Proteomics-based study on the protective effect and mechanism of Inkslan on type 2 diabetes mouse model through transcription growth factor β pathway
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus Musculus
SUBMITTER: 
Weihua Wu
PROVIDER: PXD062186 | iProX | Mon Mar 24 00:00:00 GMT 2025
REPOSITORIES: iProX
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Project description:This model is from the article:
Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics.
Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology
2011 May 24;7:492. 21613981
,
Abstract:
Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation. The TGF-β pathway elicits a transient signaling response to a single pulse of TGF-β stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-β pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-β levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-β pathway might be critical for cell fate determination.
Note:
Developer of the model: Zhike Zi
Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011
1. The global parameter that set the type of stimulation
(a) for sustained TGF-beta stimulation: set stimulation_type = 1.
(b) for single pulse of TGF-beta stimulation: set stimulation_type = 2.
parameter "single_pulse_duration" is for the duration of stimulation, for example,
single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation.
*Note: make sure that the time course cover the time point when the event is triggered.
(c) for single pulse of TGF-beta stimulation in COPASI
change the trigger of event "single_pulse_TGF_beta_washout"
from
"and(eq(stimulation_type, 2), eq(time, single_pulse_duration))" (for SBML-SAT)
to
"and(eq(stimulation_type, 2), gt(time, single_pulse_duration))" (for COPASI)
2. Notes for TGF-beta dose in terms of molecules per cell
(a) The following equation applies for conversion of TGF-beta dose in molecules per cell
TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23
(b) for standard experimental setup 1e6 cells in 2 mL medium
0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell
0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell
(c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed.
initial Vmed = 1e-8 (1e6 cells in 10 mL medium)
Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium)
3. Please note that this model contains events and the medium compartment size is varied.
4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.
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