BioModels Database logo

BioModels Database

spacer

BIOMD0000000532 - Vazquez2014 - Chemical inhibition from amyloid protein aggregation kinetics

 

 |   |   |  Send feedback
Reference Publication
Publication ID: 24572069
Vázquez JA.
Modeling of chemical inhibition from amyloid protein aggregation kinetics.
BMC Pharmacol Toxicol 2014; 15: 9
Grupo de Reciclado e Valorización de Residuos (REVAL), Instituto de Investigacións Mariñas (IIM-CSIC), C/ Eduardo Cabello 6, CP36208 Vigo, Spain. jvazquez@iim.csic.es.  [more]
Model
Original Model: BIOMD0000000532.origin
Submitter: Audald Lloret i Villas
Submission ID: MODEL1407300000
Submission Date: 30 Jul 2014 13:26:41 UTC
Last Modification Date: 09 Sep 2014 13:50:37 UTC
Creation Date: 30 Jul 2014 14:46:49 UTC
Encoders:  Audald Lloret i Villas
set #1
bqbiol:isVersionOf Gene Ontology inclusion body assembly
Gene Ontology amyloid fibril formation
set #2
bqbiol:hasProperty Human Disease Ontology Alzheimer's disease
set #3
bqbiol:hasTaxon Taxonomy Homo sapiens
Notes
Vazquez2014 - Chemical inhibition from amyloid protein aggregation kinetics

This model is described in the article:

Vázquez JA.
BMC Pharmacol Toxicol 2014; 15(1): 9

Abstract:

BACKGROUNDS: The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. RESULTS: In this manuscript, experimental data of insulin, A?42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH?=?7.4/T?=?37°C) and taiwaniaflavone for A?42 were the compounds studied that shown the greatest inhibition capacity. CONCLUSIONS: An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.

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: 24572069 Submission Date: 30 Jul 2014 13:26:41 UTC Last Modification Date: 09 Sep 2014 13:50:37 UTC Creation Date: 30 Jul 2014 14:46:49 UTC
Mathematical expressions
Rules
Assignment Rule (variable: Xm) Assignment Rule (variable: Vm) Assignment Rule (variable: Lambda) Assignment Rule (variable: X)
Physical entities
Compartments Species
cell X Xm Vm
Lambda    
Global parameters
xm vm lambda kx
mx ax kv mv
av klambda mlambda alambda
C      
Reactions (0)
Rules (4)
 
 Assignment Rule (name: Xm) Xm = xm*(1-kx*(1-exp((-ln(2))*(C/mx)^ax)))
 
 Assignment Rule (name: Vm) Vm = vm*(1-kv*(1-exp((-ln(2))*(C/mv)^av)))
 
 Assignment Rule (name: Lambda) Lambda = lambda*(1+klambda*(1-exp((-ln(2))*(C/mlambda)^alambda)))
 
 Assignment Rule (name: X) X = Xm/(1+exp(2+4*Vm/Xm*(Lambda-time)))
 
 cell Spatial dimensions: 3.0  Compartment size: 1.0
 
  X
Compartment: cell
Initial concentration: 0.00427219370168501
 
  Xm
Compartment: cell
Initial concentration: 0.972654947412286
 
  Vm
Compartment: cell
Initial concentration: 0.239400820174643
 
  Lambda
Compartment: cell
Initial concentration: 3.47731075423886
 
Global Parameters (13)
 
 xm
Value: 1.0
Constant
 
 vm
Value: 0.25
Constant
 
 lambda
Value: 3.0
Constant
 
 kx
Value: 1.0
Constant
 
 mx
Value: 5.0
Constant
 
 ax
Value: 2.0
Constant
 
 kv
Value: 1.0
Constant
 
 mv
Value: 4.0
Constant
 
 av
Value: 2.0
Constant
 
 klambda
Value: 1.0
Constant
 
 mlambda
Value: 2.0
Constant
 
 alambda
Value: 2.0
Constant
 
 C
Value: 1.0
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000532

Curator's comment: (updated: 30 Jul 2014 17:34:56 GMT)

Figure 2A of the reference publication has been reproduced here, as two-dimensional plots whereas it is representation as a three-dimensional plot in the paper.

Left: Simulation of Amyloid aggregation growth (X) with 1 mM chemical concentration for 20 hours time period.
Right: Simulation of Amyloid aggregation growth (X) depending on varying concentrations of the inhibitor (C).

The simulation was done using Copasi v4.12 (Build 81) and the plots were generated using Gnuplot. The Copasi file of the model with simulation settings can be downloaded from below link

Additional file(s)
  • Vazquez2014 - Chemical inhibition from amyloid protein aggregation kinetics:
    Copasi file of the model
spacer
spacer