  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.
