Modeling autopoiesis and cognition with reaction networks

Area of Science:

• Systems biology
• Theoretical chemistry
• Origin of life research

Background:

• Maturana and Varela defined autopoietic systems as self-regenerating networks.
• Autopoiesis is foundational to understanding living systems and their self-maintenance.
• Previous models often lacked a process-based, formal framework.

Purpose of the Study:

• To reinterpret and elaborate the concept of autopoiesis using process ontology.
• To model autopoietic organizations as self-maintaining reaction networks.
• To explore the relationship between autopoiesis, self-organization, and cognition.

Main Methods:

• Formalization using reaction networks and chemical organization theory.
• Modeling autopoietic organizations as operationally closed and self-maintaining networks.
• Applying the "good regulator theorem" to understand system resilience and cognition.

Main Results:

• Autopoietic organizations are identified as attractors in dynamic systems, promoting self-organization and modeling the origin of life.
• Resilience in variable environments necessitates cognition for perturbation compensation.
• Cognition enhances through pattern discovery, leading to predictive models of the environment.

Conclusions:

• Autopoietic systems offer a robust model for the origin and survival of life.
• Cognition is essential for autopoietic system resilience, driven by environmental interaction.
• The internal models generated by autopoietic systems are subjective constructions, not objective representations of reality.