NoPain – Meeting Monika Heiner Christian Rohr Department of Computer Science Brandenburg University of Technology Cottbus http://www-dssz.informatik.tu-cottbus.de Jan 28, 2014
NoPain – Meeting
Monika Heiner Christian Rohr
Department of Computer ScienceBrandenburg University of Technology Cottbus
http://www-dssz.informatik.tu-cottbus.de
Jan 28, 2014
Work Packages
GoalsWP1Coloured hybridPetri nets (HPN C)
WP2Connection toMATLAB
WP3
Efficient simulationofHPN C
WP4
Design of the“simulation lab”
WP5
Outline of the “simu-lation database”
WP6Implementationof the “simulationdatabase”
WP7 Preparation of theusermanuals
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
WP3
Efficient simulation of HPN C
a. Predecessor WPs: BTU-WP1, OvGUM-WP1b. Successor WPs: BTU-WP4
Examination of the Petri net models with regard to parallelisationpotentialInvestigation of optimisation possibilities and performancecomparisons with alternative tools, i.e. StochKit2, Cain. . .
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
Starting point: The Signaling Petri Net-Based Simulator: ANon-Parametric Strategy for Characterizing the Dynamics ofCell-Specific Signaling NetworksD. Ruths, M. Muller, Jen-Te Tseng, L. Nakhleh, P. T. RamPublished: February 29, 2008; DOI: 10.1371/journal.pcbi.1000005„The key insight behind our approach is the assumption that, whileall network parameters determine the actual signal propagation tosome extent, the network connectivity is the most significant singledeterminant. While this is clearly a gross simplification, severalresearchers have observed that the connectivity of a biologicalnetwork dictates, to a great extent, the network’s dynamics.”
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
„Simulation of timed Petri nets with variable auto-concurrency”.The least possible time step is 1 time unit.All enabled transitions that are not mutually exclusive, are forced tofire within a time-step, something like maximum step.When the net is filled up with tokens, every transition will fire.
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
Transition firingGenerate a random sequence of all transitions t ∈ PN .No extra conflict resolution needed, because of serial firing.Maximum step ⊆ random sequences
ExampleRandom sequences:
1 (T1, T3, T2, T4)→ {1, 1, 1, 0}2 (T1, T2, T4, T3)→ {1, 1, 1, 1}3 (T3, T4, T2, T1)→ {1, 1, 0, 0}
Maximum step:1 {T1} → {1, 1, 0, 0}
A B
C
D
T2
T1 T3
T4
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
Transition firingA transition fires concurrently to itself, i.e. token flow increases.How often a transition concurrently fires depends on its enablednessdegree and is randomly determined.firing rate = random[0, enablness degree]This approximates the stochastic behaviour of mass-action kinetics.
Example(T1, T2, T3, T4)→ {1, 5, 10, 0}T1 = 1→ 0→ {1, 5, 6, 2}T2 = 5→ 4→ {1, 5, 10, 2}T3 = 5→ 2→ {1, 5, 10, 4}T4 = 4→ 4→ {1, 5, 10, 0}
A
5
B
6
C
D
T2
T1 T3
T4
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
Transition firingA transition fires concurrently to itself, i.e. token flow increases.How often a transition concurrently fires depends on its enablednessdegree and is randomly determined.firing rate = random[0, enablness degree]This approximates the stochastic behaviour of mass-action kinetics.
Example(T1, T2, T3, T4)→ {1, 5, 10, 0}T1 = 1→ 0→ {1, 5, 6, 2}T2 = 5→ 4→ {1, 5, 10, 2}T3 = 5→ 2→ {1, 5, 10, 4}T4 = 4→ 4→ {1, 5, 10, 0}
A
5
B
6
C
D
T2
T1 T3
T4
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
Transition firingA transition fires concurrently to itself, i.e. token flow increases.How often a transition concurrently fires depends on its enablednessdegree and is randomly determined.firing rate = random[0, enablness degree]This approximates the stochastic behaviour of mass-action kinetics.
