Hierarchies of Biocomplexity: modeling lifes energetic complexity

TL;DR

This paper introduces a systems-level computational model to analyze biological selection effects, emphasizing energetic complexity and hierarchical biocomplexity, with potential insights into fitness and evolutionary outcomes.

Contribution

It presents a novel computational framework for modeling biological selection and energetic complexity within hierarchical biocomplexity systems.

Findings

Four possible model outcomes are described.

The relationship between relative fitness and selection is analyzed.

Implications for understanding biological evolution are discussed.

Abstract

In this paper, a model for understanding the effects of selection using systems- level computational approaches is introduced. A number of concepts and principles essential for understanding the motivation for constructing the model will be introduced first. This will be followed by a description of parameters, measurements, and graphical representations used in the model. Four possible outcomes for this model are then introduced and described. In addition, the relationship of relative fitness to selection is described. Finally, the consequences and potential lessons learned from the model are discussed.