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BIOMD0000000615 - Kuznetsov2016(II) - α-syn aggregation kinetics in Parkinson's Disease

 

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Reference Publication
Publication ID: 27211070
Kuznetsov IA, Kuznetsov AV.
What can trigger the onset of Parkinson's disease - A modeling study based on a compartmental model of α-synuclein transport and aggregation in neurons.
Math Biosci 2016 Aug; 278: 22-29
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218-2694, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.  [more]
Model
Original Model: BIOMD0000000615.origin
Submitter: Thawfeek Varusai
Submission ID: MODEL1608150000
Submission Date: 15 Aug 2016 13:52:21 UTC
Last Modification Date: 01 Dec 2016 13:11:00 UTC
Creation Date: 15 Aug 2016 15:02:44 UTC
Encoders:  Thawfeek Varusai
set #1
bqbiol:hasProperty Human Disease Ontology Parkinson's disease
set #2
bqbiol:isVersionOf Gene Ontology inclusion body assembly
set #3
bqbiol:hasTaxon Taxonomy Homo sapiens
Notes
Kuznetsov2016(II) - α-syn aggregation kinetics in Parkinson's
This theoretical model uses 2-step Finke-Watzky (FW) kinetics
todescribe the production, misfolding, aggregation, transport and
degradation of α-syn that may lead to Parkinson's Disease
(PD). Deregulated α-syn degradation is predicted to be
crucialfor PD pathogenesis.
 
  

This model is described in the article:

Kuznetsov IA, Kuznetsov AV.
Math Biosci 2016 Aug; 278: 22-29

Abstract:

The aim of this paper is to develop a minimal model describing events leading to the onset of Parkinson's disease (PD). The model accounts for α-synuclein (α-syn) production in the soma, transport toward the synapse, misfolding, and aggregation. The production and aggregation of polymeric α-syn is simulated using a minimalistic 2-step Finke-Watzky model. We utilized the developed model to analyze what changes in a healthy neuron are likely to lead to the onset of α-syn aggregation. We checked the effects of interruption of α-syn transport toward the synapse, entry of misfolded (infectious) α-syn into the somatic and synaptic compartments, increasing the rate of α-syn synthesis in the soma, and failure of α-syn degradation machinery. Our model suggests that failure of α-syn degradation machinery is probably the most likely cause for the onset of α-syn aggregation leading to PD.

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: 27211070 Submission Date: 15 Aug 2016 13:52:21 UTC Last Modification Date: 01 Dec 2016 13:11:00 UTC Creation Date: 15 Aug 2016 15:02:44 UTC
Mathematical expressions
Reactions
R1 R2 R3 R4
R5 R6 R7 R8
R9 R10 R11 R12
R13 R14 R15 R16
R17      
Physical entities
Compartments Species
Neuron As Bs Asyn
Bsyn    
Global parameters
nA k1 k2 qA
QBs QBsyn TAh1 TBh1
Vs Vsyn t1 t2
Reactions (17)
 
 R1  ↔ [As];  
 
 R2 [As] ↔ ;  
 
 R3 [As] ↔ ;   {Bs}
 
 R4 [As] ↔ ;  
 
 R5 [As] ↔ ;  
 
 R6  ↔ [Bs];  
 
 R7  ↔ [Bs];   {As}
 
 R8  ↔ [Bs];   {As}
 
 R9 [Bs] ↔ ;  
 
 R10  ↔ [Asyn];   {As}
 
 R11 [Asyn] ↔ ;  
 
 R12 [Asyn] ↔ ;   {Bsyn}
 
 R13 [Asyn] ↔ ;  
 
 R14  ↔ [Bsyn];  
 
 R15  ↔ [Bsyn];   {Asyn}
 
 R16  ↔ [Bsyn];   {Asyn}
 
 R17 [Bsyn] ↔ ;  
 
Functions (17)
 
 Function for R8 lambda(As, Bs, ModelValue_2, default_compartment, ModelValue_2*As*Bs/default_compartment)
 
 Function for R17 lambda(Bsyn, ModelValue_7, default_compartment, Bsyn*ln(2)/ModelValue_7/default_compartment)
 
 Function for R1 lambda(ModelValue_3, default_compartment, ModelValue_3/default_compartment)
 
 Function for R5 lambda(As, ModelValue_0, ModelValue_8, default_compartment, ModelValue_0*As/ModelValue_8/default_compartment)
 
 Function for R9 lambda(Bs, ModelValue_7, default_compartment, Bs*ln(2)/ModelValue_7/default_compartment)
 
 Function for R2 lambda(As, ModelValue_1, default_compartment, ModelValue_1*As/default_compartment)
 
 Function for R16 lambda(Asyn, Bsyn, ModelValue_2, default_compartment, ModelValue_2*Asyn*Bsyn/default_compartment)
 
 Function for R15 lambda(Asyn, ModelValue_1, default_compartment, ModelValue_1*Asyn/default_compartment)
 
 Function for R4 lambda(As, ModelValue_6, default_compartment, As*ln(2)/ModelValue_6/default_compartment)
 
 Function for R11 lambda(Asyn, ModelValue_1, default_compartment, ModelValue_1*Asyn/default_compartment)
 
 Function for R6 lambda(ModelValue_4, default_compartment, ModelValue_4/default_compartment)
 
 Function for R12 lambda(Asyn, Bsyn, ModelValue_2, default_compartment, ModelValue_2*Asyn*Bsyn/default_compartment)
 
 Function for R7 lambda(As, ModelValue_1, default_compartment, ModelValue_1*As/default_compartment)
 
 Function for R13 lambda(Asyn, ModelValue_6, default_compartment, Asyn*ln(2)/ModelValue_6/default_compartment)
 
 Function for R14 lambda(ModelValue_5, default_compartment, ModelValue_5/default_compartment)
 
 Function for R3 lambda(As, Bs, ModelValue_2, default_compartment, ModelValue_2*As*Bs/default_compartment)
 
 Function for R10 lambda(As, ModelValue_0, ModelValue_9, default_compartment, ModelValue_0*As/ModelValue_9/default_compartment)
 
 Neuron Spatial dimensions: 3.0  Compartment size: 1.0  (Units: litre)
 
 As
Compartment: Neuron
Initial concentration: 0.006  (Units: mole)
 
 Bs
Compartment: Neuron
Initial concentration: 0.0  (Units: mole)
 
 Asyn
Compartment: Neuron
Initial concentration: 0.0  (Units: mole)
 
 Bsyn
Compartment: Neuron
Initial concentration: 0.0  (Units: mole)
 
Global Parameters (12)
 
 nA
Value: 2.91E-20
Constant
 
 k1
Value: 3.0E-7
Constant
 
 k2
Value: 2.0E-9
Constant
 
 qA
Value: 4.17E-8
Constant
 
 QBs
Constant
 
 QBsyn
Constant
 
 TAh1
Value: 72000.0
Constant
 
 TBh1
Value: 720000.0
Constant
 
 Vs
Value: 4.19E-15
Constant
 
 Vsyn
Value: 4.19E-15
Constant
 
 t1
Constant
 
 t2
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000615

Curator's comment: (updated: 15 Aug 2016 16:08:19 GMT)

The model was encoded and simulated using Copasi 4.15. Wolfram Mathematica 8 and MS PowerPoint were used to process the figures. The difference between the two graphs is because of a change in a single parameter: first figure nA=2.91e-20 and second figure nA=0.

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