COSBICS

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Background

Work Packages

EU FP6 supported
Specific Targeted Research Project

Computational Systems Biology
of Cell Signalling (COSBICS)

Contract No. LSHG-CT-2004-512060

Cancer can be considered a disease of communication at molecular level. The area of cell signalling investigates the transmission of information from receptors to gene activation by means of biochemical reaction pathways that form complex signalling networks and impinge on development and health of organisms. COSBICS is to establish and apply a novel computational framework in which to investigate dynamic interactions of molecules within cells. Instead of simply mapping proteins in a pathway, COSBICS is concerned with “dynamic pathway modelling”. Dynamic pathway modelling establishes mathematical models to quantitatively predict the spatial-temporal response of signalling pathways and subsequent target gene expression.

Aims

The obtaining of predictive dynamic modelling of three signalling pathways commonly subverted in cancer: JAK2-STAT5, Ras/Raf1/MKE/ERK and NFkB pathway.
Implementation of methodologies that support design of experiments and standardisation of experimental protocols.
The enhancement of our understanding on the decision making in cell communication: cross-talk and coordination of pathways.
Implementation of new techniques for data-driven modelling and simulation (system identification).
Generation of state-of-the-art quantitative time series data describing the investigated systems in the normal and pathological conditions.
Investigation of the role that space and time have in the dynamics of signalling pathways.

Achievements

Non linear kinetic models have been set up to describe the JAK2-STAT5, RAS/RAF1/MEK/ERK and NFkB pathway in the biological conditions investigated in the project.
A plethora of quantitative experimental techniques have been tested and adapted for the purpose of modeling these pathways, including quantitative western blots, life cell imaging, ELISA kits and radioactive labeling.
Extensive sets of quantitative experimental data of sufficient quality to be used in mathematical modeling have been produced for the three pathways investigated.
Several experiments allowed the identification of new potential binding partners of the Raf kinase inhibitor protein (RKIP).
Theoretical and experimental techniques to investigate protein gradients and diffusion effects in the JAK2/STAT5 pathway has been designed, implemented and tested.
An strategy for the reduction of dimensionality in non-linear kinetic models of cellular signal transduction systems was designed and applied to the investigated pathways.
An strategy to use kinetic models based on power-law terms in cell signaling modeling has been implemented and applied.
A MATLAB toolbox for parameter estimation has been developed and implemented. This tool includes global optimisation methods fro parameter estimation as well as numerical tools for parametric sensitivity analysis.

Most recent publications of COSBICS

The role of inhibitory proteins as modulators of oscillations in NFkB signalling (in press): S.Nikolov, J.Vera, O.Rath, W.Kolch, O.Wolkenhauer. IET Systems Biology
Dynamics of receptor and protein transducer homodimerisation (2008): J.Vera, T.Millat, W.Kolch, O.Wolkenhauer. BMC Systems Biology, 2:92.
Dynamic Modeling and Multi-Experiment Fitting with PottersWheel (2008): T.Maiwald, J.Timmer. Bioinformatics.
Dynamic properties of a delayed protein cross talk model (2008): S. Nikolov, J. Vera, V. Kotev, O. Wolkenhauer and V. Petrov. Biosystems. 91:51–68.
PLMaddon: A power-law module for the MatlabTM SBToolbox (2007): J. Vera, C. Sun, Y. Oertel, O. Wolkenhauer. Bioinformatics. 23(19):2638-40.
A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK Pathways (2007): Kim, D., Rath, O., Kolch, W. and Cho, K.H. 2007. Oncogene 26:4571–4579.

MAP kinase signalling pathways in cancer (2007): Dhillon, AS., Hagan, H., Rath, O. and Kolch, W.. Oncogene, 26:3279–3290.
Power-Law Models of Signal Transduction Pathways (2007): Vera, J., Balsa-Canto E., Wellstead, P., Banga, J.R. and Wolkenhauer, O.. Cellular Signalling, 19(2007):1531-1541.
Reaction-diffusion modelling ERK- and STAT- interaction dynamics (2007): Georgiev, N., Petrov, V., Georgiev, G.. Eurasip Journal on Bioinformatics and Systems Biology (accepted).
An error model for protein quantification (2007): Kreutz C., Bartolome Rodriguez M.M., Maiwald T., Seidl M., Blum H.E., Mohr L., Timmer J.. Bioinformatics. 23(20):2747–2753
Data-based identifiability analysis of non-linear dynamical models (2007): Hengl S., Kreutz C., Timmer J., Maiwald T. Bioinformatics. Vol. 23(19):2612–2618
Reduction of Nonlinear Dynamic Systems with an Application to Signal Transduction Pathways (2007): Petrov, V., Nikolova, E., Wolkenhauer, O.. IET Systems Biology, 1(1):2-9
Regulation and role of Raf-1/B-Raf heterodimerization (2006): Rushworth, L.K., Hindley, A.D., O'Neill, E., Kolch, W.. Molecular and Cell Biology, 26(6):2262-72.
Novel Metaheuristic for Parameter Estimation in Nonlinear Dynamic Biological Systems (2006): Rodriguez-Fernandez, M., Egea, J.A., R. Banga, J.:. BMC Bioinformatics 7:483.
Regulation of RKIP binding to the N-region of the Raf-1 kinase (2006): Park, S., Rath, O., Beach, S., Xiang, X., Kelly, S.M., Luo, Z., Kolch, W., Yeung, K.C.. FEBS Letters, 580(27):6405-6412.

International conferences under COSBICS

1st Winter School on Systems Biology for Medical Applications (Puerto de la Cruz, Tenerife, 2007)

International conference on Cell Signalling Systems Biology (CESISB, Rostock, Germany, 2006)

Links

Bioinformatics and Systems Biology: A book on Bioinformatics and Systems Biology by Frederick B. Marcus
SBToolbox2: The Systems Biology Toolbox 2 for Matlab
The Plmaddon: A SBToolbox2 add-on for the analysis of power-law models
www.PowerLawModels.org: A Website on power-law models for signalling pathways