Regeneration
Regeneration & Anti-Fibrosis · Regenerative Medicine
Steer wounded tissue toward regeneration instead of scar — control the morphogen / ECM dynamics that decide fibrosis vs repair.
📋 The problem
Some animals regrow limbs and hearts; humans mostly scar. Whether injured tissue regenerates or fibroses is decided by morphogen and ECM dynamics. Steering those fields toward regeneration — and away from fibrosis — would transform medicine.
🧗 Why it's a grand challenge
Outcomes depend on spatially-patterned signaling over time; the same molecule can heal or scar depending on dose and timing; in-vivo control is limited.
🧮 Governing model
∂u/∂t = D_u∇²u + f(u,v); ∂[ECM]/∂t = k_p·TGFβ − k_d·[ECM]Reaction–diffusion morphogens coupled to a TGF-β/ECM fibrosis ODE: ∂u/∂t = D∇²u + f(u,v); fibrotic vs regenerative fixed point set by the control field.
Current best: Axolotl/zebrafish regeneration & anti-fibrotic (pirfenidone) models
🧭 Possible approaches
- Neural emulators of reaction–diffusion morphogen fields
- Optimal control of morphogen / timing inputs toward a regenerative fixed point
- Spatial-omics models that predict scar vs repair
🎯 Build the benchmark
Predict the functional tissue fraction restored (vs fibrotic scar) from post-injury morphogen/ECM fields under control inputs.
Metric: func_fraction — functional tissue fraction restored
Datasets to start from: Axolotl limb-regeneration spatial transcriptomics, Cardiac-fibrosis scar-vs-repair atlas, Wound-healing morphogen time-lapse
🤖 Build an AI agent to solve it
An agent that prescribes spatiotemporal signaling interventions to maximize regeneration for a given injury.
Once a benchmark exists, an AI4Science agent can iterate solutions against it — every verified solution earns PWM.
This is a frontier framing page — an open problem, not yet benchmarked or verified, unlike PWM's mature computational-imaging benchmarks.