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Logical, continuous and stochastic models
The four main model categories that will be considered in this review are: logical models, in which the a state variable takes one of a number of discrete values; continuous models, in which each state variable is a continuous value; and stochastic models, in which each state is a probability distribution of possible configurations. The final category consists of network models, which are primarily concerned with the structure of networks and their evolution, and only secondarily with dynamics. Within these primary categories, models may also be distinguished by their treatment of space and time.
Of the models that are concerned purely with intracellular dynamics (i.e., regulation within the cell), many omit any reference to regulation taking place in a spatial domain, Other models acknowledge the fact that cells have complex spatial structures by including time delays due to diffusion and molecular transport. Some models, such as investigating pattern formation, model the spatial arrangement of groups of cells. This spatial arrangement may be in either two or three dimensions and may be represented either on a grid or in a continuous space.
Similarly, the evolution of a system behaviour in the temporal dimension may be modelled in a discrete or continuous fashion. When time is measured in a discrete fashion, as it often must be if a model is being simulated, or solved numerically, a further choice arises of whether state variables are updated all at once (synchronously), or independently (asynchronously). Some models are concerned purely with the static structural properties of interaction networks and include no temporal dimension. A number of models have been designed to investigate how regulatory networks have evolved. In these models, evolutionary time may also be a factor.
Next: Different motivations for building Up: A diversity of models Previous: Key features of dynamic Nic Geard 2004-05-06