Biomedical Engineering Reference
In-Depth Information
Table 3
. Cancer modeled as a complex adaptive system (CAS);
these elements allow emergence of the CAS
Elements of a complex adaptive
Corresponding elements of a
system (CAS)
CAS in cancer
Agents: set of active components that
Cells
interact selectively
Building blocks: provide a mechanism
The genes that cancer cells draw on to
for generating a wide range of rules, tags,
acquire the properties that are necessary for
and internal models from a small number
survival; this often requires the activation
of parts
of genes that are normally turned off in
normal tissue
Aggregation: components group together
Cells with similar adaptive mutations
according to similar abilities
survive while others undergo apoptosis
and die
Nonlinearity: a property resulting from
One cell cannot produce enough VEGF to
conditional (nonadditive) interactions
stimulate new blood vessel growth to
between agents
supply the tumor with nutrients but many
cells together can
Flow: a property mediated by the
IF a cell produces proteases, THEN the
movement of agents within the CAS; this
tissue microenvironment will be broken
can be represented by a series of IF/THEN
down and a cell will be able to escape its
rules
local environment
Diversity: a property resulting when
Genetic instability gives cells adaptive
agents compete and adapt to fill available
advantages that allow for clonal expansion
"niches" within the system
and survival of the fittest
Tagging: a mechanism that facilitates
The tissue matrix of the cancer cells allows
interactions between and among
dynamic remodeling of the system
components
Internal Model: a mechanism for
Cancer cells turn on genes that allow
providing agents with anticipatory actions
them to use multiple growth factors
from a variety of different organ
microenvironments—key to
successful metastasis
4.1. Cells Are the Agents of the Cancer Complex Adaptive System
Complex systems are organized as a finite number of states, which can be
defined by Boolean networks. A Boolean network is an array of elements, which
has a particular rule associated with it, linked by a finite number of inputs. As
the number of elements and links increases, the number of initial states of the
system also increases. By cycling through the network (i.e., applying the ele-
ments rules as influenced by their links), however, one finds that the number of
states the system occupies is limited to certain specific state-cycles (attractors)
