Artificial chemistries make it possible to study how functional molecules and self-sustaining networks can emerge from abstract rules. This work uses an algorithmic model in which enzymes are cyclic functions that compose other functions into new ones. The resulting autocatalytic networks can sustain and amplify themselves, enabling complexity growth, self-replication, and selection.
Key Concepts
- Enzyme formation: Cyclic functions emerge from combinatorial operations inside the artificial chemistry.
- Autocatalysis: Enzymatic cycles sustain and amplify the networks that produce them.
- Complexity growth: The model tracks how evolving chemical systems accumulate structure over time.
