Diedrichs2018 - A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response

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Model Identifier
BIOMD0000000703
Short description
A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response

This model is described in the article:

Diedrichs DR, Gomez JA, Huang CS, Rutkowski DT, Curtu R.
Mol. Biol. Cell 2018 Apr; : mbcE17090565

Abstract:

The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unreported differential role for the eIF2? phosphatase GADD34 in exacerbating severe stress but ameliorating mild stress. We then used the model to test the functional importance of a feed-forward circuit within the PERK/CHOP axis, and of cross-regulatory control of BiP and CHOP expression. We found that the wiring structure of the UPR appears to balance the ability of the response to remain sensitive to ER stress yet also to be rapidly deactivated by improved protein folding conditions. This model should serve as a valuable resource for further exploring the regulatory logic of the UPR.

This model is hosted on BioModels Database and identified by: MODEL1803300000.

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 (L2V4)
Related Publication
  • A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response.
  • Diedrichs DR, Gomez JA, Huang CS, Rutkowski DT, Curtu O
  • Molecular biology of the cell , 6/ 2018 , Volume 29 , Issue 12 , pages: 1502-1517 , PubMed ID: 29668363
  • Department of Mathematics, College of Liberal Arts and Sciences, University of Iowa, Iowa City, IA 52242.
  • The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unreported differential role for the eIF2α phosphatase GADD34 in exacerbating severe stress but ameliorating mild stress. We then used the model to test the functional importance of a feedforward circuit within the PERK/CHOP axis and of cross-regulatory control of BiP and CHOP expression. We found that the wiring structure of the UPR appears to balance the ability of the response to remain sensitive to endoplasmic reticulum stress and to be deactivated rapidly by improved protein-folding conditions. This model should serve as a valuable resource for further exploring the regulatory logic of the UPR.
Contributors
Submitter of the first revision: Danilo R. Diedrichs
Submitter of this revision: Danilo R. Diedrichs
Modellers: Danilo R. Diedrichs

Metadata information

is (2 statements)
BioModels Database MODEL1803300000
BioModels Database BIOMD0000000703

isDescribedBy (2 statements)
PubMed 29668363
PubMed 29668363

isVersionOf (2 statements)
occursIn (1 statement)
Gene Ontology endoplasmic reticulum

hasPart (1 statement)

Curation status
Curated

Tags

Connected external resources

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Model files

BIOMD0000000703_url.xml SBML L2V4 representation of Diedrichs2018 - A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response 135.72 KB Preview | Download

Additional files

BIOMD0000000703-biopax2.owl Auto-generated BioPAX (Level 2) 20.22 KB Preview | Download
BIOMD0000000703-biopax3.owl Auto-generated BioPAX (Level 3) 30.26 KB Preview | Download
BIOMD0000000703.m Auto-generated Octave file 15.89 KB Preview | Download
BIOMD0000000703.png Auto-generated Reaction graph (PNG) 112.27 KB Preview | Download
BIOMD0000000703.sci Auto-generated Scilab file 170.00 Bytes Preview | Download
BIOMD0000000703.svg Auto-generated Reaction graph (SVG) 46.25 KB Preview | Download
BIOMD0000000703.vcml Auto-generated VCML file 910.00 Bytes Preview | Download
BIOMD0000000703.xpp Auto-generated XPP file 12.20 KB Preview | Download
BIOMD0000000703_urn.xml Auto-generated SBML file with URNs 135.70 KB Preview | Download
Diedrichs2018.cps Curated and annotated COPASI file 226.95 KB Preview | Download

  • Model originally submitted by : Danilo R. Diedrichs
  • Submitted: Mar 30, 2018 11:38:46 PM
  • Last Modified: May 24, 2018 5:41:04 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: May 24, 2018 5:41:04 PM
    • Submitted by: Danilo R. Diedrichs
    • With comment: Current version of BIOMD0000000703
  • Version: 1 public model Download this version
    • Submitted on: Mar 30, 2018 11:38:46 PM
    • Submitted by: Danilo R. Diedrichs
    • With comment: Original import of BIOMD0000000703

