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European Bioinformatics Institute European Bioinformatics Institute - - PowerPoint PPT Presentation

Nov 26, 2023 156 likes •817 views

European Bioinformatics Institute European Bioinformatics Institute British outstation of the European Molecular Biology Laboratory Databases Sequences, structures Transcriptomics, Proteomics pathways, models Controlled

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SLIDE 1

European Bioinformatics Institute European Bioinformatics Institute

  • Databases

–

Sequences, structures

–

Transcriptomics, Proteomics pathways, models

–

Controlled vocabularies and dictionaries

  • Research groups

–

Structural Genomics (Thornton)

–

Molecular Evolution (Goldman)

–

Text-Mining (Rebholz-Schumman)

–

Computational Systems Biology (Le Novère)

–

Statistical array analysis (Huber)

–

Genomic analysis of regulatory systems (Luscombe)

–

Functional genomics (Bertone) > 2 500 000 hits per day

British outstation of the European Molecular Biology Laboratory

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SLIDE 2

Integration of Dopamine and Glutamate signals by DARPP-32

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SLIDE 3

The brain according to Le Novère The brain according to Le Novère

Thalamus sensory input

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SLIDE 4

The brain according to Le Novère The brain according to Le Novère

Thalamus Cortex sensory input

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SLIDE 5

The brain according to Le Novère The brain according to Le Novère

Thalamus Cortex Striatum sensory input cortico-striato-thalamic loop

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SLIDE 6

The brain according to Le Novère The brain according to Le Novère

Thalamus Cortex Striatum sensory input motor output

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SLIDE 7

The brain according to Le Novère The brain according to Le Novère

Thalamus Cortex Striatum sensory input motor output Limbic afferences

motivational aspects feelings

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SLIDE 8

The brain according to Le Novère The brain according to Le Novère

Thalamus Cortex Striatum sensory input motor output Limbic afferences

motivational aspects feelings

DA

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SLIDE 9

The brain according to Le Novère The brain according to Le Novère

Thalamus Cortex Striatum sensory input motor output Limbic afferences

motivational aspects feelings

DA ACh

global activity coordination PPTg

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SLIDE 10

Reminder of the anatomy Reminder of the anatomy

thalamus

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SLIDE 11

Neocortex Striatum GPe Subthalamic nucleus Thalamus GPi/SNr

“+” “-”

Glu GABA SNc DA

Dopamine mesotelencephalic pathway Dopamine mesotelencephalic pathway

Voluntary motion Emotional control Reward Learning Parkinson's Huntington Drug addiction Schizophrenia

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SLIDE 12

Link between reward and locomotor systems Link between reward and locomotor systems

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SLIDE 13

Multiple afferent signals Multiple afferent signals

Fundamental Neuroscience Squire et al. 2nd ed(2003)

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SLIDE 14

the Medium Spiny Neuron of the striatum the Medium Spiny Neuron of the striatum

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SLIDE 15

A spiny dendrite A spiny dendrite

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SLIDE 16

Biological model Biological model of DARPP-32 regulation

  • f DARPP-32 regulation

PP-1 Thr34 DARPP-32 PP-2B PKA cAMP Ca2+ D1 NMDA AMPA Dopamine Glutamate µ Cocaine Amphetamine Nicotine Opiate 5HT4/6 Serotonin fluoxetine

striato-nigral neuron

PP-1 Thr34 DARPP-32 PP-2B PKA cAMP Ca

2+

D2 NMDA AMPA Dopamine Glutamate δ Opiate Cocaine Amphetamine Nicotine A2A Caffein 5HT4/6 Serotonin fluoxetine haloperidol Antipsychotics

striato-pallidal neuron

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SLIDE 17

Biological model Biological model of DARPP-32 regulation

  • f DARPP-32 regulation

PP-1 Thr34 Thr75 DARPP-32 PP-2B PP-2A CDK 5 PKA cAMP Ca2+ D1 NMDA AMPA Dopamine Glutamate µ Cocaine Amphetamine Nicotine Opiate 5HT4/6 Serotonin fluoxetine

