s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We introduce a model of computation that is evidently programmable, by programs reminiscent of low-level computer machine code; and at the same time biologically plausible: its functioning is defined by a single and relatively small set of chemical-like reaction rules. Further properties: the model is stored-program: programs are the same as data, so programs are not only executable, but are also compilable and interpretable. It is universal: all computable functions can be computed (in natural ways and without arcane encodings of data and algorithm); it is also uniform: new “hardware ” is not needed to solve new problems; and (last but not least) it is Turing complete in a strong sense: a universal algorithm exists, that is able to execute any program, and is not asymptotically inefficient. A prototype model has been implemented (for now in silico on a conventional computer). This work opens new perspectives on just how computation may be specified at the biological level. Keywords: biomolecular, computation, programmability, universality.

We study the computational power of systems where information is stored in independent strings and each computational step consists of exchanging information between randomly chosen pairs. The input for the system is environment which selects certain strings. To this end we introduce a population genetics model in which the operators of selection and inheritance are effectively computable (in polynomial time on probabilistic Turing machines). We show that such systems are as powerful as the usual models of parallel computations, namely they can simulate polynomial space computations in polynomially many steps. We also show that the model has the same power if the recombination rules for strings are very simple (context sensitive crossing over), which suggests that similar processes might be exploited by real organisms. 1