Viscous DNA and RNA: Quantum damped dynamical random systems

IF 1.9 4区 生物学 Q2 BIOLOGY
Hamze Mousavi, Samira Jalilvand
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引用次数: 0

Abstract

From a physics perspective, DNA and RNA molecules are characterized as dynamic biological structures that exhibit vibrations across a range of time scales. To conduct a more accurate investigation of their dynamic properties, it is essential to consider the environmental conditions surrounding these molecules. A harmonic Hamiltonian that incorporates damping, along with the Green’s function method, has been utilized to analyze the vibrational responses of viscous DNA and RNA strands. The DNA molecule is represented using a fishbone model alongside two distinct double-strand configurations, while a half-ladder model is applied to the RNA molecule. The interconnections between sub-sites are represented by linear springs, with the stiffness of the vertical springs and the damping coefficients of the dashpots varying randomly throughout the length of the systems. Furthermore, each model is examined under three distinct configurations: infinite, finite, and cyclic. The results reveal that the fluctuations in the density of states curves exhibit a gradual decline, leading to a broadening of the sharp peaks as the damping coefficient increases. Additionally, the vibrational modes become progressively less distinct with an increase in system damping, a finding that aligns well with the principles of wave mechanics and vibrational motion.
粘性DNA和RNA:量子阻尼的动态随机系统
从物理学的角度来看,DNA和RNA分子的特征是动态的生物结构,在一定的时间尺度上表现出振动。为了更准确地研究它们的动态特性,必须考虑这些分子周围的环境条件。一个包含阻尼的谐波哈密顿量,连同格林函数方法,已经被用来分析粘性DNA和RNA链的振动响应。DNA分子用鱼骨模型和两条不同的双链结构表示,而半阶梯模型应用于RNA分子。子点之间的连接由线性弹簧表示,垂直弹簧的刚度和阻尼器的阻尼系数在整个系统的长度中随机变化。此外,每个模型都在三种不同的配置下进行检查:无限、有限和循环。结果表明:随着阻尼系数的增大,态密度曲线的波动呈逐渐减小的趋势,尖峰变宽;此外,随着系统阻尼的增加,振动模式逐渐变得不那么明显,这一发现与波动力学和振动运动的原理很好地一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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