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Theoretical and Experimental DNA Computation (Natural ...

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5.8 <strong>Experimental</strong> Investigations 133<br />

Experiment 21. Evidence of exclusion was seen again, but in all cases it was<br />

incomplete.<br />

Experiment 22. There was evidence of specific exclusion (the intensity of<br />

targeted sequences reduced) but the process was incomplete. There seemed<br />

to be a basic problem with the method in that it used the enzymatic removal<br />

process to target <strong>and</strong> destroy specific sequences, followed by an incredibly<br />

sensitive technique to detect them.<br />

Experiment 32. The PCR failed to produce any product from any of the<br />

samples, including the positive control. This was probably due to loss of the<br />

template during the washing steps, reducing its concentration below the limit<br />

of detection.<br />

Discussion<br />

It is perhaps useful at this point to note that this implementation, first proposed<br />

in [12], established for the first time the “destructive” <strong>DNA</strong>-based algorithmic<br />

paradigm, which has been subsequently used in several groundbreaking<br />

papers [58, 99].<br />

In [141] Seeman et al. describe the potential pitfalls that may confront<br />

experimentalists working on <strong>DNA</strong> computation. In this section we describe<br />

in a similar fashion the lessons to be drawn from the experimental investigations<br />

just described. We hope that other experimentalists in the field may be<br />

made aware of various subtle aspects of the implementation of models of <strong>DNA</strong><br />

computation. We have found that the requirements of <strong>DNA</strong>-based algorithmic<br />

experiments are often more strict than those of “traditional” investigations<br />

in molecular biology. For example, it is rare that molecular biologists are<br />

required to sequence a heterogeneous population of <strong>DNA</strong> str<strong>and</strong>s; yet, for<br />

any nontrivial problem, this task is inevitably required as the final step of<br />

the implementation of a <strong>DNA</strong>-based algorithm. We hope that these (often<br />

non-obvious) impediments to efficient <strong>and</strong> error-resistant implementation of<br />

models of <strong>DNA</strong> computation will be made apparent in the following sections.<br />

Ensure appropriate control <strong>and</strong> optimization experiments are<br />

performed<br />

We quickly found that a major component of the work was comprised of<br />

finding optimal experimental conditions. Factors to be taken into account<br />

included str<strong>and</strong> concentration, salt concentration, restriction enzyme concentration,<br />

annealing temperature, <strong>and</strong> number of cycles. Due to the unusual<br />

nature of the experiments, we found that the system was far more sensitive<br />

to experimental conditions than is normally the case.

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