
P
example
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Automatic Network Generation
Seed Species (4)
Reaction Rules (19)
New Reactions &
Species
FcεRI Model
Network
FcεRI
(IgE)2 Lyn Syk
Network
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Automatic Network Generation
Seed Species (4)
Reaction Rules (19)
FcεRI Model
FcεRI
(IgE)2 Lyn Syk
354 Species3680 Reactions
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Automatic Network Generation
Seed Species (4)
Reaction Rules (19)
FcεRI Model
FcεRI
(IgE)2 Lyn Syk
354 Species3680 Reactions