1. Signaling model for IFN-γ
•Step 0: (Optional) pre-equilibration of JAK1 with
R1, JAK2 with R2, R1 with R2, R1 with itself
(dimerization), R2 with itself, L with itself to make
functional IFN-γ, and STAT1 with itself.
•Parameters are taken largely from Yamada’s.
ligand
L
R1
L
R1
L
R1
R2
J1
S1
Y440
L
R1
R2
J1
S1
Y440
R2
R1
J2
R2
R1
J2
R2
Y
R1
Y
R2
Y R1
Y
R1 chain
JAK1
JAK2
R2 chain
Step 1: Ligand binds to
receptor.
+
ligand
L
R1
L
R1
L
R1
R2
J1
S1
Y440
L
R1
R2
J1
S1
Y440
R2
R1
J2
R2
R1
J2
R2
Y
R1
Y
R2
Y R1
Y
R1 chain
JAK1
JAK2
R2 chain
Ligand binding opens up the
receptor complex, thereby
allowing STAT1 dimer to bind.
A model of IFN-γ mediated JAK-STAT signaling that accounts for the combinatorial complexity
James S. Cavenaugh, James R. Faeder, Michael L. Blinov,
Jeremy E. Kozdon, William S. Hlavacek
Theoretical Biology and Biophysics Group
Los Alamos National Laboratory
Los Alamos, NM 87545
Acknowledgements: We
thank Emi Shudo, David
Torney, Amitabh Trehan,
Satoshi Yamada, Jin Yang,
and Zhike Zi for helpful
discussions.
Background
• IFN-γ is an inflammatory cytokine with
broad effects, especially important in
intracellular infections.
• IFN-γ is a compact homodimer:
fro m T. Kisseleva et al. Gene 285 (2002) 1-24
primary input
presumed
primary output
Assumptions
• Cell type: “liver cells”
• R1 = R2 = R
• JAK1 = JAK2 = JAK
• ligand-induced receptor
dimerization
• Combinatorial complexity
not fully addressed, e.g.:
– STAT1 dimerizesonly
after phosphorylation
– IFN-γ doesn’t bindIFN-
γ receptor without JAK.
• Only SOCS1 for negative
feedback
• No cross-talk considered
• No positive feedback
• No ligand internalization
or receptor recycling
BioNetGen modeling
Input: seed set,
reaction rules
Reaction network:
species and reactions
Time courses for all species and observables
by ODE or SSA
• Hlavaceket al., Biotechnol. Bioeng. 2003
• Blinov et al., Bioinformatics 2004
• Faeder et al., Complexity 2005
• Faeder et al., Proc. ACM 2005
• Blinov et al., BioConcur 2005
Version 2.0 (alpha) allows
bonds (edges in graphs).
ligand
L
R1
L
R1
L
R1
R2
J1
S1
Y440
L
R1
R2
J1
S1
Y440
R2
R1
J2
R2
R1
J2
R2
Y
R1
Y
R2
Y R1
Y
R1 chain
JAK1
JAK2
R2 chain
Step 2: JAK2 autophosphorylates itself.
Step 3: Phosphorylated JAK2 phosphorylates JAK1.
Step 4: Phosphorylated JAK1 phosphorylates R1.
Step 5: STAT1 dimer can bind to phosphorylated R1.
S1
R1
Y701
loc
S1
R1
Y701
loc
STAT1
Steps 6, 7, 8: STAT1
dimer gets phosphorylated
and dissociates, then
migrates to nucleus and
activates transcription of
various genes.
SHP-2
SOCS-1
TC-45
some inhibitors of
this pathway
Combinatorial complexity
• How many individual
reactions are possible?
• Current paradigm is to limit
possible rxns by assumptions.
– Are ad hoc assumptions the
origin of system behavior?
– Arbitrary: which to ignore?
• Rule based modeling
– Treats protein-protein
interactions as reaction rules
– Example: “R” notation in
organic chemistry
(e.g., R-X + Cl- → R-CL + X-)
– Can incorporate knowledge of
important domains
L
R1
R2
J1
S1
Y440
L
R1
R2
J1
S1
Y440
R2
R1
J2
R2
R1
J2
R2
Y
R2
Y R1
Y
R1 chain
JAK1
JAK2
R2 chain
ligand
L
R1
L
R1
+
3,646,350 possible species!
(without additional restrictions)
Goals
(1) To better account for the
consequences of combinatorial
complexity inherent in protein-
protein signaling interactions;
(2) To incorporate known
biochemistry in a more mechanistic
understanding of signaling events
Yamada’s model: The starting point
•Canonical model
–Based on earlier dogma for receptor, STAT1
dimerizations
•Extended by others but essentially unchanged
–Zi et al., FEBS Letters 579 (2005) 1101-1108
–Shudo et al., submitted.
Experimental benchmarks
• Four critical tyrosines (on JAK1, JAK2, R1,
STAT1) are phosphorylated within 1 min of
ligand binding.
• STAT1 begins entering nucleus after 15 min and
is complete by 30 min of ligand binding.
• 1st wave of transcription occurs very shortly
afterwards (within 15-30 min).
• STAT1 activation is inhibited within 1 h of
activation.
