On a relational theory of biological systems: a natural model for complex biological behavior

TL;DR

This paper proposes a relational model for complex biological systems using graph theory and diffusion dynamics, incorporating Shannon's Entropy to account for biological importance and plasticity.

Contribution

It introduces the concept of biological loci and enhances existing models by integrating entropy-based importance measures and dynamic behavior analysis.

Findings

Model captures biological system plasticity and environmental adaptability.

Incorporates Shannon's Entropy to quantify biological importance.

Provides a framework for analyzing complex biological dynamics.

Abstract

In this paper, we develop a natural (empirical) relational theory for describing and modeling complex biological phenomena. We have as stepping stone the assertion: function implies structure. The theory is built upon a graph's theory structure in which a diffusion model of information takes place, and where dynamics can be investigated in order to generate steady quantifiers. In this context, we improve a seminal work by adding a free context biological importance measure given by the Shannon's Entropy. We also introduce the concept of biological loci. Such concept stands for closely related biological agents which plays a role as an agent by itself. Our results allow us to synthesize a natural model for complex biological behavior that takes into account: system's update, irreducibility, and exploit of the dynamical behavior mounted over a diffusion model. The model deals in final terms to its natural capacity to model plasticity and environmental changes, which has an intrinsic relationship with Shannon's Entropy and the sort of dynamics that biological systems can display.