This research explores the emergence of enzymatic functions within an artificial chemistry framework. By using an algorithmic model, we investigate how enzymes, defined as cyclic functions that combine two functions to produce a new one, can form autocatabolic networks. These networks sustain and amplify themselves, fostering complexity growth and enabling the emergence of self-replication and selection mechanisms.
Key Concepts
Enzyme Formation: The formation of enzymes from combinatorial functions within an artificial chemistry model.
Autocatalysis: Enzymatic cycles that drive self-replication, leading to increased complexity in the system.
Complexity Growth: Understanding the dynamics of complexity growth in chemical systems that evolve over time.
