Existence of biological uncertainty principle implies that we can never find 'THE' measure for biological complexity

TL;DR

This paper argues that due to inherent biological uncertainty and emergence, a single comprehensive measure of biological complexity is impossible, proposing a theoretical framework and uncertainty principle to explain these fundamental limitations.

Contribution

It introduces the Thread-Mesh model and formalizes an uncertainty principle, demonstrating the fundamental limits in defining a complete biological complexity measure.

Findings

Proves the impossibility of a universal biological complexity measure.

Introduces the Thread-Mesh model for biological reality.

Proposes an uncertainty principle for biological knowledge limits.

Abstract

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.