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BIOMD0000000618 - Krohn2011 - Cerebral amyloid-? proteostasis regulated by membrane transport protein ABCC1

 

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Reference Publication
Publication ID: 21881209
Krohn M, Lange C, Hofrichter J, Scheffler K, Stenzel J, Steffen J, Schumacher T, Brüning T, Plath AS, Alfen F, Schmidt A, Winter F, Rateitschak K, Wree A, Gsponer J, Walker LC, Pahnke J.
Cerebral amyloid-? proteostasis is regulated by the membrane transport protein ABCC1 in mice.
J. Clin. Invest. 2011 Oct; 121(10): 3924-3931
Department of Neurology, Neurodegeneration Research Laboratory, University of Rostock, Rostock, Germany.  [more]
Model
Original Model: http://files.kapora.de/kro...
Submitter: Felix Winter
Submission ID: MODEL1607270000
Submission Date: 27 Jul 2016 09:31:33 UTC
Last Modification Date: 31 May 2017 16:22:38 UTC
Creation Date: 27 Jul 2016 09:28:44 UTC
Encoders:  Vijayalakshmi Chelliah
   Felix Winter
set #1
bqbiol:hasProperty Human Disease Ontology Alzheimer's disease
set #2
bqbiol:isVersionOf Systems Biology Ontology non-spatial continuous framework
set #3
bqbiol:isVersionOf Gene Ontology amyloid fibril formation
set #4
bqbiol:isVersionOf Gene Ontology transport
Notes
Krohn2011 - Cerebral amyloid-β proteostasis regulated by membrane transport protein ABCC1

This model is described in the article:

Krohn M, Lange C, Hofrichter J, Scheffler K, Stenzel J, Steffen J, Schumacher T, Brüning T, Plath AS, Alfen F, Schmidt A, Winter F, Rateitschak K, Wree A, Gsponer J, Walker LC, Pahnke J.
J. Clin. Invest. 2011 Oct; 121(10): 3924-3931

Abstract:

In Alzheimer disease (AD), the intracerebral accumulation of amyloid-? (A?) peptides is a critical yet poorly understood process. A? clearance via the blood-brain barrier is reduced by approximately 30% in AD patients, but the underlying mechanisms remain elusive. ABC transporters have been implicated in the regulation of A? levels in the brain. Using a mouse model of AD in which the animals were further genetically modified to lack specific ABC transporters, here we have shown that the transporter ABCC1 has an important role in cerebral A? clearance and accumulation. Deficiency of ABCC1 substantially increased cerebral A? levels without altering the expression of most enzymes that would favor the production of A? from the A? precursor protein. In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced A? load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1. Thus, by altering the temporal aggregation profile of A?, pharmacological activation of ABC transporters could impede the neurodegenerative cascade that culminates in the dementia of AD.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Model
Publication ID: 21881209 Submission Date: 27 Jul 2016 09:31:33 UTC Last Modification Date: 31 May 2017 16:22:38 UTC Creation Date: 27 Jul 2016 09:28:44 UTC
Mathematical expressions
Rules
Assignment Rule (variable: R_T) Assignment Rule (variable: insoluble) Assignment Rule (variable: soluble) Assignment Rule (variable: blocking)
Assignment Rule (variable: a_T) Assignment Rule (variable: I_net) Assignment Rule (variable: insoluble_obs) Assignment Rule (variable: soluble_obs)
Rate Rule (variable: M) Rate Rule (variable: N) Rate Rule (variable: A7) Rate Rule (variable: A8)
Rate Rule (variable: A9) Rate Rule (variable: A10) Rate Rule (variable: A11) Rate Rule (variable: A12)
Rate Rule (variable: A13) Rate Rule (variable: A14) Rate Rule (variable: A15) Rate Rule (variable: A16)
Rate Rule (variable: A17) Rate Rule (variable: A18) Rate Rule (variable: A19) Rate Rule (variable: A20)
Rate Rule (variable: A21) Rate Rule (variable: A22) Rate Rule (variable: A23) Rate Rule (variable: A24)
Rate Rule (variable: A25) Rate Rule (variable: A26) Rate Rule (variable: A27) Rate Rule (variable: A28)
Rate Rule (variable: A29) Rate Rule (variable: A30) Rate Rule (variable: A31) Rate Rule (variable: A32)
Rate Rule (variable: A33) Rate Rule (variable: A34) Rate Rule (variable: A35) Rate Rule (variable: A36)
Rate Rule (variable: A37) Rate Rule (variable: A38) Rate Rule (variable: A39) Rate Rule (variable: A40)
Rate Rule (variable: A41) Rate Rule (variable: A42) Rate Rule (variable: A43) Rate Rule (variable: A44)
Rate Rule (variable: A45) Rate Rule (variable: A46) Rate Rule (variable: A47) Rate Rule (variable: A48)
Rate Rule (variable: A49) Rate Rule (variable: A50) Rate Rule (variable: A51) Rate Rule (variable: A52)
Rate Rule (variable: A53) Rate Rule (variable: A54)    
Physical entities
Compartments Species
Brain M N A7
A8 A9 A10
A11 A12 A13
A14 A15 A16
A17 A18 A19
A20 A21 A22
A23 A24 A25
A26 A27 A28
A29 A30 A31
A32 A33 A34
A35 A36 A37
A38 A39 A40
A41 A42 A43
A44 A45 A46
A47 A48 A49
A50 A51 A52
A53 A54 soluble_obs
insoluble_obs    
Global parameters
P c_T s_T e_T
k_n k_sol k_insol n_n
blocking soluble insoluble R_T
a_T I_net    
Reactions (0)
Rules (58)
 
