Diedrichs2018 - A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response
View the 2019-06 Model of the Month entry for this model
This model is described in the article:
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.
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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.
Submitter of this revision: Danilo R. Diedrichs
Modellers: Danilo R. Diedrichs
Metadata information
isDescribedBy (2 statements)
isVersionOf (2 statements)
occursIn (1 statement)
hasPart (1 statement)
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
Model files |
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| 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 |
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| 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
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Version: 2
- Submitted on: May 24, 2018 5:41:04 PM
- Submitted by: Danilo R. Diedrichs
- With comment: Current version of BIOMD0000000703
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Version: 1
- Submitted on: Mar 30, 2018 11:38:46 PM
- Submitted by: Danilo R. Diedrichs
- With comment: Original import of BIOMD0000000703
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: Variable used inside SBML models
| Species | Initial Concentration/Amount |
|---|---|
| G Protein phosphatase 1 regulatory subunit 15A |
1.0 item |
| b Immunoglobulin Binding Protein ; mRNA |
1.0 item |
| x X-box-binding protein 1 |
1.0 item |
| g Protein phosphatase 1 regulatory subunit 15A ; mRNA |
1.0 item |
| c DNA damage-inducible transcript 3 protein ; mRNA |
1.0 item |
| C DNA damage-inducible transcript 3 protein |
1.0 item |
| U unfolded protein [endoplasmic reticulum lumen] |
1.0 item |
| 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 |
(added: 24 May 2018, 17:40:43, updated: 24 May 2018, 17:40:43)