and D, respectively, wherek 1 rules the yellow!blue rate. In panel
D the global time course of the considered process is reported and
the arrows indicate the three equilibrium states. Such a simulation
exercise can be obviously approached through the corresponding
set of differential equations, available in this case. However, the
appealing feature of the MAS simulator remains the straightfor-
ward, intuitive connection with the statistical nature underlying any
first-order kinetic process: for example, whenk 1 ¼0.7 the switch
fromC 1 toC 2 of each member of the agents population is provided
by the following statement:ask agents[if color¼yellow and random 100< 70 [set color¼blue]]where random<100 is a random integer between 0 and 99.2.1.3 Some Results Connecting structural and functional properties has always been
the most useful way to describe and predict the behavior of living
systems, independent of size and specific features. This entitles the
use of the above modeling approach at different dimensional levels
and, in particular, in the case of cell populations where phenotypical
(structural) changes may reflect events of huge physiological and
pathological relevance. More specifically, having in mind the
changes in cellular and tissue architecture associated with neoplastic
Fig. 2Dynamics of conformational changes. A 100% “regular, yellow” population of agents (pretty similar to a
cell population in a Petri dish, panelA)att 0 undergoes a reversible transition to a “spiky, blue” state. The
yellow/blue ratio at equilibrium, as defined by the rates of the direct and inverse change, is about 1, 2, and 0.5
in panelsB,C,andD, respectively (see also the text). PanelEshows the time course of an overall process
including the transitions A!B!C!D!A, and thearrowsindicate, from left to right, the equilibrium state in
B,C,andD
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