Quantum effects in ion transport: A thermodynamic resource theory approach

IF 2 4区 生物学 Q2 BIOLOGY
Amin Mohammadi, Afshin Shafiee
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引用次数: 0

Abstract

In recent years, understanding thermodynamics in the quantum regime has garnered significant attention, driven by advances in nanoscale physics and experimental techniques. In parallel, growing evidence supports the importance of quantum effects in various biological processes, making them increasingly relevant to quantum thermodynamics. In this study, we apply resource theory formulations of thermodynamics to investigate the role of quantum properties in ion transport across cell membranes. Within this framework, quantum properties are treated as resources under generalized thermodynamic constraints in the quantum regime. Specifically, our findings reveal that non-Markovianity, which reflects memory effects in ion transport dynamics, is a key quantum resource that enhances the yield and efficiency of the ion transport process. In contrast, quantum coherence, manifested as the superposition of energy states in ion-transport proteins, reduces these metrics but plays a crucial role in distinguishing between ion channels and ion pumps—two distinct types of ion-transport proteins in cell membranes. Finally, we demonstrate that introducing an additional coherent system allows coherence to facilitate the transformation of an ion pump into an ion channel.
离子输运中的量子效应:一种热力学资源理论方法
近年来,在纳米物理和实验技术的推动下,理解量子热力学引起了极大的关注。与此同时,越来越多的证据支持量子效应在各种生物过程中的重要性,使它们与量子热力学的关系日益密切。在这项研究中,我们应用热力学的资源理论公式来研究量子性质在离子跨细胞膜传输中的作用。在这个框架中,量子性质被视为量子体系中广义热力学约束下的资源。具体来说,我们的研究结果表明,反映离子传输动力学中记忆效应的非马尔可夫性是提高离子传输过程产率和效率的关键量子资源。相比之下,量子相干,表现为离子转运蛋白中能量态的叠加,减少了这些度量,但在区分离子通道和离子泵(细胞膜中两种不同类型的离子转运蛋白)方面起着至关重要的作用。最后,我们证明了引入一个额外的相干系统可以使相干性促进离子泵向离子通道的转变。
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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