Mizas Ch, Sirakoulis G Ch, Mardiris V, Karafyllidis I, Glykos N, Sandaltzopoulos R
Democritus University of Thrace, Department of Electrical and Computer Engineering, 67100 Xanthi, Greece.
Biosystems. 2008 Apr;92(1):61-8. doi: 10.1016/j.biosystems.2007.12.002. Epub 2007 Dec 25.
Change of DNA sequence that fuels evolution is, to a certain extent, a deterministic process because mutagenesis does not occur in an absolutely random manner. So far, it has not been possible to decipher the rules that govern DNA sequence evolution due to the extreme complexity of the entire process. In our attempt to approach this issue we focus solely on the mechanisms of mutagenesis and deliberately disregard the role of natural selection. Hence, in this analysis, evolution refers to the accumulation of genetic alterations that originate from mutations and are transmitted through generations without being subjected to natural selection. We have developed a software tool that allows modelling of a DNA sequence as a one-dimensional cellular automaton (CA) with four states per cell which correspond to the four DNA bases, i.e. A, C, T and G. The four states are represented by numbers of the quaternary number system. Moreover, we have developed genetic algorithms (GAs) in order to determine the rules of CA evolution that simulate the DNA evolution process. Linear evolution rules were considered and square matrices were used to represent them. If DNA sequences of different evolution steps are available, our approach allows the determination of the underlying evolution rule(s). Conversely, once the evolution rules are deciphered, our tool may reconstruct the DNA sequence in any previous evolution step for which the exact sequence information was unknown. The developed tool may be used to test various parameters that could influence evolution. We describe a paradigm relying on the assumption that mutagenesis is governed by a near-neighbour-dependent mechanism. Based on the satisfactory performance of our system in the deliberately simplified example, we propose that our approach could offer a starting point for future attempts to understand the mechanisms that govern evolution. The developed software is open-source and has a user-friendly graphical input interface.
推动进化的DNA序列变化在一定程度上是一个确定性过程,因为诱变并非以完全随机的方式发生。到目前为止,由于整个过程极其复杂,尚未能够破译支配DNA序列进化的规则。在我们尝试解决这个问题时,我们仅专注于诱变机制,而刻意忽略自然选择的作用。因此,在本分析中,进化指的是源自突变并在不经历自然选择的情况下代代相传的基因改变的积累。我们开发了一种软件工具,它允许将DNA序列建模为一维细胞自动机(CA),每个细胞有四种状态,分别对应于四个DNA碱基,即A、C、T和G。这四种状态由四进制数系统的数字表示。此外,我们还开发了遗传算法(GA),以确定模拟DNA进化过程的CA进化规则。我们考虑了线性进化规则,并用方阵来表示它们。如果有不同进化步骤的DNA序列,我们的方法可以确定潜在的进化规则。相反,一旦破译了进化规则,我们的工具可以重建任何先前进化步骤中确切序列信息未知的DNA序列。所开发的工具可用于测试可能影响进化的各种参数。我们描述了一种基于诱变受近邻依赖机制支配这一假设的范式。基于我们的系统在刻意简化的示例中的良好表现,我们提出我们的方法可以为未来理解支配进化的机制的尝试提供一个起点。所开发的软件是开源的,并且有一个用户友好的图形输入界面。