Theoretical and Experimental DNA Computation (Natural ...

4 Introduction
The initial rush of publications following Adleman’s original paper was
dominated mainly by theoretical results, but empirical results soon followed.
In Chap. 5 we describe several laboratory implementations of algorithms
within models mentioned in the previous paragraph. Most of the significant
results are described in the abstract, in order to capture the essence of the
experimental approach. In order to also highlight the factors to be considered
when designing a protocol to implement a molecular algorithm, particular attention
is given to the details of the experiments carried out by the author’s
collaborators.
With recent advances in biology, it is becoming clear that a genome (or
complete genetic sequence of an organism) is not, as is commonly (and erroneously)
suggested, a “blueprint” describing both components and their
placement, but rather a “parts list” of proteins that interact in an incredibly
complex fashion to build an organism. The focus of biology has turned toward
reverse-engineering sequences of interactions in order to understand the
fundamental processes that lead to life. It is clear that, in the abstract, a lot
of these processes may be thought of in computational terms (for example,
one biological component may act, for all intents and purposes, as a switch,
or two components may combine to simulate the behavior of a logic gate).
This realization has stimulated interest in the study of biological systems
from a computational perspective. One possible approach to this is to build a
computational model that captures (and, ultimately, predicts) the sequence of
biological operations within an organism. Another approach is to view specific
biological systems (such as bacteria) as reprogrammable biological computing
devices. By taking well-understood genetic components of a system and
reengineering them, it is possible to modify organisms such that their behavior
corresponds to the implementation of some human-defined computation.
Both approaches are described in Chap. 6.
In less than ten years, the field of DNA computation has made huge advances.
Developments in biotechnology have facilitated (and, in some cases,
been motivated by) the search for molecular algorithms. This has sometimes
led to unfortunate speculation that DNA-based computers may, one day, supplant
their silicon counterparts. It seems clear, however, that this unrealistic
vision may one day be replaced by a scenario in which both traditional and
biological computers coexist, each occupying various niches of applicability.
Whatever the ultimate applications of biological computers may turn out to
be, they are revolutionizing the interactions between biology, computer science,
mathematics, and engineering. A new field has emerged to investigate
the crossover between computation and biology, and this volume describes
only its beginnings.