416 9.4 Personalizing Healthcare
Thus,
(9.11)
C
P
k
k X
k
k
X
k E
k C
total
=
+
+
−
−
−
5
3
2
2
2
2
6
7
/
At steady state, the dimerization rate satisfies
(9.12)
k
X
k C
−
−
=
4
2
4
2
0
Thus,
(9.13)
C
P
k
k
k
C
k
k
k
k
C
k E
k C
total
=
(
)
(
)+(
)
−
−
−
−
−
3
4
4
2
2
2
4
4
2
6
7
/
/
/
The first term is the production rate, and the shape of this versus C is sigmoidal.
This means that the rate switches rapidly from low to high values over a relatively
short range of C. This indicates a binary switching function whose output is high
or low depending on the level of C. However, there is a finite rate of degradation,
which increases with C; thus, the high state of the binary switch is only transient,
so in effect the biological circuit serves as a digital pulse generator controlled by
the specific concentration level of the protein X.
9.4 PERSONALIZING HEALTHCARE
Personalized healthcare is a medical model that proposes to cater healthcare specifically to
a unique, individual patient, as opposed to relying on generic treatments that are relevant to
population-level information. For example, we know that human genomes in general vary
significantly from one individual to the next (Chapter 2). Some people have a greater genetic
predisposition toward certain disorders and diseases than others. Also, the responses of indi
vidual patients to different treatments can vary widely, which potentially affects the outcome
of particular medical treatments.
The selection of appropriate, optimal therapies is based on specific information concerning
a patient’s genetic, molecular, and cellular makeup. In terms of biophysics, this has involved
developments in smart diagnostics such as lab-on-a-chip technologies, and smart, targeted
treatment, and cellular delivery methods, including nanomedicine. In addition, computa
tional modeling can be combined with experimental biophysics for more intelligent in silico
drug design catered toward specific individuals.
Developing methods to enable personalized healthcare is particularly important regarding
the current global increased risks of infection, and the challenges of an increasingly aging
population. For infection challenges, the past overuse of antibiotics has led to the emergence
of superbugs, such as methicillin or vancomycin resistant Staphylococcus aureus (MRSA and
VRSA respectively), which are resistant to many of the traditional antibiotics available. These
now impose significant limitations on the successful outcomes of many surgical treatments,
cancer therapies and organ transplants; personalized diagnostic biosensing to detect the suite
of different infectious pathogens present in different parts of the body in specific individ
uals could be invaluable in developing catered drug treatments to combat these. For aging
issues, the big challenges are heart disease, cancer, and dementia. Again, all these disorders
are amenable to personalized biosensing—innovative, fast-response technologies which
can utilize appropriate libraries of biomarkers to personalize earlier diagnosis and thereby