ABSTRACT
This chapter discusses representative examples of models that illustrate certain aspects of signal transduction systems. These models fall into two distinct classes with only scant overlap. The first class considers signaling networks as graphs and tries: to understand their functionality; and to deduce their causal connectivity from experimental observations with methods of discrete mathematics and statistics. The second class attempts to capture the dynamics of signaling networks with ordinary or stochastic differential equation models. An intuitive approach toward assessing signaling systems is a network with hardwired connections and signaling events that happen on a discrete timescale. Boolean networks can easily be interpreted as signaling systems, for instance, in genomes. Two-component signaling systems occur widely in archaea and bacteria, as well as in some fungi and plants. The differential equation models discussed so far are paradigmatic signal transduction systems in the narrowest sense. Many other model approaches have been proposed under the same rubric of signaling systems.
