NANO: EMT: Scalable DNA Molecular Computation

. 根据NSF EMT提案“可扩展的DNA分子计算”，加州大学滨江分校的一个科学家和学生团队将研究基于DNA分子相互作用制造大规模神经网络计算机的可能性。该研究小组包括主要研究者，物理系的艾伦米尔斯教授，化学与环境工程系的杰罗姆舒尔茨教授以及几名研究生和本科生。用几个分子组成的组作为计算元件进行实际计算的可能性是很有趣的，因为人们可能会制造出比今天已知的任何电子计算机都强大得多的分子计算机。这是因为所需大小的DNA分子在体积上比现代计算机芯片上最小的特征小数十亿倍，因此DNA相当于一个强大的微处理器，原则上可以装入一个微小的液滴。人们可以很容易地预测，一台笔记本电脑大小的DNA计算机器可能包含数百万个这样的液滴，其复合计算能力将超过人脑。1994年，在加州大学洛杉矶分校工作的Len Adleman教授戏剧性地证明了某些反应，其中各种DNA分子在溶液中找到，识别并结合到互补的DNA分子，可以用于分子计算的目的。 令人鼓舞的是，Adleman和他的同事们最近使用一个相关的方案解决了一个涉及100万个不同DNA分子的问题，在通往DNA巨大计算潜力的道路上，一个绊脚石是由于不完美的分子反应而导致的计算错误的预期积累。美国国家科学基金会的新提案旨在通过将DNA分子计算机配置为类脑神经网络来实现“可扩展的DNA分子计算”，这种计算架构使用冗余性使其具有固有的容错性，因此可以扩展到几乎任何大小。 第一个实用的神经网络架构，也是测试DNA神经网络的理想架构，是由加州理工学院和贝尔实验室的John Hopfield教授在20世纪80年代早期发明的内容可寻址记忆神经网络。在第一阶段，学生将在导师的指导下学习如何利用活细胞中自然发生的生化反应来制造DNA神经网络的必要组件。 在第二阶段的工作中，将制作一台小型分子计算机来演示DNA神经网络的操作原理。计划将制定扩大这个网络的数量级，以寻找这种类型的分子计算的大小限制。制造尺寸接近人脑的神经网络的前景非常有趣，并提出了一些有趣的问题，如如何编程或“训练”一个非常大的网络，它如何通信，它的运作是否可以自主，它是否可以表现出自我，组织和紧急行为。拟议的工作将证明DNA神经网络是否可以在某些方面与其他技术竞争。壁龛最重要的应用之一可能是使用经过训练的DNA神经网络来识别某些癌细胞类型的DNA特征，并进行廉价的遗传分析测试，以确定人们对一系列最常见疾病的易感性，例如各种癌症，哮喘，糖尿病和血色病，其中可能涉及数十种不同组合的遗传缺陷。因此，即使大型网络被证明是不可行的，相对较小的网络的最终受益者可能是患者，他们将获得基于廉价和快速诊断工具的改进治疗，以及能够根据遗传知识调整生活方式的普通民众。目前，大型DNA神经网络的前景还不确定，但原则上，它们可能是一种全新计算范式的基础。无论如何，拟议的研究将为两名博士生和几名本科生提供良好的培训，在一所以少数民族背景学生占很大比例而闻名的大学里，在新兴的生物物理学领域。

. 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.