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5. Infect bacteria that are not resistant to the antibiotics A and B with the plasmids
obtained at the end of the previous step. The adopted technology (by suitable
electrical impulses) ensures that only one plasmid can enter in one bacterium.
In this operation three possible results can occur for each bacterium: i) one
plasmid including the target molecule enters in the bacterium, ii) one plasmid
without the target molecule enters in the bacterium, iii) no plasmid enters in the
bacterium (the first situation occurs usually only for one in 10,000 bacteria).
6. Put bacteria obtained at the previous step in a microbial culture (each bacterium
is put alone in one compartment) where the antibiotic B is put with the nutrient.
In this way only bacteria including the plasmid can survive.
7. Select the bacteria which survive at the previous step and construct a
mir-
ror bacterial colony
by choosing from each compartment one bacterium and
putting it in a compartment having the same position it has in the original cul-
ture plate.
8. After a growing phase, put the antibiotic A in the mirror colony and observe
in which position bacteria die. These positions are those where in the original
colony there are bacteria hosting the plasmid which includes the target DNA
molecule. In fact in these plasmids the resistance to the antibiotic A was re-
moved. For example, if in the mirror colony bacteria in wells 2
8die,then
we deduce that in the original colony these bacteria include the altered plasmid.
9. In the original colony keep only bacteria which correspond to dead bacteria,
in the mirror colony. They include the plasmids hosting the target molecule.
Remove the external membrane of these bacteria. and recover their plasmids.
10. Put in the obtained pool of plasmids the same restriction enzyme used at
the second step. In this way copies of the target molecules, which were in-
cluded in the plasmids, are recovered and can be selected by length with a
gel-electrophoresis.
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5
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2.4
DNA Computing
In 1994 Leonard Adleman started the new research field of DNA Computing. [11,
12]. He showed that an instance of a famous combinatorial problem can be translated
in terms of DNA strands, put in a test tube in such a way that, by means of typical
laboratory manipulations, a final DNA pool is obtained where the solution of the
problem is encoded. Since then, a great deal of research has been carried out, and
many technical and theoretical achievements have been reached in DNA computing
[39, 17, 18, 75]. Recently, new research perspectives have emerged that widen the
possibilities of this field, among them: DNA self-assembly [61, 55, 58], and DNA
automata [15, 14], as well as tools for DNA and RNA manipulation inspired by
algorithmic analyses. [55, 13].
In the attempt to implement algorithms over a DNA-based “bioware”, along with
the DNA computing trend, it became increasingly apparent that the logic of DNA
operations presents deep combinatorial and algorithmic aspects.
