لطفا منتظر باشید ...
Introduction—What Is Molecular Computing?
chapter 4 of this volume). Also, systems that opt for efficiency and evolutionary adaptability would be better suited to coupling increases in computation resources to increases in problem size.
The biologically motivated molecular computer engineer is not trying to solve the origin-of-life problem or to create "living computers". The more-than-sufficient objective is to exploit the characteristic properties of biological macromolecules to produce devices that perform useful information processing functions.
Looking at how biological organisms process information, one is struck by certain aspects:
The ubiquitousness of proteins and a "two-step process" of transcription
A very high degree of parallelism
A high degree of complexity
Ignoring for the present the question as to whether proteins are the ultimate optimal mechanism or whether nature (and evolution) simply used what was available, it should be pointed out that a very important aspect is the dependence of biological systems for their "information processing" capabilities on what is known as molecular recognition. Molecules bind together weakly with other molecules—not as tightly as one finds in normal covalent bonding, but not so weakly that discrimination cannot be made between different molecules. This recognition is, at base, a quantum effect and is one of the mechanisms by which parallelism is introduced into the system.
Assume a potential surface with many valleys, one of which corresponds to the desired solution of a problem (i.e., the lowest energy state). In the absence of external perturbation (e.g., thermal agitation), a classical system would never make the transition from an incorrect potential well to the correct one. A microscopic (quantum) system, with particles such as electrons, would inevitably find the proper well by virtue of barrier penetration. But to actually exploit this for problem solving, it is necessary to "put a handle on the electron", or in more technical terms, embed the microsystem in a macroscopic architecture where the output state is obvious. Macromolecules—such as proteins—are an intermediate-size architecture that is too large to undergo barrier penetration per se, but its pattern-matching motions may in large part be controlled by the electronic wave function. When a protein is "recognized" by an enzyme, we are seeing the results of a many-valued parallel exploration of phase space made possible by quantum mechanics and carried up to the macroscale. Equivalently, the difficult problem of pattern recognition has been turned into chapter 1) is by Michael Conrad and Klaus-Peter Zauner. Their chapter covers and expands on several of the issues mentioned above, analyzing the uses of proteins and other such molecules from an information processing point of view. Included are analyses of relevant molecular properties and the necessity of the macro-micro-interface. The authors then move into a description of a prototype system, complete with a recipe for how to build it. Finally, chapter 1 ends with descriptions of experimental systems and what has been accomplished so far.
Chapter 2 is by Jean-Marie Lehn and Tanya Sienko. As has already been mentioned, molecular recognition is what underlies many of these biological systems, running from the "information processing" aspects of enzymes to the aspects of selfrecognition, self-assembly, and self-organizing systems. After a short explanation of what molecular recognition is and how to design systems to do it well, the authors work from the simplest types of self-recognition up to the most complex, along with a few comments on information transfer and how concepts of molecular recognition may be used.
The next two chapters, chapters 3 and 4, can be said to constitute the reactiondiffusion part of the book. The chemical components of the media themselves are not all that complex, yet the resultant activity can be extremely complex and lend itself to parallel information processing of certain tasks. Andrew Adamatzky (chapter 3) has contributed a chapter on the theory of computation in nonlinear media. The first part covers a theory of excitable and diffusive processors. Adamatzky then moves to an explanation of and samples of such systems with specialized processors. The chapter ends with a long section on universal processors, with a detailed explanation of the example of collision gates in DNA and monomolecular arrays. Chapter 4, by Nicholas Rambidi, goes further, with descriptions of physical reaction-diffusion systems found in chemical media, the theory of how to build a computer based on such, along with a large number of experimental examples.
At this point, we move back to more biologically oriented systems, and specifically to DNA computing. Carlo Maley (chapter 5) has kindly contributed a review chapter covering present work to date as well as comments on where the field will probably advance in the future, including all the pros and cons of the field.
Chapter 6 has been contributed by Duane Marcy, Bryan Vought, and Robert Birge, and covers bioelectronics and protein-based optical computing. The main focus of this chapter is on the possibilities inherent in biorhodopsin, as well as comments on how optical/biological computing would differ from semiconductor-based systems.
Finally, continuing our trend, we end up with a chapter on the present status of biosensors, contributed by Satoshi Sasaki and Isao Karube (chapter 7). Karube almost single-handedly invented the field many years ago, fusing electronics and micromachines together with biological films or organisms whose reaction (at the molecular level) could be read out (i.e., used) to provide a trigger signal to the micromachine/electronics. Japan remains the country most advanced in this field. We hope our readers will enjoy this review article, which covers a wide range of biosensors and what they can do.
Some final comments should be made about what we have not included in this book: We have not included articles on quantum computing because we feel this discipline lies outside the scope of what we wish to address. Nor, except in a tangential way, have we touched on the field of molecular electronics, where the attempt has been to shrink circuits down even further, to the level of say, a nanotube or a molecular wire. We have also stayed away from neural networks, feeling that there are sufficient books extant to satisfy any seeker of knowledge in that area.
What we have tried to do is sketch out, even if only lightly, certain areas and topics that have remained obscure to most computer scientists and that we feel have great potential for the future. Molecular computing and the non–von Neumann paradigms for information processing remain a vast area in which only a few explorers have left their footprints. We hope that this book is only the first of many guides into this new field.
References
Bennett, C. H. 1973. Logical reversibility of computation. IBM J. Res. Dev. 17: 525–532.
Conrad, M. 1990. Molecular Computing, issue of Advances in Computers, Vol. 31. New York: Academic Press.
Conrad, M., and D. Conrad. 1997. Of maps and territories: A three point landing on the mind-body problem. In Matter Matters, ed. P. Arhem, H. Liljenstrom, and U. Svedin, 107–137. New York: Springer-Verlag.
Landauer, R. 1982. Uncertainty principle and minimal energy dissipation in the computer. Int. J. Theor. Phys. 21: 283–297.
