Benary2019 - Controlling NFKB dynamics by B-TrCP

Model Identifier
BIOMD0000000794
Short description
its a mathematical model studying impact of b_TrCP on NFKB nuclear dynamics. This model is derived from Lipniacki2004 (PMID:15094015).
Format
SBML
(L2V4)
Related Publication
-
Controlling Nuclear NF-κB Dynamics by β-TrCP-Insights from a Computational Model.
- Benary U, Wolf J
- Biomedicines , 5/ 2019 , Volume 7 , Issue 2 , PubMed ID: 31137887
- Mathematical Modelling of Cellular Processes, Max Delbrück Center for Molecular Medicine, 13125 Berlin-Buch, Germany. uwe.benary@mdc-berlin.de.
- The canonical nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway regulates central processes in mammalian cells and plays a fundamental role in the regulation of inflammation and immunity. Aberrant regulation of the activation of the transcription factor NF-κB is associated with severe diseases such as inflammatory bowel disease and arthritis. In the canonical pathway, the inhibitor IκB suppresses NF-κB's transcriptional activity. NF-κB becomes active upon the degradation of IκB, a process that is, in turn, regulated by the β-transducin repeat-containing protein (β-TrCP). β-TrCP has therefore been proposed as a promising pharmacological target in the development of novel therapeutic approaches to control NF-κB's activity in diseases. This study explores the extent to which β-TrCP affects the dynamics of nuclear NF-κB using a computational model of canonical NF-κB signaling. The analysis predicts that β-TrCP influences the steady-state concentration of nuclear NF-κB, as well as changes characteristic dynamic properties of nuclear NF-κB, such as fold-change and the duration of its response to pathway stimulation. The results suggest that the modulation of β-TrCP has a high potential to regulate the transcriptional activity of NF-κB.
Contributors
Submitter of the first revision: Krishna Kumar Tiwari
Submitter of this revision: Krishna Kumar Tiwari
Modellers: Krishna Kumar Tiwari
Submitter of this revision: Krishna Kumar Tiwari
Modellers: Krishna Kumar Tiwari
Metadata information
is (2 statements)
isDescribedBy (1 statement)
hasTaxon (1 statement)
hasProperty (3 statements)
isPartOf (1 statement)
occursIn (1 statement)
isDescribedBy (1 statement)
hasTaxon (1 statement)
hasProperty (3 statements)
Mathematical Modelling Ontology
Ordinary differential equation model
NCIt NFKB Activation Pathway
Gene Ontology response to tumor necrosis factor
NCIt NFKB Activation Pathway
Gene Ontology response to tumor necrosis factor
isPartOf (1 statement)
occursIn (1 statement)
Curation status
Curated
Modelling approach(es)
Tags
Connected external resources
Name | Description | Size | Actions |
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Model files |
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Benary2019.xml | SBML L2V4 file for benary2019 | 234.86 KB | Preview | Download |
Additional files |
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Benary2019.cps | COPASI 4.24 (build 197) file for benary2019 | 230.93 KB | Preview | Download |
Benary2019.sedml | SEDML file for benary2019 | 2.41 KB | Preview | Download |
- Model originally submitted by : Krishna Kumar Tiwari
- Submitted: Aug 13, 2019 4:29:35 PM
- Last Modified: Aug 13, 2019 4:29:35 PM
Revisions
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
Species | Initial Concentration/Amount |
---|---|
IKK neutral Inhibitor of nuclear factor kappa-B kinase subunit alpha |
199.999999987913 nmol |
IKK inact Inhibitor of nuclear factor kappa-B kinase subunit alpha |
2.78889437354332E-25 nmol |
IkB mRNA NF-kappa-B inhibitor alpha |
0.00286970847085134 nmol |
IKK active Inhibitor of nuclear factor kappa-B kinase subunit alpha |
2.44263810567829E-26 nmol |
IKKactive IkB NF-kappa-B inhibitor alpha ; Inhibitor of nuclear factor kappa-B kinase subunit alpha |
1.17746437501971E-28 nmol |
IkB NF-kappa-B inhibitor alpha |
2.5066291758827 nmol |
IkB nuc NF-kappa-B inhibitor alpha |
3.43573095552417 nmol |
IKKactive IkB NFKB NF-kappa-B inhibitor alpha ; Inhibitor of nuclear factor kappa-B kinase subunit alpha ; Nuclear factor NF-kappa-B p105 subunit |
1.41567842221093E-26 nmol |
Reactions
Reactions | Rate | Parameters |
---|---|---|
IKK_neutral => | Cytosol*Kdeg*IKK_neutral | Kdeg = 0.0075 |
IKK_active => IKK_inact; TNF, A20 | Cytosol*function_for_R26(k2, TNF, A20, IKK_active) | k2 = 0.006 |
IkB_mRNA => | Nucleus*c3a*IkB_mRNA | c3a = 0.024 |
IKK_active + IkB_NFKB => IKKactive_IkB_NFKB | Cytosol*a3*IKK_active*IkB_NFKB | a3 = 0.06 |
IKK_active + IkB => IKKactive_IkB | Cytosol*a2*IKK_active*IkB | a2 = 0.012 |
=> IkB; IkB_mRNA | function_for_substrateless_production(c4a, IkB_mRNA) | c4a = 30.0 |
IkB_nuc => | Nucleus*function_for_transport(e1a, Kv, IkB_nuc) | e1a = 0.03; Kv = 5.0 |
IKKactive_IkB_NFKB => IKK_active + NFKB; b_TrCP | Cytosol*function_for_R3(t2, b_TrCP, IKKactive_IkB_NFKB) | t2 = 6.0 |
IKK_inact => | Cytosol*Kdeg*IKK_inact | Kdeg = 0.0075 |
Curator's comment:
(added: 13 Aug 2019, 16:29:05, updated: 13 Aug 2019, 16:29:05)
(added: 13 Aug 2019, 16:29:05, updated: 13 Aug 2019, 16:29:05)
Literature figure 2A,B,C is reproduced. Simulation is done for 630 mins post TNF stimulation on a pre-simulated steady state updated system. Model encoding and simulation is done on COPASI 4.24 (Build 197). Data plotted using Microsoft excel.