Information Technology Reference
In-Depth Information
2
Processing
Ray Paton, Michael Fisher, Grant Malcolm,
and Koichiro Matsuno
This chapter reviews and briefly discusses a set of computational methods that
can assist biologists when seeking to model interactions between components
in spatially heterogeneous and changing environments. The approach can be
applied to many scales of biological organization, and the illustrations we have
selected apply to networks of interaction among proteins.
INTRODUCTION
Biological populations, whether ecological or molecular, homogeneous or het-
erogeneous, moving or stationary, can be modeled at different scales of orga-
nization. Some models can be constructed that focus on factors or patterns that
characterize the population as a whole such as population size, average mass or
length, and so forth. Other models focus on values associated with individuals
such as age, energy reserve, and spatial association with other individuals. A
distinction can be made between population (p-state) and individual (i-state)
variables and models. We seek to develop a general approach to modeling
biosystems based on individuals.
Individual-based models (IBMs) typically consist of an environment or
framework in which interactions occur and a number of individuals defined
in terms of their behaviors (such as procedural rules) and characteristic pa-
rameters. The actions of each individual can be tracked through time. IBMs
represent heterogeneous systems as sets of nonidentical, discrete, interacting,
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