Theoretical upper bound of multiplexing in stochastic sensory receptors
arXiv:2205.09192 · doi:10.1103/PhysRevResearch.5.023032
Abstract
Biological sensory receptors provide excellent examples of microscopic scale information transduction amidst stochastic noise. We argue that stochasticity is not always a hindrance to sensing. Instead, it could allow a single stochastic sensor to perform multiplexing: simultaneously transducing multiple types of environmental information to the downstream sensory network. Through a Langevin dynamics simulation of a ligand-receptor sensor in a bath of ligands, we demonstrate that a binary-state receptor can simultaneously encode multiple independent environmental variables, such as ligand concentration and the speed of media flow. We develop a general theory of stochastic sensory multiplexing and suggest two theoretical upper bounds. Furthermore, we conjecture that randomly generated sensors typically saturate the tighter upper bound. The theoretical framework developed in this study, which involves a rank-deficient maximum likelihood analysis (rd-MLE), provides a systematic approach to comprehensively assess a sensor's sensory ability without any initial assumptions. This theoretical framework can inspire the design of more efficient artificial sensors.
References in corpus (8)
- Second-law-like inequalities with information and their interpretations
- Maximum likelihood and the single receptor
- Yang-Lee Theory for a Nonequilibrium Phase Transition
- Generic temperature compensation of biological clocks by autonomous regulation of catalyst concentration
- Physical limit to concentration sensing in a changing environment
- Fundamental Limits on Sensing Chemical Concentrations with Linear Biochemical Networks
- Receptor crosstalk improves concentration sensing of multiple ligands
- Universal properties of concentration sensing in large ligand-receptor networks