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Reconstruction of DNA sequences using genetic algorithms and cellular automata: Towards mutation prediction?

机译:使用遗传算法和细胞自动机重建DNA序列:走向突变预测?

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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.
机译:在一定程度上,推动进化的DNA序列变化是确定性过程,因为诱变并不是绝对随机发生的。到目前为止,由于整个过程的极端复杂性,尚无法破译控制DNA序列进化的规则。在我们试图解决这个问题的过程中,我们仅关注诱变的机制,而无视自然选择的作用。因此,在此分析中,进化是指遗传变异的积累,这种遗传变异起源于突变,并在不经过自然选择的情况下代代相传。我们已经开发了一种软件工具,可以将DNA序列建模为一维细胞自动机(CA),每个细胞具有四个状态,分别对应于四个DNA碱基,即A,C,T和G。这四个状态表示为按四进制数系统的数字。此外,为了确定模拟DNA进化过程的CA进化规则,我们已经开发了遗传算法(GA)。考虑了线性演化规则,并使用平方矩阵来表示它们。如果可以使用不同进化步骤的DNA序列,则我们的方法可以确定潜在的进化规则。相反,一旦确定了进化规则,我们的工具就可以在任何先前的确切分子序列信息未知的进化步骤中重建DNA序列。开发的工具可用于测试可能影响进化的各种参数。我们根据诱变受近邻依赖机制支配的假设来描述范式。基于故意简化的示例中系统的令人满意的性能,我们建议我们的方法可以为将来尝试理解控制进化的机制提供一个起点。开发的软件是开源的,并具有用户友好的图形输入界面。

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