Zhike Zi. Constraint-Based Modeling and Kinetic Analysis of the Smad Dependent TGF-β Signaling Pathway

Transcription

1 Constraint-Based Modeling and Kinetic Analysis of the Smad Dependent TGF-β Signaling Pathway Zhike Zi Computational Systems Biology Max-Planck Institute for Molecular Genetics Ihnestr. 73, Berlin, Germany Friday, January 18,

2 Introduction of TGF-β Signaling Pathway TGF-β non-clathrin dependent internalization clathrin dependent internalization LRC P Cytoplasm R-Smad Co-Smad Nucleus ligand induced negative feedback via I-Smad/Smurf P Early Endosome Lipid-raft Caveolae Constitutive Production Constitutive Degradation Ligand Induced Degradation Friday, January 18,

3 Existing Models Assumption: Only internalized ligand-bound receptors have kinase activity and the signal readout is proportional to the internalized ligand-receptor complex. General simple model Conclusion: ratio of constitutive to ligand-induced receptor degradation, CIR, is the key quantity that determines the qualitative behavior of the pathway Vilar et al. 2006, PLoS Comput Biol 2:e3 Friday, January 18,

4 Existing Models Clarke et al. 2006, IET Systems Biology,153:412 Assumption: The trafficking of receptors are not considered. Epithelial cell Melke et al. 2006, Biophs J. 91:4368 Assumption: The trafficking of receptors are not considered, Smad shuttling is also ignored. Endothelial cell Different parameter sets can show similar Smad phosphorylation profile. The distribution of the parameters can vary in several magnitude Two groups of parameters are identified: group1: sensitive group, groupt2: robust group Friday, January 18,

5 Motivation A complete model linking the receptor trafficking and Smad phosphorylation is missed Existing models fitted only partial experimental data, they are limited to explain a certain data and fail to explain other data How the downstream signal output is affect by the upstream receptor trafficking steps? Friday, January 18,

6 Challenge Identifiability of the model parameters Less experimental data than the number of parameters Experimental data come from different sources Epithelial cells (HaCaT) Friday, January 18,

7 Available time course data Lin et al, 2006, Cell, 125:915 TGF-β LRC Lin et al, 2006, Cell, 125:915 P Cytoplasm Nucleus ligand induced negative feedback via I-Smad/Smurf P Friday, January 18,

8 Available other data TGF-β Distribution of Smads in cytoplasm and nucleus LRC P Cytoplasm Nucleus ligand induced negative feedback via I-Smad/Smurf P Schmierer et al, 2005, Mol Cell Biol, 25:9845 Friday, January 18,

9 Available other data TGF-β Goumans et al, 2002, EMBO J, 21:1743 LRC P Cytoplasm Nucleus ligand induced negative feedback via I-Smad/Smurf P Friday, January 18,

10 Assumptions TGF-β LRC P Cytoplasm Nucleus ligand induced negative feedback via I-Smad/Smurf P Guglielmo et al 2003, Nat Cell Biol, 5:410 Friday, January 18,

11 Derive parameter values Friday, January 18,

12 Derive the initial conditions Friday, January 18,

13 Constraints from qualitative data The number of total receptors per cell is between 1000 and 100, 000 per cell (Wakefield et al. [26]) The experimental data indicate that about 40~50% of total receptors are in early endosomes. Therefore, we constraint that about 30~60% of receptors locate in the early endosomes. Friday, January 18,

14 Constraints from qualitative data 0.5 ng/ml (20 pm) of TGF-β is sufficient to get a maximal response of Smad2 phosphorylation after 1 hour TGF-β treatment [27,28]. psmad2 at 1h with 0.5 ng/ml TGF - β 0.9 psmad2 at 1h with 2 ng/ml TGF - β 1.1 Friday, January 18,

15 Estimation of the unknown parameters Pipeline of constraint-based modeling Establish a kinetic model in SBML format Derivation of some parameter values according to quantitative experimental analysis and steady sate analysis List of the constraints for the system based on qualitative experimental analysis g(x, p)<=0 Estimate the unknown parameter values by our SBML based parameter estimation tool (SBML-PET, Zi & Klipp, 2006, Bioinformatics, 22: 2704) with stochastic ranking evolution strategy algorithm, which is used to find the most feasible parameters that reproduce the time course data and satisfy the constraints at the same time Friday, January 18,