striato-nigral neuron

PP-1 Thr34 Thr75 DARPP-32 PP-2B PP-2A CDK5 PKA cAMP Ca

2+

D2 NMDA AMPA Dopamine Glutamate δ Opiate Cocaine Amphetamine Nicotine A2A Caffein 5HT4/6 Serotonin fluoxetine haloperidol Antipsychotics

striato-pallidal neuron

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SLIDE 18

Biological model Biological model of DARPP-32 regulation

  • f DARPP-32 regulation

PP-1 Thr34 Ser137 Thr75 DARPP-32 PP-2B CK1 PP-2A CDK 5 PP-2C PKA cAMP Ca2+ D1 NMDA AMPA Dopamine Glutamate µ Cocaine Amphetamine Nicotine Opiate 5HT4/6 Serotonin fluoxetine

striato-nigral neuron

PP-1 Thr34 Ser137 Thr75 DARPP-32 PP-2B CK1 PP-2A CDK5 PP-2C PKA cAMP Ca

2+

D2 NMDA AMPA Dopamine Glutamate δ Opiate Cocaine Amphetamine Nicotine A2A Caffein 5HT4/6 Serotonin fluoxetine haloperidol Antipsychotics

striato-pallidal neuron

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SLIDE 19

Biological model Biological model of DARPP-32 regulation

  • f DARPP-32 regulation

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK 5 PP-2C CK2 PKA cAMP Ca2+ D1 NMDA AMPA Dopamine Glutamate µ Cocaine Amphetamine Nicotine Opiate 5HT4/6 Serotonin fluoxetine

striato-nigral neuron

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK5 PP-2C CK2 PKA cAMP Ca

2+

D2 NMDA AMPA Dopamine Glutamate δ Opiate Cocaine Amphetamine Nicotine A2A Caffein 5HT4/6 Serotonin fluoxetine haloperidol Antipsychotics

striato-pallidal neuron

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SLIDE 20

Biological model Biological model of DARPP-32 regulation

  • f DARPP-32 regulation

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK 5 PP-2C CK2 PKA cAMP Ca2+ D1 NMDA AMPA Dopamine Glutamate µ Cocaine Amphetamine Nicotine Opiate 5HT4/6 Serotonin fluoxetine

striato-nigral neuron

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK5 PP-2C CK2 PKA cAMP Ca

2+

D2 NMDA AMPA Dopamine Glutamate δ Opiate Cocaine Amphetamine Nicotine A2A Caffein 5HT4/6 Serotonin fluoxetine haloperidol Antipsychotics

striato-pallidal neuron

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SLIDE 21

Biological model Biological model of DARPP-32 regulation

  • f DARPP-32 regulation

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK 5 PP-2C CK2 PKA cAMP Ca2+ D1 NMDA AMPA Dopamine Glutamate µ Cocaine Amphetamine Nicotine Opiate 5HT4/6 Serotonin fluoxetine

striato-nigral neuron

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK5 PP-2C CK2 PKA cAMP Ca

2+

D2 NMDA AMPA Dopamine Glutamate δ Opiate Cocaine Amphetamine Nicotine A2A Caffein 5HT4/6 Serotonin fluoxetine haloperidol Antipsychotics

striato-pallidal neuron

PDE PDE

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SLIDE 22

Negative loops: “russian dolls” Negative loops: “russian dolls”

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

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SLIDE 23

All the cybernetics in one system All the cybernetics in one system

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

Thr75

PKA PP-2A CDK5

positive feedback

+ +

cAMP PDE

?

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SLIDE 24

All the cybernetics in one system All the cybernetics in one system

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

Thr75

PKA PP-2A CDK5

?

