Proctor2010 - UCHL1 Protein Aggregation

  public model
Model Identifier
BIOMD0000000293
Short description

This a model from the article:
Modelling the Role of UCH-L1 on Protein Aggregation in Age-Related Neurodegeneration.
Proctor CJ, Tangeman PJ, Ardley HC. PLoS One. 2010 Oct 6;5(10):e13175 20949132 ,
Abstract:
Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and α-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions.


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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Format
SBML (L2V4)
Related Publication
  • Modelling the role of UCH-L1 on protein aggregation in age-related neurodegeneration.
  • Proctor CJ, Tangeman PJ, Ardley HC
  • PloS one , 10/ 2010 , Volume 5 , pages: e13175 , PubMed ID: 20949132
  • Center for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom. c.j.proctor@ncl.ac.uk
  • Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and α-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions.
Contributors
Carole Proctor

Metadata information

is
BioModels Database MODEL0912070000
BioModels Database BIOMD0000000293
isDerivedFrom
BioModels Database BIOMD0000000105
isDescribedBy
PubMed 20949132
hasTaxon
Taxonomy Homo sapiens
isPartOf
KEGG Pathway Parkinson's disease
hasVersion
Human Disease Ontology neurodegenerative disease
hasProperty
Human Disease Ontology Parkinson's disease

Curation status
Curated

Original model(s)
Proctor_UCHL1_PD

Tags
Name Description Size Actions

Model files

BIOMD0000000293_url.xml SBML L2V4 representation of Proctor2010 - UCHL1 Protein Aggregation 355.35 KB Preview | Download

Additional files

BIOMD0000000293.pdf Auto-generated PDF file 1.55 MB Preview | Download
BIOMD0000000293.xpp Auto-generated XPP file 73.04 KB Preview | Download
BIOMD0000000293-biopax2.owl Auto-generated BioPAX (Level 2) 597.43 KB Preview | Download
BIOMD0000000293.vcml Auto-generated VCML file 549.11 KB Preview | Download
BIOMD0000000293-biopax3.owl Auto-generated BioPAX (Level 3) 1.10 MB Preview | Download
BIOMD0000000293.png Auto-generated Reaction graph (PNG) 5.07 KB Preview | Download
BIOMD0000000293_urn.xml Auto-generated SBML file with URNs 350.88 KB Preview | Download
BIOMD0000000293.m Auto-generated Octave file 105.02 KB Preview | Download
BIOMD0000000293.svg Auto-generated Reaction graph (SVG) 861.00 bytes Preview | Download
BIOMD0000000293.sci Auto-generated Scilab file 70.50 KB Preview | Download

  • Model originally submitted by : Carole Proctor
  • Submitted: 07-Dec-2009 13:19:38
  • Last Modified: 08-Apr-2016 17:54:30
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 08-Apr-2016 17:54:30
    • Submitted by: Carole Proctor
    • With comment: Current version of Proctor2010 - UCHL1 Protein Aggregation
  • Version: 1 public model Download this version
    • Submitted on: 07-Dec-2009 13:19:38
    • Submitted by: Carole Proctor
    • With comment: Original import of BIOMD0000000293.xml.origin
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
MisP

protein
80.0 item
Ub

Ubiquitin-60S ribosomal protein L40
1500.0 item
Reactions
Reactions Rate Parameters
(MisP) => (MisP + Ub + upregUb)

([protein]) => ([protein] + [Ubiquitin-60S ribosomal protein L40] + [Ubiquitin-60S ribosomal protein L40])
kubss*MisP^6/(1500^6+MisP^6)

kubss*[protein]^6/(1500^6+[protein]^6)
kubss = 0.1
(NatP + ROS) => (MisP + ROS)

([protein] + [reactive oxygen species]) => ([protein] + [reactive oxygen species])
kmisfold*NatP*ROS

kmisfold*[protein]*[reactive oxygen species]
kmisfold = 4.0E-5
(MisP + SeqAggP) => (SeqAggP + aggMisP)

([protein] + [SeqAggP]) => ([SeqAggP] + [aggMisP])
kigrowth1*MisP*SeqAggP

kigrowth1*[protein]*[SeqAggP]
kigrowth1 = 5.0E-9
(Source) => (Ub)

([Source]) => ([Ubiquitin-60S ribosomal protein L40])
kubs*Source

kubs*[Source]
kubs = 0.009
(E1 + Ub + ATP) => (E1_Ub + AMP)

([E1] + [Ubiquitin-60S ribosomal protein L40] + [ATP]) => ([Ubiquitin-60S ribosomal protein L40] + [AMP])
kbinE1Ub*E1*Ub*ATP/(5000+ATP)

kbinE1Ub*[E1]*[Ubiquitin-60S ribosomal protein L40]*[ATP]/(5000+[ATP])
kbinE1Ub = 2.0E-4
(E3_MisP_Ub2_DUB) => (E3_MisP_Ub_DUB + Ub)

([protein; Ubiquitin-60S ribosomal protein L40]) => ([protein; Ubiquitin-60S ribosomal protein L40] + [Ubiquitin-60S ribosomal protein L40])
kactDUB*E3_MisP_Ub2_DUB

kactDUB*[protein; Ubiquitin-60S ribosomal protein L40]
kactDUB = 1.0E-4
(E3_MisP_Ub_DUB) => (E3_MisP + DUB + Ub)

([protein; Ubiquitin-60S ribosomal protein L40]) => ([protein] + [DUB] + [Ubiquitin-60S ribosomal protein L40])
kactDUB*E3_MisP_Ub_DUB

kactDUB*[protein; Ubiquitin-60S ribosomal protein L40]
kactDUB = 1.0E-4
(asyn_dam_Ub5_Proteasome + ATP) => (Ub + Proteasome + ADP)

([Alpha-synuclein; Ubiquitin-60S ribosomal protein L40; proteasome complex] + [ATP]) => ([Ubiquitin-60S ribosomal protein L40] + [proteasome complex] + [ADP])
kactProt*kproteff*asyn_dam_Ub5_Proteasome*ATP/(5000+ATP)

kactProt*kproteff*[Alpha-synuclein; Ubiquitin-60S ribosomal protein L40; proteasome complex]*[ATP]/(5000+[ATP])
kactProt = 0.01; kproteff = 1.0
Curator's comment:
(added: 10 Jan 2011, 13:49:21, updated: 10 Jan 2011, 13:49:21)
Figure 2 of the reference publication has been reproduced here. The model was integrated and simulated using Copasi v4.6 (Build 32). Stochastic (Gibson + Bruck) Method was used to simulated the model.