Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published November 17, 2005 | Submitted
Journal Article Open

The computational power of Benenson automata

Abstract

The development of autonomous molecular computers capable of making independent decisions in vivo regarding local drug administration may revolutionize medical science. Recently Benenson et al. [An autonomous molecular computer for logical control of gene expression, Nature 429 (2004) 423–429.] have envisioned one form such a "smart drug" may take by implementing an in vitro scheme, in which a long DNA state molecule is cut repeatedly by a restriction enzyme in a manner dependent upon the presence of particular short DNA "rule molecules." To analyze the potential of their scheme in terms of the kinds of computations it can perform, we study an abstraction assuming that a certain class of restriction enzymes is available and reactions occur without error. We also discuss how our molecular algorithms could perform with known restriction enzymes. By exhibiting a way to simulate arbitrary circuits, we show that these "Benenson automata" are capable of computing arbitrary Boolean functions. Further, we show that they are able to compute efficiently exactly those functions computable by log-depth circuits. Computationally, we formalize a new variant of limited width branching programs with a molecular implementation.

Additional Information

© 2005 Elsevier B.V. Received 21 December 2004; revised 29 July 2005; Accepted 29 July 2005. Communicated by T. Yokomori. Available online 24 August 2005. We thank Georg Seelig for first bringing Benenson et al.'s work to our attention. Further, we thank two anonymous reviewers for very detailed reading of this paper and useful suggestions. This research was supported by NIH training Grant MH19138-15.

Attached Files

Submitted - benenson_preprint.pdf

Files

benenson_preprint.pdf
Files (206.1 kB)
Name Size Download all
md5:cf7e390ae80aa86b06e05444fc23955e
206.1 kB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 23, 2023