From signal detection to quantum dynamics in biology

生物系统不断地暴露在不断变化的环境中，但通过收集有关其周围环境条件的信息并处理这些信息以达到最佳策略来设法适应它们。适应是生存的关键，信号检测是适应的精髓。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，如何才能模拟生物系统的动力学行为呢？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。它们的动力学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解释是，控制一大类量子系统演化的动力学方程同样是最佳信号检测的方程，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控制。一个粒子沉浸在环境中，与环境中的粒子进行能量相互作用，将遵循能量上优选的动力学-但这需要粒子提取有关环境条件的信息，而在量子水平上，这些信息必然是嘈杂的。因此，最有效的方法是进行最佳信号处理，从而导致量子动力学。换句话说，大自然是高效的。量子和生物动力学都受潜在的信号检测问题的影响，这一观察结果令人震惊，导致了这样的假设，即最佳信号检测能力是自然规律的基础，并且控制量子系统演化的动力学方程可以在必要时适用于模拟生物系统的动力学行为。我在最近的论文中提出了这些观点（科学报告第12卷，3042（2022）），已被应用于模拟绿色植物的热带运动，并将在本研究计划中进一步开发，以探索测试模型的方法，分析其他生物系统的动力学行为，并接触生物研究人员，以期对理解生物系统的动力学行为做出重大贡献。沿着这条路，我们可以在某些类型的随机方程上获得新的数学见解，这些随机方程具有量子力学的特征。

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.