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BIOMD0000000617 - Walsh2014 - Inhibition kinetics of DAPT on APP Cleavage

 

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Reference Publication
Publication ID: 25374788
Walsh R.
Are improper kinetic models hampering drug development?
PeerJ 2014; 2: e649
Department of Chemistry, Carleton University , Ottawa, ON , Canada.  [more]
Model
Original Model: BIOMD0000000617.origin
Submitter: Thawfeek Varusai
Submission ID: MODEL1609120000
Submission Date: 12 Sep 2016 16:03:32 UTC
Last Modification Date: 10 Oct 2016 16:39:28 UTC
Creation Date: 02 Sep 2016 12:09:10 UTC
Encoders:  Thawfeek Varusai
set #1
bqbiol:hasProperty Human Disease Ontology Alzheimer's disease
set #2
bqbiol:hasTaxon Taxonomy Homo sapiens
set #3
bqbiol:isVersionOf Gene Ontology amyloid precursor protein catabolic process
Notes
Walsh2014 - Inhibition kinetics of DAPT on APP Cleavage

This model is described in the article:

Walsh R.
PeerJ 2014; 2: e649

Abstract:

Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits ?-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions.

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: 25374788 Submission Date: 12 Sep 2016 16:03:32 UTC Last Modification Date: 10 Oct 2016 16:39:28 UTC Creation Date: 02 Sep 2016 12:09:10 UTC
Mathematical expressions
Reactions
R1 R2 R3 R4
Rules
Assignment Rule (variable: K2s) Assignment Rule (variable: V1s) Assignment Rule (variable: K1s) Assignment Rule (variable: V2s)
Physical entities
Compartments Species
default_compartment v    
Compartment_      
Global parameters
V1s S K1s V2s
H1 K2s H2 K3s
V2 V2i Ii Hxx
Kxx1 K2 K2i V1
V1is Hx1 Kx1 Hx2
Kx2 V1ii K1 K1is
K1ii V3    
Reactions (4)
 
 R1  → [v];  
 
 R2  → [v];  
 
 R3 [v] → ;  
 
 R4 [v] → ;  
 
Rules (4)
 
 Assignment Rule (name: K2s) K2s = K2-(K2-K2i)*Ii^Hxx/(Ii^Hxx+Kxx1^Hxx)
 
 Assignment Rule (name: V1s) V1s = ((V1-(V1-V1is)*Ii^Hx1/(Ii^Hx1+Kx1^Hx1))+(V1-V1is)*Ii^Hx2/(Ii^Hx2+Kx2^Hx2))-(V1-V1ii)*Ii^Hx2/(Ii^Hx2+Kx2^Hx2)
 
 Assignment Rule (name: K1s) K1s = ((K1-(K1-K1is)*Ii^Hx1/(Ii^Hx1+Kx1^Hx1))+(K1-K1is)*Ii^Hx2/(Ii^Hx2+Kx2^Hx2))-(K1-K1ii)*Ii^Hx2/(Ii^Hx2+Kx2^Hx2)
 
 Assignment Rule (name: V2s) V2s = V2-(V2-V2i)*Ii^Hxx/(Ii^Hxx+Kxx1^Hxx)
 
Functions (4)
 
 Function for R3 lambda(Compartment_, H1, K2s, S, V1s, Compartment_*V1s*S^H1/(S^H1+K2s^H1))
 
 Function for R4 lambda(Compartment_, H2, K3s, S, V2s, Compartment_*V2s*S^H2/(S^H2+K3s^H2))
 
 Function for R1 lambda(Compartment_, K1s, S, V1s, Compartment_*V1s*S/(S+K1s))
 
 Function for R2 lambda(Compartment_, H1, K2s, S, V2s, Compartment_*V2s*S^H1/(S^H1+K2s^H1))
 
 default_compartment Spatial dimensions: 3.0  Compartment size: 1.0  (Units: volume)
 
 v
Compartment: default_compartment
Initial concentration: 1.0  (Units: substance)
 
 Compartment_ Spatial dimensions: 3.0  Compartment size: 1.0  (Units: volume)
Global Parameters (26)
 
  V1s
Value: 64.680648010584
 
 S
Value: 61.0
Constant
 
  K1s
Value: 37.3401755830905
 
  V2s
Value: 32.4269355627923
 
 H1
Value: 1.71
Constant
 
  K2s
Value: 126.236082446952
 
 H2
Value: 2.69
Constant
 
 K3s
Value: 605.01
Constant
 
 V2
Value: 443.68
Constant
 
 V2i
Constant
 
 Ii
Value: 1000.0
Constant
 
 Hxx
Value: 0.96
Constant
 
 Kxx1
Value: 70.93
Constant
 
 K2
Value: 225.49
Constant
 
 K2i
Value: 118.41
Constant
 
 V1
Value: 20.06
Constant
 
 V1is
Value: 451.78
Constant
 
 Hx1
Value: 1.02
Constant
 
 Kx1
Value: 30.18
Constant
 
 Hx2
Value: 2.69
Constant
 
 Kx2
Value: 553.64
Constant
 
 V1ii
Constant
 
 K1
Value: 177.76
Constant
 
 K1is
Value: 29.52
Constant
 
 K1ii
Value: 34.05
Constant
 
 V3
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000617

Curator's comment: (updated: 12 Sep 2016 17:48:46 GMT)

The model was encoded and simulated using Copasi 4.15. Wolfram Mathematica 8 and MS PowerPoint were used to process the figures. The left graph of figure 2C from the paper was simulated. The different curves represent the profile of rate v for different values of the substrate S.

S = {15,25,61,92,115,250}

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