Biology Reference
In-Depth Information
P4
Medicine
P
redictive
P
reventive
P
ersonalized
P
articipatory
Wellness Quantified
Disease Demystified
FIGURE 23.2
A schematic representation of the two major objectives of P4 medicine: quantizing wellness and demystifying disease.
SYSTEMS MEDICINE
We predict that within 10 years every healthcare consumer
will be surrounded by a virtual cloud of billions of data
points (
Figure 23.3
). These will range from molecular and
cellular data, to conventional medical data, to enormous
amounts of imaging, demographic and environmental
data. Big data sets are essential to decipher 'signal' from
the noise generated by the complexities of disease and
wellness. As noted above, the complexity of biological
systems arises from the random and chaotic processes of
Darwinian evolution. The principal data analyzed by
systems medicine relate to the following central concerns
about human biological systems: the identification all of
the system components, establishing their interactions,
assessing the dynamics of those interactions
of biological networks. These disease-perturbed networks
all express altered information that changes dynamically
across time (and often space) and arises from the pertur-
bations. The amalgamation of this distorted information
explains the pathophysiology of the disease, as discussed
below in the context of a specific systems-disease example,
neurodegeneration.
The above description is not intended to suggest that
disease-perturbed networks are of a single type: there is
a 'network of networks' that reflects themulti-dimensionality
of both biological processes and disease processes
(
Figure 23.4
). Thus we can describe genetic networks arising
from the genome; molecular networks arising from protein
and DNA interactions; cellular networks arising from cell
interactions; organ networks arising from organ
both
organ
interactions; and finally, social networks arising largely in
part from the nature of an individual's environmental inter-
actions
[2,17,21]
. The integration of all of these networks is
necessary to understand their functioning in the context of the
individual.
The ascertainment of these networks requires enormous
amounts of data. For some of these measurements, the tools
are just now being developed. Big data sets pose two
e
e
temporal and spatial
and then attempting to understand
how the system as a whole executes its biological func-
tions and exhibits specific phenotypes.
A systems approach to disease takes into account the
myriad social and environmental factors that compound the
innate complexity of human biology and which are crucial
determinants of health. It does so by treating disease as a
consequence of genetic and/or environmental perturbations
e