+ +

positive feedback

cAMP PDE

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SLIDE 25

All the cybernetics in one system All the cybernetics in one system

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

Thr75

PKA PP-2A CDK5

?

negative feedback

+

  • cAMP

PDE

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SLIDE 26

All the cybernetics in one system All the cybernetics in one system

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

Thr75

PKA PP-2A CDK5

?

negative feedback

+

  • cAMP

PDE

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SLIDE 27

All the cybernetics in one system All the cybernetics in one system

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

Thr75

PKA PP-2A CDK5

?

incoherent feedforward

+

  • cAMP

PDE

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SLIDE 28

Chemical model Chemical model of DARPP-32 regulation

  • f DARPP-32 regulation

D137

D

D34:137 D34:75:137 D34 D75 D34:75 D75-137 PP2B PKA PP-2C CK1 PP2A CDK5 Ca2+ cAMP PDE AMP PKA2R2 PKA2R PDEP PP2BiCa2 PP2Bi PP2AP CK1P

Ø Ø

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SLIDE 29

Chemical model Chemical model of DARPP-32 regulation

  • f DARPP-32 regulation

D137

D

D34:137 D34:75:137 D34 D75 D34:75 D75-137 PP2B PKA PP-2C CK1 PP2A CDK5 Ca2+ cAMP PDE AMP PKA2R2 PKA2R PDEP PP2BiCa2 PP2Bi PP2AP CK1P

Ø Ø stolen from DOQCS

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SLIDE 30

Choose the right formalism Choose the right formalism

E+S ES kcat kds kas kap kdp EP E+P

catalysis irreversible

ES kcat ksa ksd E+S E+P

product is consumed before rebinding d[P] [E] kcat = dt Km 1 + [S]

S P E

steady-state

E+S ES kcat kds kas kap kdp EP E+P kcat'

d[P] = kdp[EP] - kap[E][P] dt

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SLIDE 31

Effect of the wrong choice Effect of the wrong choice

D34 D34-75 D D-75 PP-2B PP-2A PKA PKA PP-2B CDK5 PP-2A CDK5

75% 25%

D34 D34-75 D D-75 PP-2B PP-2A PKA PKA PP-2B CDK5 PP-2A CDK5

45% 15% 30% 10%

elementary steps

D34 D34-75 D D-75 PP-2B PP-2A PKA PKA PP-2B CDK5 PP-2A CDK5

75% 25%

D34 D34-75 D D-75 PP-2B PP-2A PKA PKA PP-2B CDK5 PP-2A CDK5

45% 14% 30% 8%

“Michaelis Menten”

1) unbalance; 2) Σ≠ 100%!!!

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SLIDE 32

Briggs-Haldane Briggs-Haldane

[E]=[E0]-[ES]

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SLIDE 33

Briggs-Haldane Briggs-Haldane

[E]=[E0]-[ES]

steady-state!!!

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SLIDE 34

Chemical model Chemical model of DARPP-32 regulation

  • f DARPP-32 regulation
  • D + PKA  D_PKA  D34 + PKA
  • D34 + PP2B  D34_PP2B  D + PP2B
  • D75 + PKA  D75_PKA  D34-75 + PKA
  • D34-75 + PP2B  D34-75_PP2B  D75 + PP2B
  • D137 + PKA  D137_PKA  D34-137 + PKA
  • D34-137 + PP2B  D34-135_PP2B  D137 + PP2B
  • D75-137 + PKA  D75-137_PKA  D34-75-137 + PKA
  • D34-75-137 + PP2B  D34-75-135_PP2B  D75-137 + PP2
  • PP2A + PKA  PP2A_PKA  PP2AP + PKA
  • PP2ACa + PKA  PP2ACa_PKA  PP2APCa + PKA
  • Ø  Ca2+
  • Ca2+  Ø
  • PP2Bi + 2Ca  PP2Bi_Ca2
  • PP2Bi_Ca + 2Ca  PP2B
  • R2_PKA2 + cAMP  cAMP_R2_PKA2
  • cAMP_R2_PKA2 + cAMP  cAMP2_R2_PKA2
  • cAMP2_R2_PKA2 + cAMP  cAMP3_R2_PKA2
  • cAMP3_R2_PKA2 + cAMP  cAMP4_R2_PKA2
  • cAMP4_R2_PKA2  cAMP4_R2_PKA + PKA
  • cAMP4_R2_PKA  cAMP4_R2 + PKA
  • PKA + PDE  PKA_PDE  PKA + PDEP
  • cAMP + PDE  cAMP_PDE  AMP + PDE
  • cAMP + PDEP  cAMP_PDEP  AMP + PDEP

