摘要翻译：
有无数的“生物复杂性度量”。虽然从这些试图代表生物复杂性的尝试中出现了一些模式，但生物学学生仍然无法用一种单一的方法来涵盖生物系统看似无数的特征。探讨寻找一种完整而客观的生物复杂性测度的可行性是本文的追求。本文提出了一种描述生物现实的理论结构（线网模型）。它将整个生物时空划分为一系列不同的生物组织，然后用计算和拓扑结构建模这些组织的属性空间。承认涌现是一个关键的生物学特性，这里已经证明，寻求一个客观的和包罗万象的生物复杂性度量必然以失败告终。由于对生物复杂性的任何研究都植根于对生物实在的认识，因此本文提出了一种以测不准原理的形式表达人类对本体论生物实在知识的可能极限的方法。由于生物现实描述的观察者依赖性质，提出了两个定理来建模基本限制。他们解释了未能建立一个单一和完整的生物复杂性度量的原因。该模型在各种实验结果中得到了支持，为研究生物复杂性和生物现实提供了一种可靠而通用的方法。
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英文标题：
《Existence of biological uncertainty principle implies that we can never
  find 'THE' measure for biological complexity》
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作者：
Anirban Banerji
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最新提交年份：
2009
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Biological Physics        生物物理学
分类描述：Molecular biophysics, cellular biophysics, neurological biophysics, membrane biophysics, single-molecule biophysics, ecological biophysics, quantum phenomena in biological systems (quantum biophysics), theoretical biophysics, molecular dynamics/modeling and simulation, game theory, biomechanics, bioinformatics, microorganisms, virology, evolution, biophysical methods.
分子生物物理、细胞生物物理、神经生物物理、膜生物物理、单分子生物物理、生态生物物理、生物系统中的量子现象（量子生物物理）、理论生物物理、分子动力学/建模与模拟、博弈论、生物力学、生物信息学、微生物、病毒学、进化论、生物物理方法。
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英文摘要：
  There are innumerable 'biological complexity measure's. While some patterns emerge from these attempts to represent biological complexity, a single measure to encompass the seemingly countless features of biological systems, still eludes the students of Biology. It is the pursuit of this paper to discuss the feasibility of finding one complete and objective measure for biological complexity. A theoretical construct (the 'Thread-Mesh model') is proposed here to describe biological reality. It segments the entire biological space-time in a series of different biological organizations before modeling the property space of each of these organizations with computational and topological constructs. Acknowledging emergence as a key biological property, it has been proved here that the quest for an objective and all-encompassing biological complexity measure would necessarily end up in failure. Since any study of biological complexity is rooted in the knowledge of biological reality, an expression for possible limit of human knowledge about ontological biological reality, in the form of an uncertainty principle, is proposed here. Two theorems are proposed to model the fundamental limitation, owing to observer dependent nature of description of biological reality. They explain the reasons behind failures to construct a single and complete biological complexity measure. This model finds support in various experimental results and therefore provides a reliable and general way to study biological complexity and biological reality.
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PDF链接：
https://arxiv.org/pdf/0902.0490