Prediction and Dissipation in Nonequilibrium Molecular Sensors: Conditionally Markovian Channels Driven by Memoryful Environments

• Published in: Bulletin of mathematical biology Vol. 82; no. 2; p. 25
• Main Authors: Marzen, Sarah E., Crutchfield, James P.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2020; Springer Nature B.V
• Subjects: Biological models (mathematics); Biosensing Techniques - statistics & numerical data; Cell Biology; Channels; Computational Biology; Computer Simulation; Ion Channels - metabolism; Kinetics; Life Sciences; Markov Chains; Mathematical and Computational Biology; Mathematical Concepts; Mathematics; Mathematics and Statistics; Models, Biological; Original Article; Predictions; Sensors; Synthetic Biology; Systems Biology; Thermodynamics; Thermodynamics; Nonequilibrium steady state; Predictive information rate; Information processing
• ISSN: 0092-8240; 1522-9602; 1522-9602
• DOI: 10.1007/s11538-020-00694-2

Biological sensors must often predict their input while operating under metabolic constraints. However, determining whether or not a particular sensor is evolved or designed to be accurate and efficient is challenging. This arises partly from the functional constraints being at cross purposes and partly since quantifying the prediction performance of even in silico sensors can require prohibitively long simulations, especially when highly complex environments drive sensors out of equilibrium. To circumvent these difficulties, we develop new expressions for the prediction accuracy and thermodynamic costs of the broad class of conditionally Markovian sensors subject to complex, correlated (unifilar hidden semi-Markov) environmental inputs in nonequilibrium steady state. Predictive metrics include the instantaneous memory and the total predictable information (the mutual information between present sensor state and input future), while dissipation metrics include power extracted from the environment and the nonpredictive information rate. Success in deriving these formulae relies on identifying the environment’s causal states, the input’s minimal sufficient statistics for prediction. Using these formulae, we study large random channels and the simplest nontrivial biological sensor model—that of a Hill molecule, characterized by the number of ligands that bind simultaneously—the sensor’s cooperativity. We find that the seemingly impoverished Hill molecule can capture an order of magnitude more predictable information than large random channels.