Example(T1, T2, T3, T4)→ {1, 5, 10, 0}T1 = 1→ 0→ {1, 5, 6, 2}T2 = 5→ 4→ {1, 5, 10, 2}T3 = 5→ 2→ {1, 5, 10, 4}T4 = 4→ 4→ {1, 5, 10, 0}
A
5
B
6
C
D
T2
T1 T3
T4
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
Transition firingA transition fires concurrently to itself, i.e. token flow increases.How often a transition concurrently fires depends on its enablednessdegree and is randomly determined.firing rate = random[0, enablness degree]This approximates the stochastic behaviour of mass-action kinetics.
Example(T1, T2, T3, T4)→ {1, 5, 10, 0}T1 = 1→ 0→ {1, 5, 6, 2}T2 = 5→ 4→ {1, 5, 10, 2}T3 = 5→ 2→ {1, 5, 10, 4}T4 = 4→ 4→ {1, 5, 10, 0}
A
5
B
6
C
D
T2
T1 T3
T4
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
Transition firingA transition fires concurrently to itself, i.e. token flow increases.How often a transition concurrently fires depends on its enablednessdegree and is randomly determined.firing rate = random[0, enablness degree]This approximates the stochastic behaviour of mass-action kinetics.
Example(T1, T2, T3, T4)→ {1, 5, 10, 0}T1 = 1→ 0→ {1, 5, 6, 2}T2 = 5→ 4→ {1, 5, 10, 2}T3 = 5→ 2→ {1, 5, 10, 4}T4 = 4→ 4→ {1, 5, 10, 0}
A
5
B
6
C
D
T2
T1 T3
T4
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulationAlgorithm 1 Parameter-free simulation algorithmRequire: PN with initial marking m0, time interval [τ0, τmax], runs rmax
Ensure: marking m at time point τmax
1: for r = 0; r < rmax; r ← r + 1 do2: initRand(seed)3: time τ ← τ0, marking m← m0, Tr ← T4: while τ <= τmax do5: Tr ← random_shuffle(Tr)6: for all transitions tj ∈ Tr do7: e← enablednessDegree(tj ,m)8: f ← random(0, e)9: m← m+ f ∗∆tj
10: end for11: generateResultPoint(τ,m)12: τ ← τ + 113: end while14: end for
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
ExampleRKIP inhibited ERK Pathway [Gilbert et al. 2006]
Raf1Star
Ns1
RKIP
Ns2
Raf1Star RKIPs3
ERKPP
s9
MEKPP ERKs8
Raf1Star RKIP ERKPP
s4 RKIPP RPs11
MEKPP
Ns7
ERK
Ns5
RKIPP
s6
RP
Ns10
r1 r2
r3 r4
r6 r7 r9 r10r5
r8
r11
RKIP/MEK-ERK signalling pathway [wolkenhauer 2003], [Calder 2005]
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
RKIP inhibited ERK Pathway
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(a) SPN 100 runs (<1s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(b) SPN 1,000 runs (<1s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(c) SPN 10,000 runs (<1s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(d) SPN 100,000 runs (2s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(e) QPN 100 runs (<1s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(f) QPN 1,000 runs (<1s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(g) QPN 10,000 runs (<1s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(h) QPN 100,000 runs (3s)
Figure: ERK, N=1
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
RKIP inhibited ERK Pathway
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(a) SPN 100 runs (<1s)
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(b) SPN 1,000 runs (<1s)
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(c) SPN 10,000 runs (8s)
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(d) SPN 100,000 runs (1m19s)
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(e) QPN 100 runs (<1s)
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(f) QPN 1,000 runs (<1s)
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(g) QPN 10,000 runs (<1s)
0 10 20 30 40 500
2
4
6
8
10
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(h) QPN 100,000 runs (4s)
Figure: ERK, N=10
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
RKIP inhibited ERK Pathway
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(a) SPN 100 runs (1s)
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(b) SPN 1,000 runs (9s)
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(c) SPN 10,000 runs (1m41s)
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(d) SPN 100,000 runs (17m4s)
