Biomedical Engineering Reference
In-Depth Information
here. This complex binds to the hairpin when it
recognizes the yes 1 toehold. The enzyme then
cuts the sequence at the right site to make the next
state yes represented by the yes 2 toehold. If RNA
2 is present, a cutting enzyme linked to a yes-yes
transition molecule with the yes 2' toehold appears.
This binds to and cuts the hairpin to expose yes
3 . If RNA 3 is present, the process is repeated
again and the hairpin is left with an exposed
yes release toehold. The computation ends with
the yes state. In this case, an enzyme linked to a
release transition molecule with the yes release'
toehold appears. This complex recognizes and
binds to the hairpin's yes release toehold. It cuts
the hairpin and releases the drug. Finally, this drug
specifically binds to messenger RNA causing the
disease and prevents its transcription.
If any of the three RNA molecules indicating
the disease are not present, a cutting enzyme
linked to the yes-no transition molecule appears.
This complex cuts the hairpin and exposes the
no toehold. There is only one enzyme, linked to
a no-no transition molecule, that recognizes this
no toehold. This complex is always present and
cuts the hairpin so that the next toehold is still
no . It goes on to act like another no-no transition
complex, and so on, until the hairpin exposes
the no release toehold. The computation ends
with the no state. The no release toehold is not
associated with any transition molecule and the
drug is not released.
The experiments measure the concentration
levels of RNA indicators of disease rather than
using individual molecules. If the concentration
of all these RNA molecules is high, a high con-
centration of hairpins switching from one yes
state to another is formed. This releases a high
concentration of the drug. At the other end of
the scale, a high concentration of hairpins that
end in the no state and do not release the drug
is formed.
This biomolecular automaton behaves like a
biosensor that also measures concentrations, emits
a response depending on the measurement taken.
Finally, note that, in the experiments run biomo-
lecular automata like this, no outside energy was
consumed during computation (Shapiro, 2006).
FUTURE TRENDS
Possible short-term goals of the devices created
by synthetic biology and biomolecular computa-
tion are:
Diagnose and treat diseases in vivo by means
of biomolecular information-processing
devices and nanodevices.
Assemble genetic circuits to produce de-
vices performing more complex functions
inspired by computer engineering.
Design and construct biomolecular systems
exclusively to get a better understanding
of cellular processes and to verify systems
biology models.
Design and construct new devices that
merge conventional electronics with the new
biomolecular devices to lay the foundation
of a new discipline: bionanoelectronics.
Working together with other disciplines, like
systems biology, the key long-term goals are:
Develop comprehensive models for indi-
vidual cells, multicellular systems, tissues,
organs and, finally, organisms.
Design and construct devices able to oversee
the operation of human tissues and take ac-
tion as necessary.
2020 Science is an interesting web site about
“the evolution, challenges and potential of
computer science and computing in scientific
research in the next fifteen years” (http://research.
microsoft.com/towards2020science). Some of the
milestones included are:
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