Abstract: In DNA computing, one requirement of the encoding DNA sequences is that they should keep similar thermodynamic stability in order to maintain the reliability of the computing process. Melting temperature is a suitable parameter used to evaluate the thermodynamic stability of DNA sequences. As traditional predicting methods used in biological engineering may exist lager error for a few sequences, thus it couldn’t meet situation involved large amount of DNA sequences as DNA computing. In this paper, we introduced a BP Neural Network to predict the melting temperature based on the Nearest-Neighbor parameters. Our result shows that the predicting error can be limited within ±5.5℃.

摘要： 在DNA计算中，为了确保计算过程的可靠性，要求编码信息的DNA序列必须具有相似的热力学稳定性。解链温度是目前评价DNA序列热力学稳定性的一个主要的参数，目前，生物工程中常用的各种预测方法都存在某些序列的误差偏大的缺点，因此难以满足像DNA计算这种大量DNA序列进行各种生化反应的计算过程的要求。本文以DNA序列的邻近法参数为基础，建立了一个基BP神经网络的解链温度的预测模型。计算结果表明， DNA序列的解链温度的误差可以达到±5.5℃的范围。