Selective information transmission by particle distributions

Living cells respond to environmental cues under noisy conditions, making them ideal platforms for uncovering physical principles of computation in soft materials. Signal processing by spatial heterogeneities remains a yet under explored component of such cellular regulation. Motivated by the relaying of signals across membranes, I investigate information transmission by surface-bound particle distributions. Extending maximum caliber methods, I address how microscopic constraints influence non-equilibrium particle dynamics, and identify a mechanism for information transmission arising from non-linear responses of equilibrium particle densities to spatial features in adjacent structures. This permits pattern recognition, with inter-particle interactions tuning the response function of noisy signal filters and resulting in the efficient encoding of information relevant to downstream tasks. I identify thresholding and edge-detecting regimes, and quantify how biophysical membrane properties affect information transmission by thresholding filters. I discover parameter regimes with optimal transmission where many biophysical systems fall – including nuclear pore complex distributions in Sphaeroforma arctica which show signatures of particle-mediated thresholding. Feedback from particle distributions to interaction energies is shown to improve selective information transmission. These results indicate that spatial distributions of macromolecular complexes can selectively sense environmental cues, with fundamental implications for how physical interactions may encode computational logic in soft materials.