Information and the living tree of life A theory of measurement grounded in biology.

IF 1.9 4区 生物学 Q2 BIOLOGY
Kevin Hudnall
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引用次数: 0

Abstract

We extend a formal framework that previously derived time from the multifractal structure of biological lineages (Hudnall & D'Souza, 2025). That work showed that time itself is multifractal - not a universal background dimension, but an observer-dependent geometry. Here we develop the corresponding theory of measurement: showing that a multifractal conception of time not only permits measurement, but grounds it more rigorously in the structure of biology. The tree of life is modeled as the outcome of stochastic, convex branching, and we show how information-theoretic and fractal measures render its multifractal geometry into measurable, observer-relative time intervals. At the core is a dilation equation that expresses relative time elapse between entities as dimensionless ratios. Operational standards such as the SI second remain valid, but our framework makes explicit their lineage-dependence. This framework unifies measurement theory with biological form, preserves full compatibility with established science, and provides a biologically grounded theory of observation. It enables comparative analyses of duration and kinematics across lineages, with predictions that are directly open to experimental validation.

信息与生命之树一种以生物学为基础的测量理论。
我们扩展了以前从生物谱系的多重分形结构中导出时间的正式框架(Hudnall & D'Souza, 2025)。这项工作表明,时间本身是多重分形的——不是一个普遍的背景维度,而是一个依赖于观察者的几何形状。在这里,我们发展了相应的测量理论:表明时间的多重分形概念不仅允许测量,而且更严格地以生物学结构为基础。生命之树被建模为随机凸分支的结果,我们展示了信息理论和分形测量如何将其多重分形几何呈现为可测量的,观察者相对的时间间隔。其核心是一个膨胀方程,它将实体之间的相对时间间隔表示为无因次比率。像SI second这样的操作标准仍然有效,但是我们的框架明确了它们的谱系依赖性。这一框架将测量理论与生物形式统一起来,保持了与已建立的科学的完全兼容性,并提供了一种基于生物学的观察理论。它可以对不同谱系的持续时间和运动学进行比较分析,并直接对实验验证进行预测。
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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