Efficiency of cellular information processing

TL;DR

This paper investigates the thermodynamic limits of information processing in cellular systems, introducing an informational efficiency measure and analyzing models inspired by E. coli to understand how cells learn about their environment and the associated energy costs.

Contribution

It defines an informational efficiency bound based on thermodynamics and applies it to models of cellular sensing, including adaptation, revealing insights into cellular learning and dissipation.

Findings

Receptor learning rate can be nonzero without internal dissipation due to external chemical work.

Cells in slow-changing environments are highly inefficient, dissipating more energy than they gain in information.

Adaptation mechanisms extend the range of conditions where cells can effectively learn about their environment.

Abstract

We show that a rate of conditional Shannon entropy reduction, characterizing the learning of an internal process about an external process, is bounded by the thermodynamic entropy production. This approach allows for the definition of an informational efficiency that can be used to study cellular information processing. We analyze three models of increasing complexity inspired by the E. coli sensory network, where the external process is an external ligand concentration jumping between two values. We start with a simple model for which ATP must be consumed so that a protein inside the cell can learn about the external concentration. With a second model for a single receptor we show that the rate at which the receptor learns about the external environment can be nonzero even without any dissipation inside the cell since chemical work done by the external process compensates for this learning rate. The third model is more complete, also containing adaptation. For this model we show inter alia that a bacterium in an environment that changes at a very slow time-scale is quite inefficient, dissipating much more than it learns. Using the concept of a coarse-grained learning rate, we show for the model with adaptation that while the activity learns about the external signal the option of changing the methylation level increases the concentration range for which the learning rate is substantial.