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Vilar et al. (2006), TGF-β

September 2008, model of the month by Noriko Hiroi
Original model: BIOMD0000000101

Under normal physiological conditions, members of the transforming growth factor-β (TGF-β) family help control tissue growth. In particular, TGF-β triggers antiproliferative response in epithelial, endothelial, neuronal and haematopoietic cells. In normal cells, TGF-β induces sustained signals that last more than 6 h. In contrast, certain tumours develop a resistance to the growth-suppressive effects of TGF-β, which is not based on mutations. In this case, TGF-β promotes cancer progression in tumour microenvironments. In cancer cells, TGF-β-induced signals are much shorter than in normal cells, lasting usually about 1-2 h [1, 2]. The computational model [BIOMD0000000101, 3] set out to study the mechanisms underlying these paradoxical characteristics of TGF-β family-induced signals.

The model encompasses the detection and transduction of the extracellular signals to transcription factors (Smad2 etc.), as well as TGF-β receptor trafficking. The authors' main conclusion is that the key quantity which determines the qualitative behaviour of the pathway is the ratio between the ligand-induced degradation of active complexes and their constitutive degradation. When ligand-induced degradation is dominant, the activated (phosphorylated) form of the final target of this signal (Smad2) exhibits a short activation (Figure 2 A, B). On the other hand, when the constitutive degradation process is dominant, activated Smad2 lasts longer (Figure 2 E, F). These two different behaviours can be compared to the phenotypes shown by cancerous and normal epithelial cells.

Model of receptor trafficking and signalling

Figure 1: Model of receptor trafficking and signalling. Figure taken from [3] and modified by N.H. to facilitate comparison with the version in BioModels database (BIOMD0000000101).

By considering the large variation of ligand-receptor combinations within the TGF-β family, the authors proposed an hypothesis to explain how TGF-β could work both as a suppressive signal and also as a stimulus for cell proliferation. TGF-β receptors activate different downstream pathways according to the upstream signal. By competitive inhibition, one ligand can potentially inhibit the effects of another one (Figure 3). Thus, if TGF-β loses its growth suppressor properties, it could promote growth by inhibiting the other growth suppressor pathways.

Other models in BioModels Database cover the biochemistry of TGF-β, such as BIOMD0000000112 [4], BIOMD0000000163 [5], and BIOMD0000000173 [6].


Control of Kinetic Signalling Behaviour

Figure 2: Control of Kinetic Signalling Behaviour. Smad2 activity over time is shown at different constitutive-to-induced degradation ratios (CIR) (low for A, B, medium for C, D, high for E, F) and different shapes of TGF-β input (stepwise increase for A, C, E, slow increase for B, D, F). Figure taken from [3].

Control of Signal Integration

Figure 3: Control of Signal Integration. Formation of ligand-receptor complexes at different CIR and input shapes of TGF-β. Figure taken from [3].

Bibliographic References

  1. P.M. Siegel, J. Massagué. Cytostatic and apoptotic actions of TGF-beta in homeostasis and cancer. Nat Rev Cancer 3:807-821, 2003. [SRS@EBI]
  2. F.J. Nicolas, C.S. Hill. Attenuation of the TGF-beta-Smad signaling pathway in pancreatic tumor cells confers resistance to TGF-beta-induced growth arrest. Oncogene 22: 3698-3711, 2003. [SRS@EBI]
  3. J.M. Vilar, R. Jansen, C. Sander. Signal processing in the TGF-beta superfamily ligand-receptor network. PLoS Comput Biol 2:e3, 2006. [SRS@EBI]
  4. D.C. Clarke, M.D. Betterton, X. Liu. Systems theory of Smad signalling. Syst Biol (Stevenage) 153(6):412-424, 2006.[SRS@EBI]
  5. Z. Zi, E. Klipp. Constraint-based modeling and kinetic analysis of the smad dependent TGF-Beta signaling pathway. PLoS ONE 2(9):e936, 2007. [SRS@EBI]
  6. B. Schmierer, A.L. Tournier, P.A. Bates, and C.S. Hill. Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system. Proc Natl Acad Sci U S A. 105:6608-6613, 2008. [SRS@EBI]
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