77 species, 145 reactions

  • D + CDK5  D_CDK5  D75 + CDK5
  • D75 + PP2A  D75_PP2A  D + PP2A
  • D75 + PP2ACa  D75_PP2ACa  D + PP2ACa
  • D75 + PP2AP  D75_PP2AP  D + PP2AP
  • D75 + PP2APCa  D75_PP2APCa  D + PP2APCa
  • D137 + CDK5  D137_CDK5  D75-137 + CDK5
  • D75-137 + PP2A  D75-137_PP2A  D137 + PP2A
  • D75-137 + PP2ACa  D75-137_PP2ACa  D137 + PP2ACa
  • D75-137 + PP2AP  D75-137_PP2AP  D137 + PP2AP
  • D75-137 + PP2APCa  D75-137_PP2APCa  D137 + PP2APCa
  • D34 + CDK5  D34_CDK5  D34-75 + CDK5
  • D34-75 + PP2A  D34-75_PP2A  D34 + PP2A
  • D34-75 + PP2ACa  D34-75_PP2ACa  D34 + PP2ACa
  • D34-75 + PP2AP  D34-75_PP2AP  D34 + PP2AP
  • D34-75 + PP2APCa  D34-75_PP2APCa  D34 + PP2APCa
  • D34-137 + CDK5  D34-137_CDK5  D34-75_137 + CDK5
  • D34-75-137 + PP2A  D34-75-137_PP2A  D34-137 + PP2A
  • D34-75-137 + PP2ACa  D34-75-137_PP2ACa  D34-137 + PP2ACa
  • D34-75-137 + PP2AP  D34-75-137_PP2AP  D34-137 + PP2AP
  • D34-75-137 + PP2APCa  D34-75-137_PP2APCa  D34-137 + PP2APCa
  • D + CK1  D_CK1  D137 + CK1
  • D137 + PP2C  D137_PP2C  D + PP2C
  • D75 + CK1  D75_CK1  D75-137 + CK1
  • D75-137 + PP2C  D75-137_PP2C  D75 + PP2C
  • D34 + CK1  D34_CK1  D34-137 + CK1
  • D34-75 + PP2C  D34-75_PP2C  D75 + PP2C
  • D34-75 + CK1  D34-75_CK1  D34-75-137 + CK1
  • D34-75-137 + PP2C  D34-75-137_PP2C  D34-75 + PP2C
  • CK1 + CK1  CK1_CK1  CK1P + CK1
  • CK1P + PP2B  CK1P_PP2B  CK1 + PP2B
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SLIDE 35

[P]t+∆t = [P]t + k3[ES]t . ∆t [E]t+∆t = [E]t + k3[ES]t . ∆t [S]t+∆t = [S]t + (k2[ES]t – k1[E]t[S]t) . ∆t [ES]t+∆t = [S]t + (k1[ES]t – (k1+k3)[E]t[S]t) . ∆t Euler method: d[x]/dt ≈ ([x]t+∆t – [x]t ) / ∆t [x]t+∆t ≈ [x]t + d[x]/dt . ∆t

Computational Computational model model

t [x] ∆t t [x] ∆t t [x] ∆t 4th order Runge-Kutta: [x]t+∆t = [x]t + (F1+2F2+2F3+F4)/6 . ∆t with F1 = d[x]/dt = f([x], t) F2 = f([x]t + ∆t/2 . F1, t+ ∆t/2) F3 = f([x]t + ∆t/2 . F2, t+ ∆t/2) F4 = f([x]t + ∆t . F3, t+ ∆t)

