Artificial ants deposit pheromone to search for regulatory DNA elements.
Artificial ants deposit pheromone to search for regulatory DNA elements.
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DOI：
10.1186/1471-2164-7-221
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发表时间：
2006-08-30
期刊：
影响因子：
4.4
通讯作者：
Yokota, Hiroki
中科院分区：
文献类型：
--
作者：
Liu, Yunlong;Yokota, Hiroki
转录因子结合基序（DNA序列）的识别可以被公式化为一个组合问题，其中一个有效的算法是必不可少的预测多个结合基序的作用。蚂蚁算法是一种受生物学启发的计算技术，通过模仿蚂蚁等社会昆虫的行为来解决组合问题。我们开发了一个独特的版本的蚂蚁算法来选择一组结合基序，通过考虑每个4- 7-bp长度的随机DNA序列的潜在贡献。人软骨形成被用作模型系统。结果表明，蚂蚁算法能够识别生物学上已知的结合基序，如AP-1，NFκB和sox 9。一些预测的图案是相同的那些先前与遗传算法。然而，与遗传算法不同的是，蚂蚁算法能够评估单个结合基序作为一系列分布式信息的贡献，并从更广泛的DNA库中预测核心共识基序。蚂蚁算法提供了一个有效的，可重复的程序来预测的作用，个人的转录因子结合基序使用一个独特的定义人工蚂蚁。
Identification of transcription-factor binding motifs (DNA sequences) can be formulated as a combinatorial problem, where an efficient algorithm is indispensable to predict the role of multiple binding motifs. An ant algorithm is a biology-inspired computational technique, through which a combinatorial problem is solved by mimicking the behavior of social insects such as ants. We developed a unique version of ant algorithms to select a set of binding motifs by considering a potential contribution of each of all random DNA sequences of 4- to 7-bp in length. Human chondrogenesis was used as a model system. The results revealed that the ant algorithm was able to identify biologically known binding motifs in chondrogenesis such as AP-1, NFκB, and sox9. Some of the predicted motifs were identical to those previously derived with the genetic algorithm. Unlike the genetic algorithm, however, the ant algorithm was able to evaluate a contribution of individual binding motifs as a spectrum of distributed information and predict core consensus motifs from a wider DNA pool. The ant algorithm offers an efficient, reproducible procedure to predict a role of individual transcription-factor binding motifs using a unique definition of artificial ants.
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