Previous Article |
Table of Contents
| Next Article
Departments of * Chemistry & Biochemistry and
In 1994 Leonard Adleman used DNA
strands to encode cities and flights to show that itineraries
satisfying special conditions could be constructed and isolated in the
laboratory (1). The abstract of this seminal paper concludes, "This
experiment demonstrates the feasibility of computation at the molecular
level." Within months Richard Lipton argued (2) that fundamental
problems concerning the truth of logic statements also could be
addressed in this new way. In this issue of PNAS, Faulhammer et
al. (3) use laboratory techniques with relatively low error rates
in an experiment realizing Lipton's earlier design.
However, within a year after Adleman's article, it already was known
such approaches can require unrealistic quantities of materials,
leading Stemmer (4) to suggest using existing laboratory procedures to
evolve realistically sized molecular populations by inducing variation
and selection. Such evolutionary computation is an established paradigm
for conventional computers. Interest is beginning to focus on DNA-based
implementations (5-7) of evolutionary computation because of its
alleged robustness in the presence of errors and its ability to exploit
the massive parallelism and memory inherent at the molecular level.
By its success in the laboratory, Adleman's work (1) sparked intense
excitement and marked the birth of a new field, DNA computation.
Adleman's insight was that the ability of single-stranded DNA to seek
and bind to a complementary strand allows DNA to carry out massively
parallel computation. Although it seems doubtful that biologically
based computers will be suitable for general-purpose applications,
there are some especially difficult problems where conventional
computers lack the massive parallelism and huge memory capacity
inherent in molecular computation. For example, the so-called "NP-complete" problems that apparently require exponentially
increasing computing time with a linear increase in problem size are
notoriously difficult for silicon computers to solve.
Commentary
Computation with biomolecules
and
Computer & Information Sciences, University of Delaware,
Newark, DE 19716
![]()
Article
![]()
CiteULike
Complore
Connotea
Del.icio.us
Digg What's this?
Companion article to this Commentary:
This article has been cited by other articles in HighWire Press-hosted journals:
![]() |
K. A. Schmidt, C. V. Henkel, G. Rozenberg, and H. P. Spaink DNA computing using single-molecule hybridization detection Nucleic Acids Res., September 23, 2004; 32(17): 4962 - 4968. [Abstract] [Full Text] [PDF] |
||||
![]() |
J.-M. Lehn Supramolecular Chemistry And Self-assembly Special Feature: Toward complex matter: Supramolecular chemistry and self-organization PNAS, April 16, 2002; 99(8): 4763 - 4768. [Full Text] [PDF] |
||||
![]() |
J.-M. Lehn Toward Self-Organization and Complex Matter Science, March 29, 2002; 295(5564): 2400 - 2403. [Abstract] [Full Text] [PDF] |
||||
![]() |
J.-M. Lehn Supramolecular Chemistry And Self-assembly Special Feature: Toward complex matter: Supramolecular chemistry and self-organization PNAS, April 16, 2002; 99(8): 4763 - 4768. [Full Text] [PDF] |
||||