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SLIDE 36

E-Cell System 3 E-Cell System 3

  • http://www.e-cell.org/
  • Takahashi et al. (2005). A multi-algorithm, multi-

timescale method for cell simulation. Bioinformatics, 20: 538-546

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SLIDE 37
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SLIDE 38

E-Cell System 3 E-Cell System 3

i translation i j k translation k potentiation k inhibition k catalyse j

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SLIDE 39

E-Cell System 3 E-Cell System 3

i translation i j k translation k potentiation k inhibition k catalyse j variable j variable i variable k

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SLIDE 40

E-Cell System 3 E-Cell System 3

i translation i j k translation k potentiation k inhibition k catalyse j variable j variable i variable k inhibition k (MassActionFluxProcess) potentiation k (MassActionFluxProcess) translation k (GillespieProcess) translation i (GillespieProcess) catalyse j (ExpressionFluxProcess)

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SLIDE 41

E-Cell System 3 E-Cell System 3

i translation i j k translation k potentiation k inhibition k catalyse j variable j variable i variable k inhibition k (MassActionFluxProcess) potentiation k (MassActionFluxProcess) translation k (GillespieProcess) translation i (GillespieProcess) catalyse j (ExpressionFluxProcess)

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SLIDE 42

E-Cell System 3 E-Cell System 3

i translation i j k translation k potentiation k inhibition k catalyse j variable j variable i variable k inhibition k (MassActionFluxProcess) potentiation k (MassActionFluxProcess) translation k (GillespieProcess) translation i (GillespieProcess) catalyse j (ExpressionFluxProcess)

slide-43
SLIDE 43

E-Cell System 3 E-Cell System 3

i translation i j k translation k potentiation k inhibition k catalyse j variable j variable i variable k inhibition k (MassActionFluxProcess) potentiation k (MassActionFluxProcess) translation k (GillespieProcess) translation i (GillespieProcess) catalyse j (ExpressionFluxProcess) Stepper A (ODE45Stepper) δt1 Stepper B (NRStepper) δt2

slide-44
SLIDE 44

E-Cell System 3 E-Cell System 3

i translation i j k translation k potentiation k inhibition k catalyse j variable j variable i variable k inhibition k (MassActionFluxProcess) potentiation k (MassActionFluxProcess) translation k (GillespieProcess) translation i (GillespieProcess) catalyse j (ExpressionFluxProcess) Stepper A (ODE45Stepper) δt1 Stepper B (NRStepper) δt2

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SLIDE 45

Effect of a cAMP pulse Effect of a cAMP pulse

350 400 450 500 550 600 650 700 750 500 1000 1500 2000 2500 3000 3500 4000

# Molecule Time (/s) 20 40 60 80 100 DARPP-32 Phosphorylation (%) Thr34 Thr75 non Ser137

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SLIDE 46

Effect of calcium spikes Effect of calcium spikes

A 350 400 450 500 550 600 400 800 1200 1600 2000 2400 2800 3200 Molecule number 20 40 60 80 100 DARPP-32 Phosphorylation (%) Thr75 non Ser137

model A model A

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SLIDE 47

What is the questionmark? What is the questionmark?

Nishi et al. (2002). J Neurochem, 82: 832-841

slide-48
SLIDE 48

What is the questionmark? What is the questionmark?

Nishi et al. (2002). J Neurochem, 82: 832-841

slide-49
SLIDE 49

Biological model Biological model of DARPP-32 regulation

  • f DARPP-32 regulation

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK 5 PP-2C CK2 PKA cAMP Ca2+ D1 NMDA AMPA Dopamine Glutamate µ Cocaine Amphetamine Nicotine Opiate 5HT4/6 Serotonin fluoxetine

striato-nigral neuron

PP-1 Thr34 Ser137 Thr75 Ser102 DARPP-32 PP-2B CK1 PP-2A CDK5 PP-2C CK2 PKA cAMP Ca

2+

D2 NMDA AMPA Dopamine Glutamate δ Opiate Cocaine Amphetamine Nicotine A2A Caffein 5HT4/6 Serotonin fluoxetine haloperidol Antipsychotics

striato-pallidal neuron

? ?

