Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA)

  public model
Model Identifier
BIOMD0000000864
Short description
Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100001.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

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.

Format
SBML (L2V3)
Related Publication
  • Computer simulation models as a tool to investigate the role of microRNAs in osteoarthritis.
  • Proctor CJ, Smith GR
  • PloS one , 1/ 2017 , Volume 12 , Issue 11 , pages: e0187568 , PubMed ID: 29095952
  • Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, United Kingdom.
  • The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.
Contributors
Submitter of the first revision: Carole Proctor
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Carole Proctor, Mohammad Umer Sharif Shohan

Metadata information

is (2 statements)
BioModels Database MODEL1610100001
BioModels Database MODEL1610100001

isDescribedBy (1 statement)
PubMed 29095952

hasProperty (1 statement)
Taxonomy Homo sapiens


Curation status
Curated

Tags

Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files

Proctor2017_model1.xml SBML L2V3 representation of Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA) 28.58 KB Preview | Download

Additional files

MODEL1610100001-biopax2.owl Auto-generated BioPAX (Level 2) 13.48 KB Preview | Download
MODEL1610100001-biopax3.owl Auto-generated BioPAX (Level 3) 24.00 KB Preview | Download
MODEL1610100001.m Auto-generated Octave file 5.14 KB Preview | Download
MODEL1610100001.png Auto-generated Reaction graph (PNG) 55.35 KB Preview | Download
MODEL1610100001.sci Auto-generated Scilab file 67.00 Bytes Preview | Download
MODEL1610100001.svg Auto-generated Reaction graph (SVG) 20.40 KB Preview | Download
MODEL1610100001.vcml Auto-generated VCML file 17.70 KB Preview | Download
MODEL1610100001.xpp Auto-generated XPP file 3.08 KB Preview | Download
MODEL1610100001_url.xml old xml file 14.68 KB Preview | Download
MODEL1610100001_urn.xml Auto-generated SBML file with URNs 14.67 KB Preview | Download
Proctoe2017_model1.cps COPASI version 4.24 (Build 197) Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA) 65.65 KB Preview | Download
Proctor2017_model1.sedml SEDML L1V2 Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA) 1.01 KB Preview | Download

  • Model originally submitted by : Carole Proctor
  • Submitted: Oct 10, 2016 4:14:02 PM
  • Last Modified: Nov 14, 2019 3:55:47 PM
Revisions
  • Version: 4 public model Download this version
    • Submitted on: Nov 14, 2019 3:55:47 PM
    • Submitted by: Mohammad Umer Sharif Shohan
    • With comment: Automatically added model identifier BIOMD0000000864
  • Version: 2 public model Download this version
    • Submitted on: Nov 6, 2017 11:23:05 AM
    • Submitted by: Carole Proctor
    • With comment: Current version of MODEL1610100001
  • Version: 1 public model Download this version
    • Submitted on: Oct 10, 2016 4:14:02 PM
    • Submitted by: Carole Proctor
    • With comment: Original import of MODEL1610100001

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
TF1 0.0 item
miR gene 2.0 item
miR gene TF1 0.0 item
TF1 mRNA 0.0 item
Signal 0.0 item
miR 0.0 item
Reactions
Reactions Rate Parameters
miR_gene + TF1 => miR_gene_TF1 cell*kbinTF1miRgene*miR_gene*cell*TF1*cell/cell kbinTF1miRgene = 0.005
miR_gene_TF1 => miR_gene + TF1 cell*krelTF1miRgene*miR_gene_TF1*cell/cell krelTF1miRgene = 5.0
Signal => Signal + TF1_mRNA cell*ksynTF1mRNA*Signal*cell/cell ksynTF1mRNA = 10.0
TF1_mRNA => TF1_mRNA + TF1 cell*ksynTF1*TF1_mRNA*cell/cell ksynTF1 = 0.05
miR_gene_TF1 => miR_gene_TF1 + miR cell*ksynMiR*miR_gene_TF1*cell/cell ksynMiR = 5.0
TF1_mRNA + miR => miR cell*kdegTF1mRNAbyMiR*TF1_mRNA*cell*miR*cell/cell kdegTF1mRNAbyMiR = 0.001
Curator's comment:
(added: 14 Nov 2019, 15:53:27, updated: 14 Nov 2019, 15:53:27)
The model has been encoded using COPASI 4.24 (Build 197) and the Supplementary figure C is generated using ggplot package of R. The figure is not exact but the pattern is similar