. Under NSF EMT Proposal "Scalable DNA molecular computation" a team of scientists and students at the University of California, Riverside will investigate the possibility of making a large-scale neural network computer based on the interactions of DNA molecules. The research team includes the principle investigator, Professor Allen Mills from the Department of Physics, Professor Jerome Schultz from the Department of Chemical and Environmental Engineering and several graduate and undergraduate students. The possibility of doing practical computations using groups of a few molecules as computing elements is intriguing because one might be able to make molecular computers much more powerful than any electronic computer known today. This is because DNA molecules of the required size are billions of times smaller in volume than the smallest feature on a modern computer chip, so that the DNA equivalent of a powerful microprocessor could in principle fit into a tiny droplet of liquid. One can easily project that a DNA computing machine with the physical size of a laptop could contain millions of such droplets that would have a composite computing power exceeding that of a human brain. The first indication that computing could be done with DNA molecules came in 1994 when Prof. Len Adleman, working at UCLA, dramatically demonstrated that certain reactions, in which various DNA molecules in solution find, recognize and bind to complementary DNA molecules, may be utilized for the purpose of doing computations with molecules. It is encouraging that Adleman and his coworkers have recently used a related scheme to solve a problem involving one million different DNA molecules. One stumbling block on the path to the enormous computing potential of DNA is the anticipated accumulation of errors in a computation due to imperfect molecular reactions. The new NSF proposal seeks to achieve "Scalable DNA molecular computation" by configuring a DNA molecular computer as a brain-like neural network, a computing architecture that uses redundancy to make it inherently fault tolerant and therefore possibly scalable to virtually any size. The first practical neural network architecture, and one that is perhaps ideal for testing a DNA neural network, is the content-addressable memory neural network invented by Prof. John Hopfield at Cal Tech and Bell Labs in the early 1980's. In the first phase of the proposal, the students under the guidance of their mentors will learn how to use biochemical reactions naturally occurring in living cells to make the necessary components for a DNA neural network. In the second phase of the work, a small-scale molecular computer will be made to demonstrate the principle of operation of a DNA neural network. Plans will be drawn up for scaling this network up by an order of magnitude to search for size limitations in this type of molecular computing. The prospect of making neural networks with a size approaching that of a human brain is very intriguing and raises interesting questions about how one would program or "train" a very large network, how it would communicate, whether its operation could be autonomous and whether it could exhibit self-organization and emergent behavior. The proposed work will demonstrate whether DNA neural networks could be competitive with other technologies in certain niches. One of the most important applications could be the use of DNA neural networks trained to recognize the DNA signatures of certain cancer cell types and for performing inexpensive genetic profiling tests to determine the susceptibility of people to a spectrum of the most common diseases such as various cancers, asthma, diabetes and hemochromatosis where dozens of genetic defects in different combinations may be involved. Thus even if giant networks prove to be infeasible, the eventual beneficiaries of relatively small networks may be patients that would receive improved therapy based on the availability of an inexpensive and rapid diagnostic tool, and the general populace who will be able to adjust their lifestyles based on genetic knowledge. The prospects for large DNA neural networks are uncertain at this time, but in principle they could be the basis for an entirely new computing paradigm. In any case, the proposed research will provide excellent training for two PhD students and several undergraduate students in the emerging field of biophysics at a University noted for its large component of students from minority backgrounds.