PDE PDE

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SLIDE 50

All the cybernetics in one system All the cybernetics in one system

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

Thr75

PKA PP-2A CDK5

?

+

  • incoherent feedforward

cAMP PDE

slide-51
SLIDE 51

All the cybernetics in one system All the cybernetics in one system

PP-2B Ca2+

PP-1

CK1

Thr34 Ser137

PP-2C

DARPP-32

GluR CaMKII

Thr75

PKA PP-2A CDK5

?

coherent feedforward

+ +

cAMP PDE

slide-52
SLIDE 52

Chemical model Chemical model of DARPP-32 regulation

  • f DARPP-32 regulation

PP2APCa D137

D

D34:137 D34:75:137 D34 D75 D34:75 D75-137 PP2B PKA PP-2C CK1 PP2A CDK5 Ca2+ cAMP PDE AMP PKA2R2 PKA2R PDEP PP2BiCa2 PP2Bi PP2ACa PP2AP CK1P

Ø Ø

slide-53
SLIDE 53

Chemical model Chemical model of DARPP-32 regulation

  • f DARPP-32 regulation
  • D + PKA  D_PKA  D34 + PKA
  • D34 + PP2B  D34_PP2B  D + PP2B
  • D75 + PKA  D75_PKA  D34-75 + PKA
  • D34-75 + PP2B  D34-75_PP2B  D75 + PP2B
  • D137 + PKA  D137_PKA  D34-137 + PKA
  • D34-137 + PP2B  D34-135_PP2B  D137 + PP2B
  • D75-137 + PKA  D75-137_PKA  D34-75-137 + PKA
  • D34-75-137 + PP2B  D34-75-135_PP2B  D75-137 + PP2
  • PP2A + PKA  PP2A_PKA  PP2AP + PKA
  • PP2ACa + PKA  PP2ACa_PKA  PP2APCa + PKA
  • Ø  Ca2+
  • Ca2+  Ø
  • PP2Bi + 2Ca  PP2Bi_Ca2
  • PP2Bi_Ca + 2Ca  PP2B
  • PP2A + Ca  PP2ACa
  • PP2AP + Ca  PP2APCa
  • R2_PKA2 + cAMP  cAMP_R2_PKA2
  • cAMP_R2_PKA2 + cAMP  cAMP2_R2_PKA2
  • cAMP2_R2_PKA2 + cAMP  cAMP3_R2_PKA2
  • cAMP3_R2_PKA2 + cAMP  cAMP4_R2_PKA2
  • cAMP4_R2_PKA2  cAMP4_R2_PKA + PKA
  • cAMP4_R2_PKA  cAMP4_R2 + PKA
  • PKA + PDE  PKA_PDE  PKA + PDEP
  • cAMP + PDE  cAMP_PDE  AMP + PDE
  • cAMP + PDEP  cAMP_PDEP  AMP + PDEP

