Automating the DNA Computer: Solving n-Variable 3-SAT Problems

  • Clifford R. Johnson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4287)


In the decade since the first molecular computation was performed, it has been shown that DNA molecules can perform sophisticated, massively parallel computations avoiding the Von Neumann bottleneck. However, progress in the field has been slow. The largest problem solved to date is an instance of the 20-variable 3-CNF SAT problem. Performing the computation took more than two man-weeks to complete because every aspect of the computation was performed by hand. Molecular computations are extremely labor intensive and error prone–automation is necessary for further progress.

The next step, (the second generation DNA computer – that of taking the laborious, laboratory bench protocols performed by hand, and automating them), has been achieved with the construction of an automated DNA computer dubbed EDNAC. It employs the same paradigm that was used to solve the labor-intensive instance of the 20-variable 3-CNF SAT problem. Using a combinatorial DNA library and complementary probes, EDNAC solves instances of the n-variable 3-CNF SAT problem. A 10 variable instance of the 3-CNF SAT problem was essayed. The computation took 28 hours to perform. EDNAC correctly computed nine of the ten variables, with a tenth variable remaining ambiguous. This result is comparable to current results in the molecular computation community. This research tested the critical properties, such as complexity, robustness, reliability, and repeatability necessary for the successful automation of a molecular computer.


Computation Module Release Module Cool Unit Molecular Computation Motion Control System 
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  1. 1.
    Adleman, L.: Molecular computation of solutions to combinatorial problems. Science 266, 1021–1024 (1994)CrossRefGoogle Scholar
  2. 2.
    Braich, R., Chelyapov, N., Johnson, C., Rothemund, P., Adleman, L.: Solution of a 20-Variable 3-SAT Problem on a DNA Computer. Science 296, 499–502 (2002)CrossRefGoogle Scholar
  3. 3.
    Braich, R., Johnson, C., Rothemund, P.W.K., Hwang, D., Chelyapov, N., Adleman, L.: Satisfiability Problem on a Gel Based DNA Computer. In: DNA Computing – DNA 6, vol. 2054, Springer, New York (2000)Google Scholar
  4. 4.
    Reif, J.H.: Computing. Success and challenges. Science 268, 478–479 (2002)CrossRefGoogle Scholar
  5. 5.
    Lipton, R.J.: DNA solution of hard computational problems. Science 268, 542–545 (1995)CrossRefGoogle Scholar
  6. 6.
    Olsen, K., Ross, D., Tarlov, M.: Immobilization of DNA Hydrogel Plugs in Microfluidic Channels. Anal. Chem. 74, 1436–1441 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Clifford R. Johnson
    • 1
  1. 1.Dept. of ChemistryNew York University 

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