Journal
BULLETIN OF MATHEMATICAL BIOLOGY
Volume 82, Issue 2, Pages -Publisher
SPRINGER
DOI: 10.1007/s11538-020-00694-2
Keywords
Predictive information rate; Information processing; Nonequilibrium steady state; Thermodynamics
Categories
Funding
- John Templeton Foundation [52095]
- Foundational Questions Institute [FQXi-RFP-1609]
- U. S. Army Research Laboratory
- U. S. Army Research Office [W911NF-13-1-0390]
- MIT Physics of Living Systems Fellowship
- AFOSR [FA9550-19-1-0411]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available