Biomedical Engineering Reference
In-Depth Information
tion emerge from interactions among thousands of interacting molecules and
genes. Our goal is to uncover principles that govern how many heterogeneous,
interacting molecular components can self-assemble and self-organize to pro-
duce higher-level features characteristic of whole living cells. However, as we
will show, the same design principle may also govern how emergence occurs at
higher levels of organization (e.g., tissues, organs, organism), even though the
higher-level networks are composed of different players with distinct rules of
interaction.
In chemistry, aggregate variables may be used to represent an average prop-
erty of a homogenous population of parts. Unfortunately, because of the hierar-
chy of emergence and the heterogeneity of the parts in a whole organism, this
approach is not well suited to describe living systems. In fact, this is the major
limitation in most past studies that attempt to explain cell structure and mechan-
ics using conventional engineering approaches (e.g., continuum mechanics), as
well as cell function using laws of mass action for molecular interactions.
In contrast, networks provide a simple general formalism for abstraction in
order to study how the collective action of interacting parts gives rise to emer-
gent properties, and thus, a means to handle hierarchical complexity. Because
the essential ingredients that make the whole different from the sum of its parts
are the
interactions
between the heterogeneous components, a biological system
can be formalized as a large
network
that consists of the component elements
(the molecules) and their links (their interactions), which need not be identical.
The major point here is that network models can be applied to both
structural
systems (i.e., physical scaffolds that lend mechanical stability to the network)
and
information-processing
systems (i.e., the abstract diagrams that represent
how elements of the network influence each other's activities and the behavior of
the whole). Applied to mammalian cells, the structural network is the "cy-
toskeleton" that determines how the building blocks (proteins) are physically
attached to each other to give the cell its physical shape and mechanical stabil-
ity. The information-processing network is the regulatory network that deter-
mines how the state of interacting elements (genes and proteins) influence each
other, and thereby process the information that is encoded in the genome or re-
ceived from the external milieu to generate a distinct cell behavior.
The common basic property of both networks in living cells, one structural
and the other informational, is that the collective action of their constituent mo-
lecular elements gives rise to a system with emergent properties. However, there
is a fundamental formal difference. Structural network models describe a con-
crete object while information networks are an abstraction. Nevertheless, while
one may think of the structural network as a physical scaffold in which every
individual building block (including all members of the same class of elements)
has to be depicted in the model, like in an architect's blueprint, the model of the
cell that we discuss below also offers some abstraction. Specifically, models that
contain a few elements (with their prototypic mechanical properties) mimic
