Trans-algorithmic nature of learning in biological systems

Area of Science:

• Theoretical Biology
• Systems Biology
• Biophysics

Background:

• Learning is crucial for biological systems' dynamic stability in changing environments.
• Current algorithmic models inadequately capture the full scope of biological learning.
• Physical hypercomputation has not yet successfully modeled general biological learning.

Purpose of the Study:

• To propose an alternative framework for understanding biological learning.
• To describe biosystems' learning through environmental interactions and modifications.
• To conceptualize learning as a trans-algorithmic process.

Main Methods:

• Describing biosystems as enumerating physical compositions via random modifications by active operating resources (AORs).
• Biosystems regulate modification intensity based on an optimality criterion for learning.
• Viewing biosystem evolution and adaptation as a motion in the space of algorithms.

Main Results:

• Biosystems learn via algorithmic regulation of environmentally imposed random modifications.
• The learning process is characterized as trans-algorithmic, exceeding simple algorithmic descriptions.
• Environmental AORs drive modifications, including population-level events like the death of unfit members.

Conclusions:

• Biological learning is fundamentally trans-algorithmic, driven by environmental interactions.
• This framework provides a more comprehensive understanding of life's adaptive capabilities.
• The model applies to diverse biosystems, from viruses to complex organisms.