Gradistics: An underappreciated dimension in evolutionary space.

Area of Science:

• Evolutionary biology
• Systems biology
• Biomedicine

Background:

• The growth of complexity is a fundamental yet underappreciated problem in biology.
• Natural selection, while not directly favoring complexity, can drive complexification through ecological niche diversification.
• Existing models do not fully explain the observed increase in biological complexity.

Purpose of the Study:

• To explore the causes and mechanisms underlying the growth of biological complexity.
• To propose and compare two models explaining complexification: the 'atomic orbitals' (AO) model and the 'regulatory advantage' (RA) model.
• To highlight the biomedical significance of understanding complexity, particularly in animal evolution.

Main Methods:

• Hypothesis testing and theoretical modeling.
• Focus on information storage and processing as proxies for complexity.
• Analysis of ecological niche dynamics and regulatory adaptation, including behavioral and genetic mechanisms.

Main Results:

• The 'atomic orbitals' (AO) model describes how niche diversification, driven by demographic pressure, increases biocenosis complexity.
• The 'regulatory advantage' (RA) model posits that regulatory adaptation, enhanced by accumulated information and learning, further drives complexification.
• Animal evolution, with its development of nervous systems, provides a key case study for these models.

Conclusions:

• Complexification is an emergent property of ecological and evolutionary processes, not a direct goal of natural selection.
• Regulatory adaptation offers a significant evolutionary advantage, enabling faster responses than genetic adaptation alone.
• Understanding the drivers of complexity is crucial for fields like biomedicine, particularly concerning human evolution and health.