Computational Modelling of Kinase Signalling Cascades
In this chapter, we describe general methods used to create dynamic computational models of kinase signalling cascades, and tools to support this activity. We focus on the ordinary differential equation models, and show how these fit into a general framew
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1. Introduction Computational modelling of intracellular biochemical networks has become a growth topic in recent years, due to advances both in the power and availability of software systems for the simulation and analysis of such networks, as well as an increase in the quality and amount of experimentally determined parameter data available for modelling. Modelling biochemical systems is the core part of the process of BioModel Engineering (1) which is at the interface of computing science, mathematics, engineering, and biology, and provides a systematic approach for designing, constructing, and analyzing computational models of biological systems. BioModel Engineering does not aim at engineering biological systems per se (in contrast to synthetic biology), but rather aims at describing their structure and behaviour, in particular at the level of intracellular molecular processes, using computational tools and techniques.
Rony Seger (ed.), MAP Kinase Signaling Protocols: Second Edition, Methods in Molecular Biology, vol. 661, DOI 10.1007/978-1-60761-795-2_22, © Springer Science+Business Media, LLC 2010
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The most useful kinds of models for signalling pathways are dynamic models that describe the time course behaviour of molecular concentrations or even individual molecules. This contrasts with static models which merely describe the topology of the system, i.e. the molecular species involved and their relationships or wiring diagram. In addition to simulation, dynamic models permit a range of analytical techniques that give insight about system-level features that emerge from the elementary interactions of the components. Emergent properties such as bifurcations, robustness to interference, or oscillations are not obvious from the network topology and their discovery requires computational methodologies. Dynamic models provide a powerful framework for hypothesis generation and testing and the identification of inconsistencies between a model and experimental data. They are often used by life scientists as a means to explore their ideas about the organisation and control of a biological system. The “correctness” of a model can be established in several ways. Biological model validation establishes whether a model contradicts our knowledge of a biological system and hence requires experimental data about the behaviour of the system. A special technique contributing to model validation is model checking, which establishes whether a set of formal properties hold for a model, and is often automated using computer programs. A biologically valid model can be incomplete and hence may not describe all the observations we can potentially make of a system, but should not incorrectly describe those behaviours of the system for which it allows predictions. As the EGFR-activated ERK (EGFR/ERK) pathway is such an important signalling pathway, the deregulation of which has long been implicated in various forms of cancer, it has become a popular target for computational modelling strategies (2–4). Cur
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