From signal detection to quantum dynamics in biology

从信号检测到生物学中的量子动力学

• 批准号: EP/X019926/1
• 负责人: Dorje Brody
• 金额: $ 9.17万
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX019926%2F1
• 关键词: signal; detection; quantum; dynamics; biology

Biological systems are constantly exposed to changing environments but manage to adapt to them by gathering information about the conditions of their surroundings and processing this information to arrive at best strategies. Adaptation is essential to survival and signal detection is quintessential to adaptation. Hence all biological processes are generated by their information-processing capabilities. How can one then model the dynamical behaviours of biological systems? A highly effective way forward is to model the flow of noisy information available to biological systems. Their dynamical behaviours can logically be derived from the techniques of stochastic filtering and signal detection. That is, mathematical techniques of signal detection is the most suited method to model dynamical systems driven by signal detection. Perhaps a much less intuitive obserrvation is that dynamical equations governing the evolution of a broad class of quantum systems likewise are equations for optimal signal detection, and there are reasons to believe that all quantum dynamics may be governed by underlying information-processing capabilities of fundamental particles. A particle immersed in an environment, energetically interacting with particles in the environment, will follow an energetically preferred dynamics --- but this requires the particle to extract information about the conditions of the environment, and at a quantum level this information is necessarily noisy. Hence the most efficient way forward is to conduct optimal signal processing, leading to quantum dynamics. In other words, nature is highly efficient. The observation that both quantum and biological dynamics are governed by underlying signal detection problems is striking, leading to the hypothesises that optimal signal detection capability is fundamental to laws of nature, and that dynamical equations governing the evolution of quantum systems can, mutatis mutandis, applied to model dynamical behaviours of biological systems. These ideas, set out in my recent paper (Scientific Report vol. 12, 3042 (2022)), have been applied to model tropic motions of green plants, and they will be developed further in this research programme to explore ways to test the models, to analyse dynamical behaviours of other biological systems, and to reach out biological researchers, with the view towards making a significant contribution to understanding dynamical behaviours of biological systems. Along the way, new mathematical insights can be gained on certain types of stochastic equations admitting quantum-mechanical characteristics.

生物系统不断暴露在变化的环境中，但通过收集周围环境的信息并处理这些信息以达到最佳策略，从而设法适应这些环境。适应是生存的必要条件，而信号检测是适应的必要条件。因此，所有的生物过程都是由它们的信息处理能力产生的。那么，如何对生物系统的动态行为进行建模呢？一个非常有效的方法是对生物系统可用的噪声信息流进行建模。它们的动态行为可以从随机滤波和信号检测技术中逻辑地推导出来。也就是说，信号检测的数学技术是对由信号检测驱动的动态系统建模的最合适的方法。也许一个不那么直观的观察是，控制大量量子系统演化的动力学方程同样也是最优信号检测的方程，并且有理由相信，所有量子动力学都可能由基本粒子的潜在信息处理能力所控制。一个粒子沉浸在一个环境中，与环境中的粒子进行能量相互作用，将遵循一种能量优先的动力学——但这需要粒子提取有关环境条件的信息，而在量子水平上，这些信息必然是有噪声的。因此，最有效的方法是进行最佳信号处理，从而实现量子动力学。换句话说，大自然是高效的。量子动力学和生物动力学都是由潜在的信号检测问题所控制的这一观察结果是惊人的，这导致了这样的假设：最佳信号检测能力是自然法则的基础，而控制量子系统进化的动力学方程可以在必要时应用于生物系统的动力学行为模型。这些想法，在我最近的论文（科学报告卷12,3042(2022)）中提出，已被应用于绿色植物的热带运动模型，并将在本研究计划中进一步发展，以探索测试模型的方法，分析其他生物系统的动态行为，并接触到生物研究人员，以期对理解生物系统的动态行为做出重大贡献。在此过程中，新的数学见解可以在某些类型的承认量子力学特征的随机方程中获得。

项目成果

Quantum formalism for the dynamics of cognitive psychology.

• DOI: 10.1038/s41598-023-43403-4
• 发表时间: 2023-09-26
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Brody, Dorje C.

Biological efficiency in processing information in green plants

• DOI: 10.1098/rspa.2022.0809
• 发表时间: 2023-08-30
• 期刊: PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
• 影响因子: 3.5
• 作者: Brody，Dorje C.;Trewavas，Anthony J.
• 通讯作者: Trewavas，Anthony J.