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back to the fi rst discovery of cellular-level phenomena, have emphasized a
reductionist approach to science [
12
]. In such a reductionist approach, large and
complex problems are broken down into small, manageable units for investigation
and inquiry. The size and scope of such units have historically been dictated by a
combination of inherent human cognitive limitations and the capabilities of avail-
able instruments and data management methods, the latter spanning a spectrum from
paper to early computers to modern cloud based technologies. The belief system
surrounding reductionist thinking has been and continues to be predicated on the
idea that if we can understand the structure, function, or other features of interest of
a given unit of investigation, we can then reassemble the knowledge gained from
such investigation with similar understanding of complementary or co-occurring
units within broader settings (such as biological or organ systems, or at a higher
order, organizations and populations). In effect, the reductionist viewpoint is that
knowledge of a system can be built from “building blocks” of knowledge concerning
the sub-units of that system, as studied in isolation. Such a mindset is quite pervasive
in the biomedical and broader scientifi c communities, dictating aspects of those
fi elds including the ways in which we describe and label various sub-disciplines
from an educational and professional standpoint, to the ways in which we organized
publication venues and funding programs (e.g., in a manner aligned with organ,
disease, or higher-order systems or foci, decomposed from broader systems such as
human beings or populations). However, recent scientifi c endeavors have begun to
elucidate a number of critical fl aws in this type of reductionist thinking, namely [
12
]:
The elemental units that may make up a complex biological system rarely oper-
ate in isolation, and instead, are highly interrelated from a structural and func-
tional standpoint with any number of other entities making up the broader whole.
As a result, by studying such units in isolation, the likely outcome is that we will:
(1) not fully understand the phenomena of interest that can characterize that
unit; and (2) not understand or measure the important interrelationships and
dependencies between and across units, thus limiting the ability to reassemble
unit-level knowledge into an understanding of the greater system
;
Given emerging evidence that many if not all biological systems behave in a
similar manner when evaluated as networks of interacting entities and processes
(what is referred to in the scientifi c community as “scale free network theory” -
an explained in greater detail by Barabasi and colleagues
[
6
]
), it can be con-
cluded that the most important targets for the disruption or manipulation of
those systems are the nodes or components that are the most highly interrelated
with other nodes or components (i.e., having a high degree of “nodality” or in
more lay-level terms, serving as “hubs” between individual or groups of nodes).
However, by not studying the interrelationships and/or dependencies between
the units that comprise systems of interest, the ability to identify such high-value
targets, which could inform diagnostic and/or therapeutic strategies, is signifi -
cantly diminished
;
and
The building body of knowledge generated by the systems biology and medicine
communities (e.g., scientifi c communities who are applying the preceding
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