 Assignment Rule (name: R_T) R_T = c_T*M
 
 Assignment Rule (name: insoluble) insoluble = A15*15+A16*16+17*A17+18*A18+19*A19+20*A20+21*A21+22*A22+23*A23+24*A24+25*A25+26*A26+27*A27+28*A28+29*A29+30*A30+31*A31+32*A32+33*A33+34*A34+35*A35+36*A36+37*A37+38*A38+39*A39+40*A40+41*A41+42*A42+43*A43+44*A44+45*A45+46*A46+47*A47+48*A48+49*A49+50*A50+51*A51+52*A52+53*A53+54*A54
 
 Assignment Rule (name: soluble) soluble = M+N*n_n+7*A7+8*A8+9*A9+10*A10+11*A11+12*A12+13*A13+14*A14
 
 Assignment Rule (name: blocking) blocking = A15+A16+A17+A18+A19+A20+A21+A22+A23+A24+A25+A26+A27+A28+A29+A30+A31+A32+A33+A34+A35+A36+A37+A38+A39+A40+A41+A42+A43+A44+A45+A46+A47+A48+A49+A50+A51+A52+A53+A54
 
 Assignment Rule (name: a_T) a_T = R_T*s_T^e_T/(s_T^e_T+blocking^e_T)
 
 Assignment Rule (name: I_net) I_net = P-a_T
 
 Assignment Rule (name: insoluble_obs) insoluble_obs = insoluble
 
 Assignment Rule (name: soluble_obs) soluble_obs = soluble
 
 Rate Rule (name: M) d [ M] / d t= ((((((((((((((((((((((((((((((((((((((((((((((((I_net-k_n*n_n*M^n_n)-k_sol*N*M)-k_sol*A7*M)-k_sol*A8*M)-k_sol*A9*M)-k_sol*A10*M)-k_sol*A11*M)-k_sol*A12*M)-k_sol*A13*M)-k_insol*A14*M)-k_insol*A15*M)-k_insol*A16*M)-k_insol*A17*M)-k_insol*A18*M)-k_insol*A19*M)-k_insol*A20*M)-k_insol*A21*M)-k_insol*A22*M)-k_insol*A23*M)-k_insol*A24*M)-k_insol*A25*M)-k_insol*A26*M)-k_insol*A27*M)-k_insol*A28*M)-k_insol*A29*M)-k_insol*A30*M)-k_insol*A31*M)-k_insol*A32*M)-k_insol*A33*M)-k_insol*A34*M)-k_insol*A35*M)-k_insol*A36*M)-k_insol*A37*M)-k_insol*A38*M)-k_insol*A39*M)-k_insol*A40*M)-k_insol*A41*M)-k_insol*A42*M)-k_insol*A43*M)-k_insol*A44*M)-k_insol*A45*M)-k_insol*A46*M)-k_insol*A47*M)-k_insol*A48*M)-k_insol*A49*M)-k_insol*A50*M)-k_insol*A51*M)-k_insol*A52*M)-k_insol*A53*M
 
