Bioelectricity and Morphogenesis

A planarian fragment does not only carry genes and geometry. It also carries physiological state across tissue: membrane potentials, gap-junction-like communication, wound signals, and downstream patterning responses. The experimental literature around planarian regeneration makes that a useful teaching case, because early bioelectric state can affect anterior/posterior polarity and regenerated head/tail outcomes (Durant et al., 2019; Beane et al., 2011).

The Rusty Morphospace model does not try to become a full physiology simulator. It treats bioelectricity as an inspectable layer over a stable surface graph: sample nodes on a body mesh, conductance edges between neighboring samples, normalized voltage state, perturbation bands, memory state, and readout fields. This is enough to teach the architecture of a tissue-scale model while leaving calibrated wet-lab prediction outside the current claim.

Bioelectricity matters to Mesmer Prism because it gives another concrete vocabulary for multi-level patterning: voltage-like fields, conductance, gap-junction-like coupling, perturbation, memory, readout, repair, and target-state change. In Michael Levin's broader framing, cells, tissues, and organs are not passive materials. They are nested problem-solving systems whose physiological networks help navigate anatomical morphospace (Levin, 2023a; Levin, 2023b).

That does not make Plasmatic Multitudes, Mixed-Ability HSI, or Rusty Morphospace biological projects. The useful transfer is more constrained: bioelectric morphogenesis suggests synthetic dynamics for fields, coupling, memory, regeneration-like repair, and multi-scale agency. Those dynamics can inspire virtual swarm bodies or educational prosthetic/biotech questions later, while the current implementation remains a source-linked teaching model rather than a medical, prosthetic, or wet-lab system (Levin, 2021; Levin, 2022).

DiffeoMorph belongs on this adjacent side too: the paper DiffeoMorph: Learning to Morph 3D Shapes Using Differentiable Agent-Based Simulations and the hormoz-lab/diffeomorph implementation are useful for target-shape metrics and learned many-agent controllers, but they are not bioelectric or planarian physiology sources.

Planarians are useful here because regeneration is spatially legible. A transverse cut gives a wound band, an anterior/posterior axis, and a clear readout problem: head-like and tail-like identity must resolve in the right places. Experimental work gives qualitative constraints for an educational model, including gap-junction-mediated polarity effects and persistent altered morphology after transient perturbations (Oviedo et al., 2010; Emmons-Bell et al., 2015).

The first scenarios therefore stay simple: baseline anterior/posterior separation, transverse-cut wound response, conductance-block perturbation, transient depolarization with memory, and a no-memory control. Those scenarios are not meant to predict a real animal. They are checks that the model can express the right kind of tissue-scale relationship before any calibrated source data is introduced.

Head size and organ scaling studies add another useful lesson: voltage-like state can be read as an instructive patterning variable, not just a passive byproduct of cell state (Beane et al., 2013). In the current model, that idea appears as readout fields driven by normalized voltage and memory state.

The current implementation is not BETSE, a named-ion-channel simulator, a wet-lab planning system, a calibrated PlanformDB predictor, or a full xenobot/anthrobot world model. It uses normalized state and synthetic scenarios to make tissue-scale relationships visible before the project takes on heavier physiology or real-data claims.

That boundary is an engineering choice. It keeps the page readable, keeps code ownership clear, and avoids presenting educational dynamics as empirical prediction. The current PlanformDB slice is deliberately metadata-only. More detailed source-derived work can be added later when source IDs, license notices, transformations, and validation targets are explicit; source-paper figures and raw datasets should remain outside the public page unless rights and provenance have been reviewed.