This research demonstrates that reinforcement learning enhances active transport and self-assembly in soft materials, indicating new programmable biomaterial applications.
Programming rapid, repeatable motions in soft materials has remained a challenge in active matter and biomimetic design. Here, we present a light-controlled chemomechanical network based on Tetrahymena thermophila calcium-binding protein 2 (Tcb2), a Ca 2+ -sensitive contractile protein. These networks—driven by Ca 2+ -triggered structural rearrangements—exhibit dynamic self-assembly, spatiotemporal growth, and contraction rates comparable to actomyosin systems. By coupling light-sensitive chelators for optically triggered Ca 2+ release, we achieve precise growth and repeatable mechanical contractility of Tcb2 networks, revealing emergent phenomena such as boundary-localized active regions and density gradient-driven reversals in motion. A coupled reaction-diffusion and elastic model explains these dynamics, highlighting the interplay between chemical network assembly and mechanical response. We further demonstrate active transport of particles via network-mediated forces in vitro and implement reinforcement learning to program seconds-scale spatiotemporal actuation in silico. These results establish a platform for designing responsive active materials with rapid chemomechanical dynamics and tunable optical control, with applications in synthetic cells, sub-cellular force generation, and programmable biomaterials.
No takes yet. Share an insight, caveat, or question.
Lei et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: