Towards a Robust Biocomputing Solution of Intractable Problems

An incremental approach to construction of biomolecular algorithms solving intractable problems is presented. The core idea is to build gradually the space of candidate solutions and remove invalid solutions as soon as possible. We demonstrate two examples of this strategy: a P system with replication and inhibitors for solving the Maximum Clique Problem for a graph, and an incremental DNA algorithm for the same problem inspired by the membrane solution. The DNA implementation is based on the parallel filtering DNA model featuring error-resistance of the employed operations. The algorithm is compared with two standard papers that addressed the same problem and its DNA implementation in the past. The comparison is carried out on the basis of a series of computational and physical parameters. The incremental algorithm features a dramatically lower cost in terms of time, the number and size of DNA strands, together with a high error-resistance. A probabilistic analysis shows that physical parameters (volume of the DNA pool, concentration of the solution-encoding strands) and error-resistance of the algorithm should allow to process in vitro instances of graphs with hundreds to thousands of vertices.

[1]  Mihai Ionescu,et al.  On P Systems with Promoters/Inhibitors , 2004, J. Univers. Comput. Sci..

[2]  Martyn Amos,et al.  Theoretical and Experimental DNA Computation (Natural Computing Series) , 2005 .

[3]  Mitsunori Ogihara Breadth First Search 3SAT Algorithms for DNA Computers , 1996 .

[4]  Thomas Bäck,et al.  Evolutionary Computation as a Paradigm for DNA-Based Computing , 2002 .

[5]  Svante Janson,et al.  Random graphs , 2000, ZOR Methods Model. Oper. Res..

[6]  T. Head,et al.  Aqueous computing: writing on molecules , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[8]  Grzegorz Rozenberg,et al.  Introduction: DNA Computing in a Nutshell , 1998 .

[9]  Svante Janson,et al.  Random graphs , 2000, Wiley-Interscience series in discrete mathematics and optimization.

[10]  Gheorghe Paun,et al.  Computing with Membranes , 2000, J. Comput. Syst. Sci..

[11]  Vincenzo Manca,et al.  A Clause String DNA Algorithm for SAT , 2001, DNA.

[12]  Martyn Amos,et al.  Theoretical and Experimental DNA Computation , 1999, Bull. EATCS.

[13]  Eric Bach,et al.  DNA models and algorithms for NP-complete problems , 1996, Proceedings of Computational Complexity (Formerly Structure in Complexity Theory).

[14]  Carlos Martín-Vide,et al.  Membrane systems with promoters/inhibitors , 2002, Acta Informatica.

[15]  S. Ross A random graph , 1981 .

[16]  D T Chiu,et al.  Using three-dimensional microfluidic networks for solving computationally hard problems , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Clifford R. Johnson,et al.  Solution of a 20-Variable 3-SAT Problem on a DNA Computer , 2002, Science.

[18]  P D Kaplan,et al.  DNA solution of the maximal clique problem. , 1997, Science.

[19]  Karl-Heinz Zimmermann,et al.  Efficient DNA sticker algorithms for NP-complete graph problems , 2002 .

[20]  Dan Boneh,et al.  Running dynamic programming algorithms on a DNA computer , 1996, DNA Based Computers.

[21]  Raghavan Rama,et al.  P Systems with Replicated Rewriting , 2001, J. Autom. Lang. Comb..

[22]  Martyn Amos,et al.  Error-resistant implementation of DNA computations , 1996, DNA Based Computers.

[23]  榊原 康文,et al.  G. Paun, G. Rozenberg and A. Salomaa : "DNA Computing-New Computing Paradigms", Springer-Verlag (1998) , 2000 .

[24]  L F Landweber,et al.  Chess games: a model for RNA based computation. , 1999, Bio Systems.

[25]  Gheorghe Paun,et al.  Membrane Computing , 2002, Natural Computing Series.

[26]  David I. Lewin,et al.  DNA computing , 2002, Comput. Sci. Eng..

[27]  J S McCaskill Optically programming DNA computing in microflow reactors. , 2001, Bio Systems.