Biomedical Engineering Reference
In-Depth Information
lytic activity and move free in solution. However, when multiple proteins with
different enzymatic activities assemble together, they can form a higher-order
enzyme complex that exhibits stable 3D form as well as novel functions based
on coupled metabolic processing activities. For example, the pyruvate dehydro-
genase enzyme complex has a mass approaching 10 million Daltons in mam-
mals and it exhibits a highly organized, pentagonal dodecahedral shape (69).
Similarly, individual cells of the pancreas can secrete digestive enzymes in a
polarized manner (i.e., from the apical pole of the cell); however, disease (pan-
creatitis) results if these cells dissociate from each other and their orienting ex-
tracellular matrix scaffold, and lose their higher-order tissue architecture. Thus,
each level has its specific rules of interaction that involve structural as well as
dynamic constraints since the nature of the parts and interactions of each level
are different. Therefore, unlike fractals, we have discrete layers of patterns gov-
erned by distinct, level-specific rules, and there is no general "scale-invariance,"
although some principles apply to various scales, as we will see.
(ii)
Heterogeneity of interacting elements
. The entities in systems with
emergent (e.g., individual molecules with characteristic 3D structure and func-
tion, multimolecular complexes with novel enzyme activities, organelles with
specialized metabolic functions, living cells that move and grow) do not form a
uniform population, as is the case for the molecules in self-organizing patterns
of chemicals or of individuals in schools of fish. Instead, these entities are
unique individuals, or belong to classes of entities with similar properties that
can be clearly distinguished from each other. For instance, cells that arise from
molecular self-assembly and give rise to tissues can be classified into hundreds
of qualitatively different classes or types (e.g., liver, muscle, nerve, skin).
The complexity of each biological network leads to individuality of the
emergent entities (e.g., a cell type), even if their component parts are identical
(e.g., the genes). This variety of individual entities, which serve as building
blocks, enables combinatorial diversity at the next level of integration (e.g., tis-
sues). This introduces new types of interaction rules at each size scale, thus add-
ing a unique layer of complexity characteristic of living organisms. As
mentioned earlier, it is the heterogeneity and size of the population of the mo-
lecular parts that has necessitated both the massively parallel analytical methods
and the detailed modeling approaches of systems biology. However, systems
biology does not currently embrace the concepts of hierarchy and heterogeneity
in complex systems.
3.
MODEL
:
NETWORKS AS THE GENERAL
CONCEPTUAL FRAMEWORK
Given that the cells are the most basic building unit of life, which itself is a
complex system, we will first focus our discussion on how cell shape and func-
