Signaling model for IFN-•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-, and STAT1 with itself.

•Parameters are taken largely from Yamada’s.

ligandL

R1

L

R1

L

R1

R2

J1

S1

Y440

L

R1

R2

J1

S1

Y440

R2

R1

J2

R2

R1

J2R2

YR1

Y

R2

Y R1

Y

R1 chain

JAK1JAK2

R2 chain

Step 1: Ligand binds to receptor.

+

ligandL

R1

L

R1

L

R1

R2

J1

S1

Y440

L

R1

R2

J1

S1

Y440

R2

R1

J2

R2

R1

J2R2

YR1

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 GroupLos 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- is an inflammatory cytokine with broad effects, especially important in intracellular infections.

• IFN- 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 dimerizes only

after phosphorylation– IFN- doesn’t bind IFN-

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 observablesby ODE or SSA

• Hlavacek et 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).

ligandL

R1

L

R1

L

R1

R2

J1

S1

Y440

L

R1

R2

J1

S1

Y440

R2

R1

J2

R2

R1

J2R2

YR1

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

J2R2

Y

R2

Y R1

Y

R1 chain

JAK1JAK2

R2 chain

ligandL

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)

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)

Signaling model for IFN-•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-, and STAT1 with itself.

•Parameters are taken largely from Yamada’s.

ligandL

R1

L

R1

L

R1

R2

J1

S1

Y440

L

R1

R2

J1

S1

Y440

R2

R1

J2

R2

R1

J2R2

YR1

Y

R2

Y R1

Y

R1 chain

JAK1JAK2

R2 chain

Step 1: Ligand binds to receptor.

+

ligandL

R1

L

R1

L

R1

R2

J1

S1

Y440

L

R1

R2

J1

S1

Y440

R2

R1

J2

R2

R1

J2R2

YR1

Y

R2

Y R1

Y

R1 chain

JAK1

JAK2

R2 chain

Ligand binding opens up the receptor complex, thereby allowing STAT1 dimer to bind.

ligandL

R1

L

R1

L

R1

R2

J1

S1

Y440

L

R1

R2

J1

S1

Y440

R2

R1

J2

R2

R1

J2R2

YR1

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

Timecourses: without signal transduction

Timecourses: only signal transduction

Timecourses: only signal transduction

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.

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.

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.

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)