Lee2008 - ERK and PI3K signal integration by Myc

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Short description
Mechanisitc model of PI3K and ERK signal integration by Myc. ERK and PI3K regulated Myc satbility by phosphorylating the same. (PMID:18463697)
Related Publication
  • Sensing and integration of Erk and PI3K signals by Myc.
  • Lee T, Yao G, Nevins J, You L
  • PLoS computational biology , 2/ 2008 , Volume 4 , Issue 2 , pages: e1000013 , PubMed ID: 18463697
  • Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
  • The transcription factor Myc plays a central role in regulating cell-fate decisions, including proliferation, growth, and apoptosis. To maintain a normal cell physiology, it is critical that the control of Myc dynamics is precisely orchestrated. Recent studies suggest that such control of Myc can be achieved at the post-translational level via protein stability modulation. Myc is regulated by two Ras effector pathways: the extracellular signal-regulated kinase (Erk) and phosphatidylinositol 3-kinase (PI3K) pathways. To gain quantitative insight into Myc dynamics, we have developed a mathematical model to analyze post-translational regulation of Myc via sequential phosphorylation by Erk and PI3K. Our results suggest that Myc integrates Erk and PI3K signals to result in various cellular responses by differential stability control of Myc protein isoforms. Such signal integration confers a flexible dynamic range for the system output, governed by stability change. In addition, signal integration may require saturation of the input signals, leading to sensitive signal integration to the temporal features of the input signals, insensitive response to their amplitudes, and resistance to input fluctuations. We further propose that these characteristics of the protein stability control module in Myc may be commonly utilized in various cell types and classes of proteins.
Submitter of the first revision: Krishna Kumar Tiwari
Submitter of this revision: Krishna Kumar Tiwari
Modellers: Krishna Kumar Tiwari

Metadata information

is (2 statements)
BioModels Database MODEL1909170004
BioModels Database BIOMD0000000818

isDescribedBy (1 statement)
PubMed 18463697

hasTaxon (1 statement)
Taxonomy Mammalia

hasProperty (3 statements)
Mathematical Modelling Ontology Ordinary differential equation model
Pathway Ontology growth factor signaling pathway
Gene Ontology regulation of protein stability

occursIn (1 statement)
Brenda Tissue Ontology cancer stem cell

Curation status


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Model files

Lee2008.xml SBML L2V4 file for Lee2008 model 63.59 KB Preview | Download

Additional files

Lee2008.cps COPASI 4.24 (build 217) file for Lee2008 model 96.75 KB Preview | Download
Lee2008.sedml SEDML file for Lee2008 model 4.49 KB Preview | Download

  • Model originally submitted by : Krishna Kumar Tiwari
  • Submitted: Sep 19, 2019 2:17:35 PM
  • Last Modified: Sep 19, 2019 2:17:35 PM
  • Version: 3 public model Download this version
    • Submitted on: Sep 19, 2019 2:17:35 PM
    • Submitted by: Krishna Kumar Tiwari
    • With comment: Automatically added model identifier BIOMD0000000818
: Variable used inside SBML models

Reactions Rate Parameters
AKT => AKTp; PI3K Cell*k_ap*PI3K*AKT/(K_AP+AKT) K_AP = 0.01 nmol/l; k_ap = 360.0 1/h
AKTp => AKT Cell*k_AD*AKTp/(K_AD+AKTp) k_AD = 72.0 nmol/(h*l); K_AD = 0.01 nmol/l
GSK3B => GSK3Bp; AKTp Cell*k_GP*AKTp*GSK3B/(K_GP+GSK3B) K_GP = 0.01 nmol/l; k_GP = 360.0 1/h
Myc_ser62 => Myc_thr58; GSK3B Cell*k_MT*GSK3B*Myc_ser62/(K_MT+Myc_ser62) k_MT = 0.4 1/h; K_MT = 0.01 nmol/l
Myc_thr58 => Cell*dMT*Myc_thr58 dMT = 2.08 1/h
Myc => Cell*dM*Myc dM = 2.08 1/h
=> Myc; GF Cell*kM*GF kM = 1.0 1/h
Curator's comment:
(added: 19 Sep 2019, 14:16:40, updated: 19 Sep 2019, 14:16:40)
Figure 1B from the literature is reproduced. Model encoded and simulated using COPASI 4.24 (built217). In the model, units are taken in nM instead of uM (as given in literature) due to COPASI bug with not handling umol unit well and it gives error in SBML check.