poster - ICSB 2005 v2
11
Signaling m odelfor IFN - •Step 0: (O ptional)pre-equilibration ofJAK1 w ith R1, JA K 2 w ith R2, R1 w ith R2, R1 w ith itself (dim erization), R2 w ith itself,L w ith itselfto m ake functionalIFN - , and STA T1 w ith itself. •Param etersare taken largely from Yam ada’s. ligand L R1 L R1 L R1 R2 J1 S1 Y 440 L R1 R2 J1 S1 Y 440 R2 R1 J2 R2 R1 J2 R2 Y R1 Y R2 Y R1 Y R1 chain JAK 1 JAK 2 R2 chain Step 1 :Ligand bindsto receptor. + ligand L R1 L R1 L R1 R2 J1 S1 Y 440 L R1 R2 J1 S1 Y 440 R2 R1 J2 R2 R1 J2 R2 Y R1 Y R2 Y R1 Y R1 chain JAK 1 JAK 2 R2 chain Ligand binding opensup the receptorcom plex,thereby allow ing STA T1 dim erto 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- isan inflam m atory cytokinew ith broad effects,especially im portantin intracellularinfections. • IFN- isacom pacthom odim er: from T.K isselevaetal. G ene 285 (2002)1-24 prim ary input presum ed prim ary output Assum ptions • Cell type: “livercells” • R1 = R2 = R • JA K 1 = JA K 2 = JA K • ligand-induced receptor dimerization • Com binatorialcom plexity notfully addressed, e.g.: – ST AT1 dim erizesonly afterphosphorylation – IFN - doesn’tbindIFN- receptorwithoutJA K. • O nly SO CS1 fornegative feedback • N o cross-talk considered • N o positivefeedback • No ligand internalization orreceptorrecycling BioN etG en m odeling Input: seed set, reaction rules R eaction netw ork: species and reactions Tim e courses forall species and observables by O D E orSSA • H lavaceket al., Biotechnol. Bioeng. 2003 • Blinov et al., Bioinform atics2004 • Faederet al., Com plexity 2005 • Faederet al., Proc. A CM 2005 • Blinov et al., BioConcur2005 Version 2.0 (alpha)allow s bonds(edgesin graphs). ligand L R1 L R1 L R1 R2 J1 S1 Y 440 L R1 R2 J1 S1 Y 440 R2 R1 J2 R2 R1 J2 R2 Y R1 Y R2 Y R1 Y R1 chain JAK 1 JAK 2 R2 chain Step 2 :JA K 2 autophosphorylatesitself. Step 3 :Phosphorylated JA K 2 phosphorylatesJA K 1. Step 4 :Phosphorylated JA K 1 phosphorylatesR1. Step 5 :STA T1 dim ercan bind to phosphorylated R1. S1 R1 Y 701 loc S1 R1 Y 701 loc STA T1 Steps6, 7, 8 :STA T1 dim ergetsphosphorylated and dissociates, then m igratesto nucleusand activatestranscription of variousgenes. SH P-2 SO CS-1 TC-45 som e inhibitorsof thispathw ay Com binatorialcom plexity • H ow m any individual reactionsarepossible? • C urrentparadigm is to lim it possible rxns by assum ptions. – A re ad hoc assum ptions the origin ofsystem behavior? – A rbitrary: w hichto ignore? • Rulebased m odeling –Treatsprotein-protein interactionsasreaction rules – Exam ple: “R ” notation in organicchem istry (e.g.,R-X + C l - R-C L + X - ) – C an incorporate know ledge of im portant dom ains L R1 R2 J1 S1 Y 440 L R1 R2 J1 S1 Y 440 R2 R1 J2 R2 R1 J2 R2 Y R2 Y R1 Y R1 chain JAK 1 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 Y am ada’sm odel:The starting point •Canonicalm odel –Based on earlierdogm a forreceptor, STA T1 dimerizations •Extended by othersbutessentially unchanged –Zietal., F E BS L etters 579 (2005)1101-1108 –Shudo etal., subm itted. Experim entalbenchm arks • Fourcritical tyrosines(on JAK 1,JAK2,R1, STA T1)are phosphorylated w ithin 1 m in of ligand binding. • STA T1 begins entering nucleus after15 m in and iscom pleteby 30 m in ofligand binding. •1 st waveoftranscription occursvery shortly afterw ards (w ithin 15-30 m in). • STA T1 activation is inhibited w ithin 1 h of activation. R esults • The totalnum berofallow ed speciesis135,200. • Achieved 53,836 species and 500,895 reactions in 13 iterations, then… • Outofm em ory! – Running on D ellPrecision 670 Plus:D ual3.60 G H z Intel X eon Processors,2 M B L2 cache,2 G B ECC RA M – 40% completed • Can the netw ork be splitinto parts,i.e. separating the signaltransduction from the receptorbinding? YES ( approximately) Tim ecourses:withoutsignaltransduction Tim ecourses:only signaltransduction Tim ecourses:only signaltransduction C onclusions • M ostextensive m odelto date w ith BioN etG en. – M ajordriving force forBN G v.2 • Network generation takesby farthe longest am ountoftim e. – Reaction generation took the longestCPU tim e. – O D E solverisvery fast. • O K to splitthe netw ork into subsections • U nsplitnetw ork islikely doable w ith optim ized BN G code • “Full” com plexity (w ith R1, R2 dissociation)stilltoo big. •M eetsinitialexperimental benchm arksbetterthan Y am ada’sm odel • Sensitivity analysisand param eteroptimizations needed forlaterevents(too slow , even m ore than Y am ada’s)
-
Upload
james-s-cavenaugh -
Category
Documents
-
view
219 -
download
0
Transcript of poster - ICSB 2005 v2
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)
