Computing life: Add logos to biology and bios to physics

Area of Science:

• Interdisciplinary research bridging physics and biology.
• Focus on emergent properties in complex systems.

Background:

• Biological systems exhibit high component state-dependency, indicating strong emergence.
• Reconstructing biological emergence is computationally intensive, exceeding human cognitive limits.
• Current modeling practices may oversimplify complexity, underestimating emergence strength.

Purpose of the Study:

• To analyze approaches for reconstructing emergent properties in physical and biological systems.
• To propose a method for scaling emergence based on component state-dependency.
• To advocate for a 'law of completeness' over Occam's razor in biological modeling.

Main Methods:

• Analysis of system component state-dependency to quantify emergence.
• In silico reconstruction of biological emergence using experimental rate laws and parameter values.
• Estimation of computational requirements for biological system modeling.

Main Results:

• Biological emergence is characterized as 'very strong' due to high state-dependency.
• In silico modeling of biological systems requires approximately 10^5 interactions.
• Computational power is not the primary limitation; observer effects and methodological assumptions pose greater challenges.

Conclusions:

• Biological emergence can be computationally modeled, but requires comprehensive data.
• The observer effect and traditional simplification methods (Occam's razor) hinder accurate modeling.
• A 'law of completeness' is proposed to ensure accurate reconstruction of strong emergence in biological systems.