Life, self-reproduction and information: beyond the machine metaphor

Area of Science:

• Theoretical Biology
• Information Theory
• Computational Science

Background:

• Current automation models of self-replication face challenges in information representation.
• Existing models inadequately capture the nuances of natural biological reproduction.

Purpose of the Study:

• To address the problem of information representation in self-replication automation models.
• To critique the inadequacy of current theoretical understandings of life and replication.
• To propose a new theoretical framework for biological information.

Main Methods:

• Analysis of information representation in computable self-replication models.
• Comparison between natural reproduction and artificial replication processes.
• Theoretical discussion on the nature of information in living systems.

Main Results:

• Computable models of self-replication do not fully encode information for guiding the process, unlike natural reproduction.
• Current theoretical frameworks based on these models are deemed insufficient.
• A distinction is made between biological information and 'knowledge' representable by computer programs.

Conclusions:

• A new theory of information is required for biological systems.
• This new theory must recognize information in living systems as fundamentally different from computable knowledge.
• Further development of biological theories should explore this new direction for understanding replication.