No abstract

No abstract

No abstract

Biological sensory receptors provide perfect examples of microscopic scale information transduction in the presence of non-negligible thermal fluctuations. For example, studies of ligand-receptor reveal that accurate concentration sensing is achieved by integrating out noise in the sensor's stochastic trajectories. However, we argue that the stochastic trajectory is not always an adversary -it could allow a single sensor to perform multiplexing (or muxing) by simultaneously transducing multiple environmental variables (e.g., concentration, temperature, and flow speed) to the downstream sensory network. This work develops a general theory of stochastic sensory muxing and a theoretical upper bound of muxing. The theory is demonstrated and verified by an exactly solvable Markov dynamics model, where an arbitrary sensor can achieve the upper bound of muxing without optimizing parameters. The theory is further demonstrated by a realistic Langevin dynamics simulation of a ligand receptor within a bath of ligands. Simulations verify that even a binary state ligand receptor with short-term memory can simultaneously sense two out of three independent environmental variables -ligand concentration, temperature, and media's flow speed. Both models demonstrate that the upper bound for muxing is tight. This theory provides insights on designing novel microscopic sensors that are capable of muxing in realistic and complex environments.