Path 9: Autocatalytic Sets & Networks

Rationale: Life began as a self-organizing chemical reaction network – a set of molecules that collectively catalyze each other’s formation (even if no single molecule can copy itself alone). This theory often invokes hypercycles or other mathematical models where information is distributed among multiple molecular species. Manfred Eigen’s hypercycle (a cyclic coupling of replicators) and Stuart Kauffman’s collectively autocatalytic sets showed theoretically that relatively simple molecules can form self-sustaining networks given the right reactions. For example, if A helps produce B, B helps produce C, and C helps produce A, you get a closed loop of catalysis – the system can reproduce as a whole. Such networks could arise more easily than one super-molecule that does everything. There are hints in modern biochemistry: metabolic networks and even translation are so interlinked that no piece works alone – a vestige, perhaps, of a stage when no single polymer dominated. Kauffman’s models demonstrated that when a “soup” of polymers reaches a certain complexity, there’s a high probability a subset will be collectively autocatalytic. This path embraces life as an emergent property of complex chemistry.

Prerequisites: Familiarity with reaction kinetics, network theory, and a bit of computational modeling – one must grasp how to identify autocatalysis in a set of reactions and understand concepts like catalysis graphs and fixed points.

Dependencies: This path is quite general and can overlay others. It could occur in solution (with or without a membrane, though enclosure might help – tying to Path 6) or on surfaces. It doesn’t specify the molecules, so it could involve peptides, RNAs, or simple organics – thus bridging Path 4, 5, or 11. Ultimately, for true evolution, a system like a hypercycle benefits from encapsulation (to prevent “cheaters” from freeloading) – so it likely dovetails with Path 6 in later stages.

Signs of Progress: On the experimental side, a few autocatalytic networks have been observed (e.g. the formose sugar-forming reaction network shows autocatalysis; some small peptide networks show mutually catalytic growth). A landmark in 2016 was the creation of a self-reproducing set of three replicator strands of DNA, each helping to make the next – a kind of hypercycle in vitro. In silico and mathematical models continue to support that random chemistry above a threshold can organize itself. A clear victory for this path would be a demonstration of a sustained, evolving chemical network in the lab – for instance, a set of simple metabolic reactions that produce all catalysts they need in a cycle, or an artificial chemical ecosystem that can undergo “mutations” and selection. This remains an open challenge, but ongoing advances in systems chemistry are steadily moving toward designing such networks.

Base Camps

The original Deep Research document notes that detailed Base Camps for Path 9 “would follow similarly” to the first six paths (covering theoretical models like hypercycles and RAF sets, plus lab realizations) but were not fully enumerated in the source text due to length. The Inventory above provides the essential rationale and research directions.

Bibliography

(No separate path bibliography was provided for Path 9 in the source text; see the Hub page for cross-cutting references and the Partial Results section for relevant experimental findings.)