 Rate Rule (name: N) d [ N] / d t= k_n*M^n_n-k_sol*N*M
 
 Rate Rule (name: A7) d [ A7] / d t= k_sol*N*M-k_sol*A7*M
 
 Rate Rule (name: A8) d [ A8] / d t= k_sol*A7*M-k_sol*A8*M
 
 Rate Rule (name: A9) d [ A9] / d t= k_sol*A8*M-k_sol*A9*M
 
 Rate Rule (name: A10) d [ A10] / d t= k_sol*A9*M-k_sol*A10*M
 
 Rate Rule (name: A11) d [ A11] / d t= k_sol*A10*M-k_sol*A11*M
 
 Rate Rule (name: A12) d [ A12] / d t= k_sol*A11*M-k_sol*A12*M
 
 Rate Rule (name: A13) d [ A13] / d t= k_sol*A12*M-k_sol*A13*M
 
 Rate Rule (name: A14) d [ A14] / d t= k_sol*A13*M-k_insol*A14*M
 
 Rate Rule (name: A15) d [ A15] / d t= k_insol*A14*M-k_insol*A15*M
 
 Rate Rule (name: A16) d [ A16] / d t= k_insol*A15*M-k_insol*A16*M
 
 Rate Rule (name: A17) d [ A17] / d t= k_insol*A16*M-k_insol*A17*M
 
 Rate Rule (name: A18) d [ A18] / d t= k_insol*A17*M-k_insol*A18*M
 
 Rate Rule (name: A19) d [ A19] / d t= k_insol*A18*M-k_insol*A19*M
 
 Rate Rule (name: A20) d [ A20] / d t= k_insol*A19*M-k_insol*A20*M
 
 Rate Rule (name: A21) d [ A21] / d t= k_insol*A20*M-k_insol*A21*M
 
 Rate Rule (name: A22) d [ A22] / d t= k_insol*A21*M-k_insol*A22*M
 
 Rate Rule (name: A23) d [ A23] / d t= k_insol*A22*M-k_insol*A23*M
 
 Rate Rule (name: A24) d [ A24] / d t= k_insol*A23*M-k_insol*A24*M
 
 Rate Rule (name: A25) d [ A25] / d t= k_insol*A24*M-k_insol*A25*M
 
 Rate Rule (name: A26) d [ A26] / d t= k_insol*A25*M-k_insol*A26*M
 
 Rate Rule (name: A27) d [ A27] / d t= k_insol*A26*M-k_insol*A27*M
 
 Rate Rule (name: A28) d [ A28] / d t= k_insol*A27*M-k_insol*A28*M
 
 Rate Rule (name: A29) d [ A29] / d t= k_insol*A28*M-k_insol*A29*M
 
 Rate Rule (name: A30) d [ A30] / d t= k_insol*A29*M-k_insol*A30*M
 
 Rate Rule (name: A31) d [ A31] / d t= k_insol*A30*M-k_insol*A31*M
 
 Rate Rule (name: A32) d [ A32] / d t= k_insol*A31*M-k_insol*A32*M
 
 Rate Rule (name: A33) d [ A33] / d t= k_insol*A32*M-k_insol*A33*M
 
 Rate Rule (name: A34) d [ A34] / d t= k_insol*A33*M-k_insol*A34*M
 
 Rate Rule (name: A35) d [ A35] / d t= k_insol*A34*M-k_insol*A35*M
 
 Rate Rule (name: A36) d [ A36] / d t= k_insol*A35*M-k_insol*A36*M
 
 Rate Rule (name: A37) d [ A37] / d t= k_insol*A36*M-k_insol*A37*M
 
 Rate Rule (name: A38) d [ A38] / d t= k_insol*A37*M-k_insol*A38*M
 
 Rate Rule (name: A39) d [ A39] / d t= k_insol*A38*M-k_insol*A39*M
 
 Rate Rule (name: A40) d [ A40] / d t= k_insol*A39*M-k_insol*A40*M
 
 Rate Rule (name: A41) d [ A41] / d t= k_insol*A40*M-k_insol*A41*M
 
 Rate Rule (name: A42) d [ A42] / d t= k_insol*A41*M-k_insol*A42*M
 
 Rate Rule (name: A43) d [ A43] / d t= k_insol*A42*M-k_insol*A43*M
 