79 species, 151 reactions

  • D + CDK5  D_CDK5  D75 + CDK5
  • D75 + PP2A  D75_PP2A  D + PP2A
  • D75 + PP2ACa  D75_PP2ACa  D + PP2ACa
  • D75 + PP2AP  D75_PP2AP  D + PP2AP
  • D75 + PP2APCa  D75_PP2APCa  D + PP2APCa
  • D137 + CDK5  D137_CDK5  D75-137 + CDK5
  • D75-137 + PP2A  D75-137_PP2A  D137 + PP2A
  • D75-137 + PP2ACa  D75-137_PP2ACa  D137 + PP2ACa
  • D75-137 + PP2AP  D75-137_PP2AP  D137 + PP2AP
  • D75-137 + PP2APCa  D75-137_PP2APCa  D137 + PP2APCa
  • D34 + CDK5  D34_CDK5  D34-75 + CDK5
  • D34-75 + PP2A  D34-75_PP2A  D34 + PP2A
  • D34-75 + PP2ACa  D34-75_PP2ACa  D34 + PP2ACa
  • D34-75 + PP2AP  D34-75_PP2AP  D34 + PP2AP
  • D34-75 + PP2APCa  D34-75_PP2APCa  D34 + PP2APCa
  • D34-137 + CDK5  D34-137_CDK5  D34-75_137 + CDK5
  • D34-75-137 + PP2A  D34-75-137_PP2A  D34-137 + PP2A
  • D34-75-137 + PP2ACa  D34-75-137_PP2ACa  D34-137 + PP2ACa
  • D34-75-137 + PP2AP  D34-75-137_PP2AP  D34-137 + PP2AP
  • D34-75-137 + PP2APCa  D34-75-137_PP2APCa  D34-137 + PP2APCa
  • D + CK1  D_CK1  D137 + CK1
  • D137 + PP2C  D137_PP2C  D + PP2C
  • D75 + CK1  D75_CK1  D75-137 + CK1
  • D75-137 + PP2C  D75-137_PP2C  D75 + PP2C
  • D34 + CK1  D34_CK1  D34-137 + CK1
  • D34-75 + PP2C  D34-75_PP2C  D75 + PP2C
  • D34-75 + CK1  D34-75_CK1  D34-75-137 + CK1
  • D34-75-137 + PP2C  D34-75-137_PP2C  D34-75 + PP2C
  • CK1 + CK1  CK1_CK1  CK1P + CK1
  • CK1P + PP2B  CK1P_PP2B  CK1 + PP2B
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SLIDE 54

Effect of calcium spikes Effect of calcium spikes

A 350 400 450 500 550 600 400 800 1200 1600 2000 2400 2800 3200 Molecule number 20 40 60 80 100 DARPP-32 Phosphorylation (%) Thr75 non Ser137 B 350 400 450 500 550 600 400 800 1200 1600 2000 2400 2800 3200 20 40 60 80 100 DARPP-32 Phosphorylation (%) Molecule number Time (s) B

model A model B

slide-55
SLIDE 55

Dynamic simulations of DARPP-32 function Dynamic simulations of DARPP-32 function

350 400 450 500 550 600 650 700 750 600 1200 1800 2400

20 40 60 80

Time (s)

Phosphorylation (%)

cAMP calcium

Thr34 Thr75 Ser137

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SLIDE 56

Varying parameters of the simulation Varying parameters of the simulation

slide-57
SLIDE 57

Some parameters are sensitive Some parameters are sensitive

model A model B

slide-58
SLIDE 58

Some parameters are sensitive Some parameters are sensitive

Takahahi et al (2005) PNAS, 102: 1737–1742

slide-59
SLIDE 59

Some parameters are robust Some parameters are robust

model A model B

slide-60
SLIDE 60

Sensitivity analysis Sensitivity analysis

model A model B

0.01 0.1 1 10 100 300 400 500 600 700 800 900 0.01 0.1 1 10 100 50 55 60 65 70 75 80 85 90 95 100

Thr34min (/# mles) recovery (/s) kcat CK1 autophosphorylation (/s-1) kcat CK1 autophosphorylation (/s-1)

C D

slide-61
SLIDE 61

Site-directed mutagenesis Site-directed mutagenesis

300 350 400 450 500 550 600 650 700 750 800 250 500 750 1000 1250 1500 1750 2000 2250 2500

cAMP-50-Ca "Nishi"

Thr34 Thr34Delta137 Thr75 Thr75Delta137

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SLIDE 62

Conclusion Conclusion

  • ODE systems are nice because easy to understand
  • ODE systems can reproduce experimental observations
  • ODE systems can produce some predictions

but ...

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SLIDE 63

Conclusion Conclusion

  • ODE systems are nice because easy to understand
  • ODE systems can reproduce experimental observations
  • ODE systems can produce some predictions

but ...

  • ODE systems are cumbersome; lead to large

dimentionality.

slide-64
SLIDE 64

Acknowledgements Acknowledgements

  • E-cell developers

Kouichi Takahashi Kazunari Kaizu Gabor Bereczki

  • Eric Fernandez

Renaud Schiappa