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(e) QPN 100 runs (<1s)
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(f) QPN 1,000 runs (<1s)
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(g) QPN 10,000 runs (<1s)
0 10 20 30 40 500
20
40
60
80
100
Raf1StarRKIPRaf1Star_RKIP
ERKPPMEKPP_ERK
Raf1Star_RKIP_ERKPP
RKIPP_RP
MEKPPERKRKIPPRP
(h) QPN 100,000 runs (4s)
Figure: ERK, N=100
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
ExampleMitogen-activated Protein Kinase[Huang et al. 1996], [Gilbert et al. 2007]
e1
e2
k
N
k kkpp
kk
N
kk kkkp
kkk N
kkk e1
kkkp
kkkp e2
kkp
kkp kkkp
kkp ptase
kkpp
kkpp ptase
kkptase
kp
kp kkpp
kp ptase
kpp
kpp ptase
kptase
k k kk k k kk
k k ptase
k k ptase
k kk kkk k kk kkk
k kk ptase k kk ptase
k kkk e1
k kkk e2
a d kkk e1
a d kkk e2
a d kk ptase
a d kk kkk
a d kk ptase
a d kk kkk
a d k kk
a d k ptase
a d k kk
a d k ptase
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Mitogen-activated Protein Kinase
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(a) SPN 100 runs (4s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(b) SPN 1,000 runs (4s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(c) SPN 10,000 runs (4s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(d) SPN 100,000 runs (4s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(e) QPN 100 runs (4s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(f) QPN 1,000 runs (4s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(g) QPN 10,000 runs (4s)
0 10 20 30 40 500.0
0.2
0.4
0.6
0.8
1.0
kkkkkkkkkpkkpkkppkpkpp
(h) QPN 100,000 runs (4s)
Figure: MAPK, N=1
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Mitogen-activated Protein Kinase
0 10 20 30 40 500.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(a) SPN 100 runs (<1s)
0 10 20 30 40 500.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(b) SPN 1,000 runs (<1s)
0 10 20 30 40 500.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(c) SPN 10,000 runs (2s)
0 10 20 30 40 500.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(d) SPN 100,000 runs (25s)
0 50 100 150 2000.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(e) QPN 100 runs (<1s)
0 50 100 150 2000.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(f) QPN 1,000 runs (<1s)
0 50 100 150 2000.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(g) QPN 10,000 runs (3s)
0 50 100 150 2000.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
kkkkkkkkkpkkpkkppkpkpp
(h) QPN 100,000 runs (33s)
Figure: MAPK, N=4
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Mitogen-activated Protein Kinase
0 10 20 30 40 500
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(a) SPN 100 runs (<1s)
0 10 20 30 40 500
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(b) SPN 1,000 runs (<1s)
0 10 20 30 40 500
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(c) SPN 10,000 runs (3s)
0 10 20 30 40 500
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(d) SPN 100,000 runs (39s)
0 100 200 300 400 5000
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(e) QPN 100 runs (<1s)
0 100 200 300 400 5000
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(f) QPN 1,000 runs (<1s)
0 100 200 300 400 5000
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(g) QPN 10,000 runs (8s)
0 100 200 300 400 5000
2
4
6
8
10
kkkkkkkkkpkkpkkppkpkpp
(h) QPN 100,000 runs (1m20s)
Figure: MAPK, N=10
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
ExampleAngiogenesis [Napione et al. 2009]
Akt
N
AktP3
AktStar
DAG
N
DAGEEnz
N
Gab1 NGP3
GStarP3
GStarP3kP3
GStarPgP3
KdStar
N
KdStar
N
KdStar
N
KdStar
N
KdStar
N
KdStar
N
KdStarG KdStarGP3
KdStarGStar
KdStarGStarP3
KdStarGStarP3k
KdStarGStarP3kP3
KdStarGStarP3kStar
KdStarGStarP3kStarP2
KdStarGStarP3kStarP3
KdStarGStarP3kStarP3P2
KdStarGStarPgKdStarGStarPgP3
KdStarGStarPgStar
KdStarGStarPgStarP2
KdStarGStarPgStarP3
KdStarGStarPgStarP3P2
KdStarPg
