C. Elegans: The worm that no computer scientist can crack

Computational difficulty of simulating biology

• Commenters stress how extreme the scales are: femtosecond-level timesteps, vast numbers of interacting atoms, and highly crowded, heterogeneous cytoplasm.
• Realistic whole‑cell simulations already push top supercomputers; one “empty cytoplasm” model of ~1/50th of a cell volume was a major effort just to get diffusion rates roughly right.
• Directed transport (e.g., along cytoskeleton) and massive numbers of unproductive molecular collisions add complexity beyond simple diffusion.
• State-of-the-art classical simulations reach ~10¹² atoms on huge GPU clusters; a single C. elegans neuron at all-atom resolution could be 10¹¹–10¹⁴ atoms, and the whole nervous system is 302 neurons—several orders of magnitude beyond current frontier work.
• Specialized molecular dynamics hardware and GPGPUs help; quantum computing is viewed as promising mainly for electronic-structure calculations, but far from practical at organism scale.

Limits and direction of worm modeling efforts

• Several people note that having the connectome ≠ understanding dynamics: the “wiring diagram” is known, but not the equivalent of weights, biases, and time-varying activations.
• Some argue the project is less “computational biology” and more “CFD with an embedded neural network,” implying a mismatch between hype and actual biological fidelity.
• There’s mention of funding drying up for neuron-simulation projects and claims that the original OpenWorm effort is effectively dead, with code spun off into a company; others simply say it’s good people are still trying.

Abstraction vs full-detail simulation

• Debate over whether one must simulate down to molecules or even quantum levels, or whether higher-level abstractions suffice for accurate behavior.
• One side: abstractions are inherently “leaky” because biology wasn’t designed with modular boundaries.
• Other side: non-leaky abstractions probably exist in practice; we already solve hard biology problems (e.g., protein folding) heuristically without full physics.

Free will, consciousness, and simulability

• A long subthread explores whether free will or non-physical aspects of mind make accurate simulation impossible.
• Positions range from dualist/idealistic (consciousness separate from matter; universe non-deterministic; strong skepticism that replaying physics yields same timeline) to strict physicalism (brains are physical systems; any such system is, in principle, simulable).
• Quantum randomness is invoked both as potential “room” for free will and as irrelevant noise that doesn’t create agency.
• Compatibilist views appear: we likely have constrained “will,” heavily shaped by biology and environment, not absolute freedom.

Reductionism, physicalism, and higher-level patterns

• Linked talks and essays (Michael Levin, Iain McGilchrist) are cited as challenges to strict reductionism, emphasizing high-level goal-directed behavior and “platonic” patterns.
• Critics counter that such frameworks are more philosophical than empirical and can often be reinterpreted as selection effects within physical law.

LLMs, life, and “cracking” complex systems

• Comparison of C. elegans vs LLMs: LLMs get massive funding and are easy to “simulate” (we built them), yet even this tiny worm resists faithful modeling.
• Some argue C. elegans is clearly more advanced as a life form (metabolism, reproduction, depression-like behavior), regardless of information-processing complexity.
• Others highlight that we haven’t truly “cracked” other biological systems either (yeast, bacteria, viruses), underscoring how far biology is from engineered-systems levels of understanding.