 Rate Rule (name: A44) d [ A44] / d t= k_insol*A43*M-k_insol*A44*M
 
 Rate Rule (name: A45) d [ A45] / d t= k_insol*A44*M-k_insol*A45*M
 
 Rate Rule (name: A46) d [ A46] / d t= k_insol*A45*M-k_insol*A46*M
 
 Rate Rule (name: A47) d [ A47] / d t= k_insol*A46*M-k_insol*A47*M
 
 Rate Rule (name: A48) d [ A48] / d t= k_insol*A47*M-k_insol*A48*M
 
 Rate Rule (name: A49) d [ A49] / d t= k_insol*A48*M-k_insol*A49*M
 
 Rate Rule (name: A50) d [ A50] / d t= k_insol*A49*M-k_insol*A50*M
 
 Rate Rule (name: A51) d [ A51] / d t= k_insol*A50*M-k_insol*A51*M
 
 Rate Rule (name: A52) d [ A52] / d t= k_insol*A51*M-k_insol*A52*M
 
 Rate Rule (name: A53) d [ A53] / d t= k_insol*A52*M-k_insol*A53*M
 
 Rate Rule (name: A54) d [ A54] / d t= k_insol*A53*M
 
 Brain Spatial dimensions: 3.0  Compartment size: 1.0
 
 M
Compartment: Brain
Initial concentration: 1.04389999999997
 
 N
Compartment: Brain
Initial concentration: 0.0
 
 A7
Compartment: Brain
Initial concentration: 0.0
 
 A8
Compartment: Brain
Initial concentration: 0.0
 
 A9
Compartment: Brain
Initial concentration: 0.0
 
 A10
Compartment: Brain
Initial concentration: 0.0
 
 A11
Compartment: Brain
Initial concentration: 0.0
 
 A12
Compartment: Brain
Initial concentration: 0.0
 
 A13
Compartment: Brain
Initial concentration: 0.0
 
 A14
Compartment: Brain
Initial concentration: 0.0
 
 A15
Compartment: Brain
Initial concentration: 0.0
 
 A16
Compartment: Brain
Initial concentration: 0.0
 
 A17
Compartment: Brain
Initial concentration: 0.0
 
 A18
Compartment: Brain
Initial concentration: 0.0
 
 A19
Compartment: Brain
Initial concentration: 0.0
 
 A20
Compartment: Brain
Initial concentration: 0.0
 
 A21
Compartment: Brain
Initial concentration: 0.0
 
 A22
Compartment: Brain
Initial concentration: 0.0
 
 A23
Compartment: Brain
Initial concentration: 0.0
 
 A24
Compartment: Brain
Initial concentration: 0.0
 
 A25
Compartment: Brain
Initial concentration: 0.0
 
 A26
Compartment: Brain
Initial concentration: 0.0
 
 A27
Compartment: Brain
Initial concentration: 0.0
 
 A28
Compartment: Brain
Initial concentration: 0.0
 
 A29
Compartment: Brain
Initial concentration: 0.0
 
 A30
Compartment: Brain
Initial concentration: 0.0
 
 A31
Compartment: Brain
Initial concentration: 0.0
 
 A32
Compartment: Brain
Initial concentration: 0.0
 
 A33
Compartment: Brain
Initial concentration: 0.0
 
 A34
Compartment: Brain
Initial concentration: 0.0
 
 A35
Compartment: Brain
Initial concentration: 0.0
 
 A36
Compartment: Brain
Initial concentration: 0.0
 
 A37
Compartment: Brain