KdStarPgStar
KdStarPgStarP2
Pip2
N
Pip2
N
Pip2
N
Pip2
N
Pip2
N
Pip2
N
Pip3
Pip3
Pip3
Pip3
P3k
N
P3k
N
P3k
N
Pg
N
Pg
N
Pg
N
Pg
N
Pten
N
PtP2
PtP3
PtP3P2
k0k1
k10k11
k12 k13
k14
k15
k16 k17
k18
k19
k2
k20
k21
k22 k23
k24
k25k26
k27
k28k29
k3
k30
k31k32
k33
k34k35
k36
k37k38
k39
k4
k40k41
k42
k43k44
k45
k46k47
k48
k49
k5
k50
k51
k52
k53k54
k55
k56k57
k58k59
k6
k60 k61
k62
k63
k7
k8k9
Compound Symbols:
KDR = Kd = n1
Gab1 = G = n3
Pi3k = P3k = n2
PlcGamma = Pg = n4
Pip3 = P3
Pip2 = P2 = n5
Pten = Pt = n6
Enz = E = n7
Akt = n8
Transition k3, k4, k5, k6, k7 are dead
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Angiogenesis
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(a) SPN 100 runs (<1s)
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(b) SPN 1,000 runs (<1s)
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(c) SPN 10,000 runs (<1s)
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(d) SPN 100,000 runs (7s)
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(e) QPN 100 runs (<1s)
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(f) QPN 1,000 runs (<1s)
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(g) QPN 10,000 runs (2s)
0 20 40 60 80 1000.0
0.2
0.4
0.6
0.8
1.0
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(h) QPN 100,000 runs (21s)
Figure: ANG, N=1
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Angiogenesis
0 20 40 60 80 1000
1
2
3
4
5
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(a) SPN 100 runs (<1s)
0 20 40 60 80 1000
1
2
3
4
5
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(b) SPN 1,000 runs (<1s)
0 20 40 60 80 1000
1
2
3
4
5
Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(c) SPN 10,000 runs (1s)
0 20 40 60 80 1000
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(d) SPN 100,000 runs (13s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(e) QPN 100 runs (<1s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(f) QPN 1,000 runs (<1s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(g) QPN 10,000 runs (2s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(h) QPN 100,000 runs (22s)
Figure: ANG, N=5
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Angiogenesis
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(a) SPN 100 runs (<1s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(b) SPN 1,000 runs (<1s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(c) SPN 10,000 runs (2s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(d) SPN 100,000 runs (23s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(e) QPN 100 runs (<1s)
0 20 40 60 80 1000
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(f) QPN 1,000 runs (<1s)
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(g) QPN 10,000 runs (2s)
0 20 40 60 80 1000
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Pip2Pip3P3kPgPtenPtP2PtP3PtP3P2
(h) QPN 100,000 runs (23s)
Figure: ANG, N=10
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Parameter-free simulation
ConclusionsMixed results:
1 performance comparable to stochastic simulation, some times better,some time worse
2 correct results for N = 1, contradictory for N > 1Potential solutions:
1 weighted shuffle of transitions2 mass-action kinetics: ct ·
∏p∈•t
(m(p)f(p,t)
)enabledness degree: minp∈•t
(⌊m(p)f(p,t)
⌋)
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Milestones
2013 2014 2015
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
WP1 M1
WP2 M2
WP3 M3
WP4 M4
WP5 M4
WP6 M5
WP7 M6
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Next steps...
Model compilation for simulation, i.e. a Petri net model and thesimulation algorithm will be compiled into an executable file.
Performance comparisons with alternative simulation tools, i.e.Stochkit2, Cain, Copasi, StochPy. . .
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014
Thank you for your attention!
AG Heiner (BTU Cottbus) NoPain Jan 28, 2014