An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational...
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An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational and Systems Biology University of Pittsburgh School of Medicine 2014 CMACS Winter Workshop Lehman College
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Transcript of An Introduction to Modeling Biochemical Signal Transduction Jim Faeder Department of Computational...
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
Cell as Information Processor
http://en.wikipedia.org/wiki/Cell_signaling
The cellular brain
http://www.biochemweb.org/fenteany/research/cell_migration/neutrophil.html
Original film from David Rogers (Vanderbuilt University)
Organization of Signaling Networks
Yarden & Sliwkowski, Nature Rev. Mol. Cell Biol. 02: 127-137 (2001).
Ras in network context
The Biology of Cancer (© Garland Science 2007)
Figure 5.15 The Biology of Cancer (© Garland Science 2007)
Initiating Events: Receptor Aggregation
Figure 6.12 The Biology of Cancer (© Garland Science 2007)
Initiating Events: Complex Formation “Effector” Activation
Ras at Multiple Scales
The Biology of Cancer (© Garland Science 2007)
>20% human tumors carry Ras point mutations.
>90% in pancreatic cancer.
Transformed
Ras Structure to Model
Ras Structure to Model
Ras
pi3k ral
gn
sos raf~GDP ~GTP
Sos RasGAP Raf PI3K Ral
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
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)
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)
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
“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
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.
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
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?
Standard Chemical Kinetics
Species Reactions
Reaction Network Model of Signaling
Kholodenko et al., J. Biol. Chem. 274, 30169 (1999)
EGF
EGFR
GRB2
SOS
EGF
EGFR
GRB2
SOS
SHC
Reaction Network Model of Signaling
Kholodenko et al., J. Biol. Chem. 274, 30169 (1999)
22 species 25 reactions
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,
Representation Simulation
Modeling cell signaling
Reaction Network
How does set of Molecules and Rules get transformed into a Reaction Network of Species and Reactions?
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)
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:
Rules generate events
A B
+k+1
A BRule1
Ab
Y1
Ba+
Reaction1
1 2
Rules generate events
A B
+k+1
A BRule1
Ab
Y1
Ba+
Reaction1
1 2
Rules generate events
A B
+k+1
A BRule1
Ab
Y1
Ba
Ab
Y1
Ba
k+1
+
Reaction1
1 2 3
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
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
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
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
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
Automatic Network Generation
Seed Species (4)
Reaction Rules (19)
New Reactions &
Species
FcεRI Model
Network
FcεRI
(IgE)2 Lyn Syk
Network
Automatic Network Generation
Seed Species (4)
Reaction Rules (19)
FcεRI Model
FcεRI
(IgE)2 Lyn Syk
354 Species3680 Reactions
Automatic Network Generation
Seed Species (4)
Reaction Rules (19)
FcεRI Model
FcεRI
(IgE)2 Lyn Syk
354 Species3680 Reactions
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