Results
• The total number of allowed species is 135,200.
• Achieved 53,836 species and 500,895 reactions in
13 iterations, then…
• Out of memory!
– Running on Dell Precision 670 Plus: Dual 3.60 GHz Intel
Xeon Processors, 2 MB L2 cache, 2 GB ECC RAM
– 40% completed
• Can the network be split into parts, i.e. separating
the signal transduction from the receptor binding?
YES (approximately)
Timecourses: without signal transduction
Timecourses: only signal transduction
Timecourses: only signal transductionConclusions
• Most extensive model to
date with BioNetGen.
– Major driving force for BNG
v.2
• Network generation
takes by far the longest
amount of time.
– Reaction generation took the
longest CPU time.
– ODE solver is very fast.
• OK to split the network
into subsections
• Unsplit network is likely
doable with optimized BNG
code
• “Full” complexity (with R1,
R2 dissociation) still too big.
• Meets initial experimental
benchmarks better than
Yamada’s model
• Sensitivity analysis and
parameter optimizations
needed for later events (too
slow, even more than
Yamada’s)
2. Results
• The total number of allowed species is 135,200.
• Achieved 53,836 species and 500,895 reactions in
13 iterations, then…
• Out of memory!
– Running on Dell Precision 670 Plus: Dual 3.60 GHz Intel
Xeon Processors, 2 MB L2 cache, 2 GB ECC RAM
– 40% completed
• Can the network be split into parts, i.e. separating
the signal transduction from the receptor binding?
YES (approximately)
3. Signaling model for IFN-γ
•Step 0: (Optional) pre-equilibration of JAK1 with
R1, JAK2 with R2, R1 with R2, R1 with itself
(dimerization), R2 with itself, L with itself to make
functional IFN-γ, and STAT1 with itself.
•Parameters are taken largely from Yamada’s.
ligand
L
R1
L
R1
L
R1
R2
J1
S1
Y440
L
R1
R2
J1
S1
Y440
R2
R1
J2
R2
R1
J2
R2
Y
R1
Y
R2
Y R1
Y
R1 chain
JAK1
JAK2
R2 chain
Step 1: Ligand binds to
receptor.
+
ligand
L
R1
L
R1
L
R1
R2
J1
S1
Y440
L
R1
R2
J1
S1
Y440
R2
R1
J2
R2
R1
J2
R2
Y
R1
Y
R2
Y R1
Y
R1 chain
JAK1
JAK2
R2 chain
Ligand binding opens up the
receptor complex, thereby
allowing STAT1 dimer to bind.
4. ligand
L
R1
L
R1
L
R1
R2
J1
S1
Y440
L
R1
R2
J1
S1
Y440
R2
R1
J2
R2
R1
J2
R2
Y
R1
Y
R2
Y R1
Y
R1 chain
JAK1
JAK2
R2 chain
Step 2: JAK2 autophosphorylates itself.
Step 3: Phosphorylated JAK2 phosphorylates JAK1.
Step 4: Phosphorylated JAK1 phosphorylates R1.
Step 5: STAT1 dimer can bind to phosphorylated R1.
S1
R1
Y701
loc
S1
R1
Y701
loc
STAT1
Steps 6, 7, 8: STAT1
dimer gets phosphorylated
and dissociates, then
migrates to nucleus and
activates transcription of
various genes.
SHP-2
SOCS-1
TC-45
some inhibitors of
this pathway
8. Experimental benchmarks
•Four critical tyrosines (on JAK1, JAK2, R1,
STAT1) are phosphorylated within 1 min of ligand
binding.
•STAT1 begins entering nucleus after 15 min and is
complete by 30 min of ligand binding.
•1st
wave of transcription occurs very shortly
afterwards (within 15-30 min).
•STAT1 activation is inhibited within 1 h of
activation.
9. Conclusions
• Most extensive model to date with BioNetGen.
– Major driving force for BNG v.2
• Splitting the network into subsections seems to work.
• Network generation takes by far the longest amount of
time.
– Reaction generation took the longest CPU time.
– ODE solver is very fast.
• Unsplit network is likely doable with optimized BNG
code:
– Better memory usage, parallelization, etc.
– Parallelization (to spread memory requirements)
– Stochastic simulation
– Improved logic in restricting reaction rules
• “Full” complexity (with R1, R2 dissociation) is too big.
10. Yamada’s model: The starting point
•Canonical model
–Based on earlier dogma for receptor, STAT1
dimerizations
•Extended by others but essentially unchanged
–Zi et al., FEBS Letters 579 (2005) 1101-1108
–Shudo et al., submitted.
11. Conclusions
• Most extensive model to
date with BioNetGen.
– Major driving force for BNG
v.2
• Network generation
takes by far the longest
amount of time.
– Reaction generation took the
longest CPU time.
– ODE solver is very fast.
• OK to split the network
into subsections
• Unsplit network is likely
doable with optimized BNG
code
• “Full” complexity (with R1,
R2 dissociation) still too big.
• Meets initial experimental
benchmarks better than
Yamada’s model
• Sensitivity analysis and
parameter optimizations
needed for later events (too
slow, even more than
Yamada’s)