(*) 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
Reactions
Reactions Rate Parameters
G => ER*kdG*G kdG = 0.003852 1/(16.6667*s)
=> b; A4, A6, x ER*(kdb*(1+alphaI*(Ip-Ip_star))/(1+betaI*(Ip-Ip_star))*b_star+alphaA6*(1+Kb6*A4)*(A6-A6_star)^nA6/((A6-A6_star)^nA6+KA6^nA6*(1+Kth6*A4^nA))+alphaA4*(1+Kb4*A6)*(A4-A4_star)^nA4/((A4-A4_star)^nA4+KA4^nA4*(1+Kth4*A6)^nA4)+alphaX*(x-x_star)/((x-x_star)+KX)) A6_star = 1.0 1; nA4=2.0; nA=7.0; KA6=3.0; Kth6=1.0E-5; x_star = 1.0 1; Ip = 1.0 1; Kb4=0.5; alphaX=0.002; alphaA6=0.012; kdb = 0.001284 1/(16.6667*s); Ip_star = 1.0 1; betaI = 0.1 1; KA4=3.0; alphaA4=0.007; Kb6=0.56; alphaI = 0.2 1; nA6=2.0; b_star = 1.0 1; KX=8.0; Kth4=0.167; A4_star = 1.0 1
x => ; A6 ER*kdx*x kdx = 0.006546 1/(16.6667*s)
=> x ER*ksp*Ip*(xtot_norm-x)/((Kx+xtot_norm)-x) ksp=0.00785; Kx=3.0; xtot_norm = 16.0 1; Ip = 1.0 1
=> g; A4, C ER*(kdg*g_star+etaC*((A4-A4_star)+KA4g*(A4-A4_star)*(C-C_star))/((A4-A4_star)+Kth4g*(A4-A4_star)*(C-C_star)+KC)) KA4g=0.75; C_star = 1.0 1; etaC=0.012; Kth4g=0.1; kdg = 0.003468 1/(16.6667*s); g_star = 1.0 1; KC=5.0; A4_star = 1.0 1
=> c; A6, A4, C ER*(kdc*c_star+muA4*(1+Kc4*A6)*(A4-A4_star)^n/((A4-A4_star)^n+KA4c^n*(1+Kth4c*A6)^n)) KA4c=2.0; Kth4c=0.25; c_star = 1.0 1; muA4=0.1; A4_star = 1.0 1; Kc4=0.56; n=2.0; kdc = 0.00393 1/(16.6667*s)
=> C; Ep, c ER*(kdC*C_star/c_star+ktC*(Ep-Ep_star))*c C_star = 1.0 1; ktC=1.0E-4; kdC = 0.005478 1/(16.6667*s); c_star = 1.0 1; Ep_star = 1.0 1
U => ; x ER*delta*U/(1+KII*(Ip-Ip_star))*B Ip_star = 1.0 1; KII=0.01; B = 0.444444444444444 1; delta=1.5; Ip = 1.0 1
=> U; Ep, U ER*(ksU/(1+KUI*(Ip-Ip_star))+Stress)/(1+Ep/KE+(U/KUU)^n) KUI=0.01; Ip_star = 1.0 1; KUI = 2.17848410757946 1; Stress = 2.0 1/(16.6667*s); KE=3.0; ksU=0.89; n=4.0; Ip = 1.0 1; KUU=6.0
b => ; A4, A6 ER*kdb*(1+alphaI*(Ip-Ip_star))/(1+betaI*(Ip-Ip_star))*b kdb = 0.001284 1/(16.6667*s); alphaI = 0.2 1; Ip_star = 1.0 1; betaI = 0.1 1; Ip = 1.0 1
Curator's comment:
(added: 24 May 2018, 17:40:43, updated: 24 May 2018, 17:40:43)
The model as such, when simulated in COPASI, produces the exact simulation results as shown by the blue curves (2.5 model) in Figure 3 of the publication. Since the publication simulations were performed in COPASI, the curated figure illustrates the simulation results obtained with SBMLsimulator 1.2.1 using the Runge Kutta Event Solver and an interval size of 0.1. The simulation data was imported into and plotted with MATLAB R2104b. The same general behaviour is observed; the only discrepancy is that the values are slightly underestimated.