Initial concentration: 0.0
 
 A38
Compartment: Brain
Initial concentration: 0.0
 
 A39
Compartment: Brain
Initial concentration: 0.0
 
 A40
Compartment: Brain
Initial concentration: 0.0
 
 A41
Compartment: Brain
Initial concentration: 0.0
 
 A42
Compartment: Brain
Initial concentration: 0.0
 
 A43
Compartment: Brain
Initial concentration: 0.0
 
 A44
Compartment: Brain
Initial concentration: 0.0
 
 A45
Compartment: Brain
Initial concentration: 0.0
 
 A46
Compartment: Brain
Initial concentration: 0.0
 
 A47
Compartment: Brain
Initial concentration: 0.0
 
 A48
Compartment: Brain
Initial concentration: 0.0
 
 A49
Compartment: Brain
Initial concentration: 0.0
 
 A50
Compartment: Brain
Initial concentration: 0.0
 
 A51
Compartment: Brain
Initial concentration: 0.0
 
 A52
Compartment: Brain
Initial concentration: 0.0
 
 A53
Compartment: Brain
Initial concentration: 0.0
 
 A54
Compartment: Brain
Initial concentration: 0.0
 
  soluble_obs
Compartment: Brain
Initial concentration: 1.04389999999997
 
  insoluble_obs
Compartment: Brain
Initial concentration: 0.0
 
Global Parameters (14)
 
 P
Value: 91.239
Constant
 
 c_T
Value: 82.418999999
Constant
 
 s_T
Value: 17.744
Constant
 
 e_T
Value: 7.8115
Constant
 
 k_n
Value: 0.34508
Constant
 
 k_sol
Value: 0.34237
Constant
 
 k_insol
Value: 0.3586
Constant
 
 n_n
Value: 6.0
Constant
 
  blocking  
 
  soluble
Value: 1.04389999999997
 
  insoluble  
 
  R_T
Value: 86.0371940989535
 
  a_T
Value: 86.0371940989535
 
  I_net
Value: 5.20180590104651
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000618

Curator's comment: (updated: 06 Oct 2016 15:18:42 GMT)

Figure 2 of the reference publication has been reproduced here. To obtain figure 2C and 2D, change c_T to 9.5 (11% increase of transport capacity). The paper use weeks as time scale, the model uses days instead of weeks (as the data was taken from mice at days 50, 100, etc - co-author of the paper and submitter of the model reported). The data was generated by simulating the model using SBMLSimulator 1.2.1, and gnuplot was used to generate the plots.

Note: The SED-ML file that generates the curation figure can be downloaded from below link and can be run by loading it into SED-ML Web Tool - http://sysbioapps.dyndns.org/SED-ML_Web_Tools/.

Additional file(s)
  • Simulation experiment description::
    This SED-ML file allow you to reproduce the curation results (Figures 2A-D), for example,
    by loading it into SED-ML Web Tools (http://sysbioapps.dyndns.org/SED-ML_Web_Tools/).
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