基因调控区的进化计算和模块化组织

A. Spirov, E. Myasnikova
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引用次数: 2

摘要

基因调控区域(启动子和增强子)的模块化组织对我们理解基因的功能和进化至关重要。在进化计算领域,受到进化生物学思想和概念的启发,特别关注进化搜索效率的理论基础。图式定理和积木假说(J. Holland)为这一领域奠定了基础。在进一步发展理论的道路上,引入并全面研究了皇家道路函数(rrf)。在这里,我们正在考虑一些模块化基因调控区域的案例研究,这些区域可以被视为定向分子进化(SELEX等)中的rfs实现。来自不同数学领域的分析工具使van Nimwegen和他的合作者有可能对RRF类问题的搜索动态进行详细和定量的描述。该方法连接了从基准案例(如RRF)到生物学案例(可以作为试管中更有效的定向分子进化的基础)的进化计算,这些案例在理论上已经得到了很好的理解。通过引入在rrf上表现良好的交叉算子,我们正在开发计算技术来处理细菌启动子的实际设计问题。特别是,我们正在引入类似逆转录病毒或“性”PCR重组的交叉操作符。我们发现,我们的算法能够实现进化搜索的效率大大高于标准EC。EC的计算理论既有助于理解真实的基因结构是如何进化的,也有助于加快启动子和其他基因调控元件定向进化的实验室工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolutionary Computations and Modular Organization of the Gene Regulatory Regions
Modular organization of gene regulatory regions (promoters and enhancers) is crucial for our understanding of the gene functioning and evolution. In the area of Evolutionary Computations, inspired by the ideas and concepts from evolutionary biology, it was paid a special attention to the theoretical foundations for the evolutionary search efficacy. These were Schema theorem and Building block hypothesis (by J. Holland) that laid the foundation for this area. On the way to further develop the theory, the Royal Road functions (RRFs) were introduced and comprehensively studied. Here we are considering some case-studies of the modular gene regulatory regions which could be treated as RRFs implementations in directed molecular evolution (SELEX, etc.). Analytical tools from different math fields gave possibilities for van Nimwegen with co-authors to develop a detailed and quantitative description of the search dynamics for the RRF class of problems. The approach bridges evolutionary computations from benchmark cases, such as RRF, which are well-understood theoretically, to biological cases, which can serve as a basis for more efficient directed molecular evolution in the test tube. By introducing crossover operators that perform well on RRFs, we are developing computational techniques to deal with the real design problems for bacterial promoters. In particular, we are introducing crossover operators that work like retroviral or "sexual" PCR recombination. We found that our algorithms are capable to achieve the efficacy of the evolutionary search substantially higher than standard EC. Computational theory from EC can contribute to both understanding how real gene structures have evolved and to speeding up laboratory work on directed evolution of promoters and other gene regulatory elements.
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