Optimal temporal patterns for dynamical cellular signaling

TL;DR

This paper uses optimal control theory to identify energy-efficient and reliable dynamical signaling patterns in cells, explaining the prevalence of steep, gradual, and overshooting signals through principles of minimizing energy and uncertainty.

Contribution

It introduces a framework applying optimal control theory to determine biologically relevant signaling patterns, revealing the conditions favoring different dynamical behaviors.

Findings

Overshooting signals are optimal for energy minimization.

Steep, gradual, and overshooting patterns confer specific advantages.

Cell signaling patterns can be explained by energy and uncertainty minimization.

Abstract

Cells use temporal dynamical patterns to transmit information via signaling pathways. As optimality with respect to the environment plays a fundamental role in biological systems, organisms have evolved optimal ways to transmit information. Here, we use optimal control theory to obtain the dynamical signal patterns for the optimal transmission of information, in terms of efficiency (low energy) and reliability (low uncertainty). Adopting an activation-deactivation decoding network, we reproduce several dynamical patterns found in actual signals, such as steep, gradual, and overshooting dynamics. Notably, when minimizing the energy of the input signal, the optimal signals exhibit overshooting, which is a biphasic pattern with transient and steady phases; this pattern is prevalent in actual dynamical patterns. We also identify conditions in which these three patterns (steep, gradual, and overshooting) confer advantages. Our study shows that cellular signal transduction is governed by the principle of minimizing free energy dissipation and uncertainty; these constraints serve as selective pressures when designing dynamical signaling patterns.