Zhang Mingjun, Cheng Maggie X, Tarn Tzyh-Jong
Life Sciences and Chemical Analysis Division, Agilent Technologies, Santa Clara, CA 95051, USA.
IEEE Trans Nanobioscience. 2006 Mar;5(1):32-40. doi: 10.1109/tnb.2005.864017.
DNA computation is to use DNA molecules for information storing and processing. The task is accomplished by encoding and interpreting DNA molecules in suspended solutions before and after the complementary binding reactions. DNA computation is attractive, due to its fast parallel information processing, remarkable energy efficiency, and high storing capacity. Challenges currently faced by DNA computation are: 1) lack of theoretical computational models for applications and 2) high error rate for implementation. This paper attempts to address these problems from mathematical modeling and genetic coding aspects. The first part of this paper presents a mathematical formulation of DNA computation. The model may serve as a theoretical framework for DNA computation. In the second part, a genetic code based DNA computation approach is presented to reduce error rate for implementation, which has been a major concern for DNA computation. The method provides a promising alternative to reduce error rate for DNA computation.
DNA计算是利用DNA分子进行信息存储和处理。该任务通过在互补结合反应前后对悬浮溶液中的DNA分子进行编码和解码来完成。DNA计算具有吸引力，因为它具有快速并行信息处理能力、显著的能源效率和高存储容量。目前DNA计算面临的挑战是：1）缺乏适用于应用的理论计算模型；2）实施过程中的错误率高。本文试图从数学建模和遗传编码方面解决这些问题。本文的第一部分给出了DNA计算的数学公式。该模型可作为DNA计算的理论框架。在第二部分中，提出了一种基于遗传密码的DNA计算方法，以降低实施过程中的错误率，这一直是DNA计算的一个主要问题。该方法为降低DNA计算的错误率提供了一种有前景的替代方案。
