生物不确定性原理的存在意味着我们永远找不到生物复杂性的“THE”测度
Existence of biological uncertainty principle implies that we can never find 'THE' measure for biological complexity
摘要：
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，但生物学的学生仍然无法用一种单一的方法来涵盖生物系统的看似无数的特征。本文的目的是探讨寻找一种完整、客观的生物复杂性测度的可行性。本文提出了一种理论构造（“线-网模型”）来描述生物现实。它将整个生物时空分割成一系列不同的生物组织，然后用计算和拓扑结构对每个组织的属性空间进行建模。承认涌现是一个关键的生物特性，这里已经证明，寻求一个客观和全面的生物复杂性措施必然会以失败告终。由于任何对生物复杂性的研究都植根于对生物实在性的认识，本文提出了人类对本体论生物实在性认识的可能限度的一种表述，即不确定性原理。两个定理提出了模型的基本限制，由于观察者依赖性的生物现实的描述。他们解释了构建一个单一而完整的生物复杂性度量失败背后的原因。该模型得到了各种实验结果的支持，因此为研究生物复杂性和生物现实性提供了一种可靠和通用的方法。
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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