16 Comparison of model simulation with experimental results Lin et al, 2006, Cell, 125:915 (A B) for in-sample fit. (C D) for out-sample fit Friday, January 18,

17 Comparison of model simulation with experimental results Friday, January 18,

18 Estimation of the unknown parameters by only fitting time course data Establish a kinetic model in SBML format Derivation of some parameter values according to quantitative experimental analysis and steady sate analysis List of the constraints for the system based on qualitative experimental analysis g(x, p)<=0 Estimate the unknown parameter values by our SBML based parameter estimation tool (SBML-PET, Zi & Klipp, 2006, Bioinformatics, 22: 2704) with stochastic ranking evolution strategy algorithm, which is used to find the most feasible parameters that reproduce the time course data and satisfy the constraints at the same time Friday, January 18,

19 Comparison of experimental analysis and simulation results from the model obtained by only fitting the time course data Over fitted, too good to be true Goumans et al, 2002, EMBO J, 21:1743 Lin et al, 2006, Cell, 125:915 Friday, January 18,

20 Range of the variation for the estimated parameters in the 1000 parameter sets Friday, January 18,

21 Existing Models Clarke et al. 2006, IET Systems Biology,153:412 Assumption: The trafficking of receptors are not considered. Epithelial cell Melke et al. 2006, Biophs J. 91:4368 Assumption: The trafficking of receptors are not considered, Smad shuttling is also ignored. Endothelial cell Different parameter sets can show similar Smad phosphorylation profile. The distribution of the parameters can vary in several magnitude Two groups of parameters are identified: group1: sensitive group, groupt2: robust group Friday, January 18,

22 Sensitivity analysis of the parameters Friday, January 18,

23 Regulation of TGF-β signal TGF-β Inhibition of clathrin dependent internalization ki EE by KCL -, Dynamin K44A LRC Inhibition of non-clathrin dependent internalization ki Cave by Nystatin or Dynamin K44A P Cytoplasm Nucleus ligand induced negative feedback via I-Smad/Smurf P Friday, January 18,

24 Controversial experimental data Inhibition of clathrin dependent internalization ki EE by KCL -, Dynamin K44A Lu et al, 2002, J Biol Chem, 277:29363 Inhibition of non-clathrin dependent internalization ki Cave by Nystatin or Dynamin K44A Is receptor endocytosis essential for Smad2 activation? Guglielmo et al 2003, Nat Cell Biol, 5:410 Friday, January 18,

25 Regulation of TGF-β signal TGF-β TGF-β LRC LRC Inhibition of clathrin dependent internalization ki EE by KCL -, Dynamin K44A Inhibition of non-clathrin dependent internalization ki Cave by Nystatin or Dynamin K44A Computational simulations of the time course of nuclear phosphorylated Smad2 by the inhibition of different receptor endocytosis in 1000 parameter sets estimated by constraint-based modeling method Friday, January 18,

26 Regulation of TGF-β signal TGF-β LRC Zi & Klipp 2007, PLoS ONE 2:e963 Vilar et al. 2006, PLoS Comput Biol 2:e3 P Cytoplasm Nucleus ligand induced negative feedback via I-Smad/Smurf P Friday, January 18,

27 Ongoing work TGF-β LRC P Cytoplasm Nucleus ligand induced negative feedback via I-Smad/Smurf P Gene Expression Model JunB Smad6/7 Arkadia PAI SnoN c-jun p15 p21 Smurf c-myc Friday, January 18,

28 Ongoing work Robert A. Weinberg, 2007, The Biology of Cancer Friday, January 18,

29 Ongoing work Friday, January 18,

30 Summary The performance of the model generated by constraint-based modeling method is significantly improved compared to the model obtained by only fitting the quantitative data The model suggest that the R-Smad activation is controlled by the balance between clathrin dependent endocytosis and nonclathrin mediated endocytosis. The higher ratio of clathrin to nonclathrin depedent endocytosis, the stronger activation of R-Smad. Ongoing work requires more efforts on experimental data with high quantitative quality Friday, January 18,

31 Acknowledgements Computational Systems Biology Group in MPI Molecular Genetics Edda Klipp Ludwig Institute for Cancer Research Uppsala Branch Aristidis Moustakas Friday, January 18,

32 Thank you! Friday, January 18,