4.5 Review Book Chapter

Modelling the dynamics of signalling pathways

Journal

Publisher

PORTLAND PRESS LTD
DOI: 10.1042/BSE0450001

Keywords

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Funding

  1. NIH [K25 CA 113133, U56 CA 112963]
  2. Case Provost Opportunity Fund Award for the Systems Biology Center of Excellence Initiative
  3. Korea Science and Engineering Foundation (KOSEF)
  4. Korea Ministry of Education, Science and Technology through the Systems Biology [M10503010001-07N030100112]
  5. Nuclear Research Grant [M20708000001-07B0800-00110]
  6. Science Foundation Ireland [RP 03/RPI/1383]
  7. NATIONAL CANCER INSTITUTE [K25CA113133, U56CA112963] Funding Source: NIH RePORTER

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In the present chapter we discuss methodologies for the modelling, calibration and validation of cellular signalling pathway dynamics. The discussion begins with the typical range of techniques for modelling that might be employed to go from the chemical kinetics to a mathematical model of biochemical pathways. In particular, we consider the decision-making processes involved in selecting the right mechanism and level of detail of representation of the biochemical interactions. These Include the choice between (i) deterministic and stochastic chemical kinetics representations, (ii) discrete and continuous time models and (iii) representing continuous and discrete state processes. We then discuss the task of calibrating the models using information available ill web-based databases. For situations iii which the data are not available from existing sources we discuss model calibration based upon measured data and system identification methods. Such methods, together with mathematical modelling databases and computational tools, are often available in standard packages. We therefore make explicit mention of a range of popular and useful sites. As all example of the whole modelling and calibration process, we discuss a study of the cross-talk between the IL-1 (interleukin-1)-stimulated NF-kappa B (nuclear factor kappa B) pathway and the TGF-beta (transforming growth factor beta)-stimulated Smad2 pathway.

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