iGEM人工合成DNA序列的层次结构进化与重复使用
Evolution of Hierarchical Structure & Reuse in iGEM Synthetic DNA Sequences
摘要：
许多复杂的系统，无论是在技术上还是在自然界中，都表现出层次模块化：较小的模块，每个模块都提供特定的功能，在执行更复杂功能的较大模块中使用。在此之前，我们提出了一个建模框架，称为Evo-Lexis，它提供了一些关于进化层次系统的基本问题的见解。Evo-Lexis模型的预测应该使用来自进化系统的真实数据进行测试，其中输出可以很好地用序列表示。在本文中，我们调查的时间序列的iGEM合成DNA数据集序列，以及所得到的iGEM层次结构是否表现出定性性质预测的Evo-Lexis框架。与Evo-Lexis相反，在iGEM中，重用量在数据集的时间轴期间减少。虽然这导致开发成本效益较低且深度较低的LPEG-DAG，但数据集在重用特定节点方面表现出比其他节点更频繁的偏差。这导致LPEG-DAG采取具有相对高的H分数值和稳定的核心节点集的沙漏形状。尽管核心集的重用偏差和稳定性，但数据集在目标之间呈现出大量的多样性，这与Evo-Lexis的建模一致。
英文摘要：
Many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions. Previously, we have proposed a modeling framework, referred to as Evo-Lexis, that provides insight to some fundamental questions about evolving hierarchical systems. The predictions of the Evo-Lexis model should be tested using real data from evolving systems in which the outputs can be well represented by sequences. In this paper, we investigate the time series of iGEM synthetic DNA dataset sequences, and whether the resulting iGEM hierarchies exhibit the qualitative properties predicted by the Evo-Lexis framework. Contrary to Evo-Lexis, in iGEM the amount of reuse decreases during the timeline of the dataset. Although this results in development of less cost-efficient and less deep Lexis-DAGs, the dataset exhibits a bias in reusing specific nodes more often than others. This results in the Lexis-DAGs to take the shape of an hourglass with relatively high H-score values and stable set of core nodes. Despite the reuse bias and stability of the core set, the dataset presents a high amount of diversity among the targets which is in line with modeling of Evo-Lexis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
