Discrete Dynamic Modeling of Signal Transduction Networks
Newly available experimental data characterizing different processes involved in signaling pathways have provided the opportunity for network analysis and modeling of these interacting pathways. Current approaches in studying the dynamics of signaling networks fall into two major groups, namely, continuous and discrete models. The lack of kinetic information for biochemical interactions has limited the wide applicability of continuous models. To address this issue, discrete dynamic models, based on a qualitative description of a system’s variables, have been applied for the analysis of biological systems with many unknown parameters. The purpose of this chapter is to give a detailed description of Boolean modeling, the simplest type of discrete dynamic modeling, and the ways in which it can be applied to analyze the dynamics of signaling networks. This is followed by practical examples of a Boolean dynamic framework applied to the modeling of the abscisic acid signal transduction network in plants as well as the T-cell survival signaling network in humans.
Key wordsBoolean dynamic modeling Synchronous method Asynchronous method Signal transduction
This work was supported by NSF grant CCF-0643529.
- 1.Marks F, Klingmuller U, Muller-Decker K (2009) Cellular signal processing: an introduction to the molecular mechanisms of signal transduction: Garland sciences. Taylor and Francis Group, LLC, Philadelphia, PAGoogle Scholar
- 11.Peterson JL (1981) Petri Net Theory and the modeling of systems. Prentice Hall PTR, Upper Saddle River, NJGoogle Scholar
- 13.Kauffman S (1993) Origins of order: self-organization and selection in evolution. Oxford University Press, OxfordGoogle Scholar
- 22.Harvey I, Bossomaier T (1997) Time out of joint: attractors in asynchronous random Boolean networks. In: Husbands P, Harvey I, editors; Proceedings of the Fourth European Conference on Artificial Life, MIT Press; Cambridge, pp 67–75Google Scholar