Accurate information transmission through dynamic biochemical signaling networks

Abstract

Stochasticity inherent to biochemical reactions (intrinsic noise) and variability in cellular states (extrinsic noise) degrade information transmitted through signaling networks. We analyze the ability of temporal signal modulation, that is dynamics, to reduce noise-induced information loss. In the extracellular signal-regulated kinase (ERK), calcium (Ca2+), and nuclear factor kappa-B (NFκB) pathways, response dynamics resulted in significantly greater information transmission capacities compared to non-dynamic responses. Theoretical analysis demonstrated that signaling dynamics has a key role in overcoming extrinsic noise. Experimental measurements of information transmission in the ERK network under varying signal-to-noise confirmed our predictions and showed that signaling dynamics mitigate, and can potentially eliminate, extrinsic noise induced information loss. By curbing the information-degrading effects of cell-to-cell variability, dynamic responses substantially increase the accuracy of biochemical signaling networks.

Extending the dynamic range of transcription factor action by translational regulation

“…If noise in gene expression is Gaussian, as we assumed here, then the response of the regulatory pathway is fully characterized at steady state by the distribution P (g|c) = (g; ḡ(c),σ g (c)), where is a normal distribution with the mean and standard deviation that can be computed from the Langevin equations (1) and (10)- (13). The mutual information, I (c; g), between the regulator concentration c and the downstream expression level g is then given by (35) where P g (g) = ∫ dc P c (c)P (g|c).…”

“…To exploit this concept, as a first step it is necessary to estimate the various information theoretic quantities from data on real systems, and for genetic regulatory networks that has been achieved only very recently. There are estimates of the mutual information between the concentration of a TF and its target gene expression [8,9], the information that expression levels of multiple genes carry about the position of cells in the developing fruit fly embryo [10,11], and the information that gene expression levels provide about external signals in mammalian cells [12,13]. As a second step, we need to understand theoretically how the various features of the systemsthe architecture of signal transmission, the noise levels, the distribution of input signalscontribute to determining information transmission.…”

Trade-Offs in Delayed Information Transmission in Biochemical Networks

“…However, it has been argued that information transmission may be enhanced by dynamic biochemical readouts at multiple time points [33] or when the regulatory response is at a delay relative to input signaling [37]. Additionally, many biochemical networks function out of steady state, responding to inputs that are changing in time.…”

“…They argued that negative feedback, or information sharing between cells, can help transmit more information. The NF-kappa B and ERK pathways were recently used to demonstrate that dynamical measurements of the response can transmit more information than static or one time readouts [33]. Lastly, an information-theoretic approach was used in an experimental and numerical study to show the interdependence of stochastic processes controlling enzymatic calcium signaling under different cellular conditions [34].…”

Information Thermodynamics Derives the Entropy Current of Cell Signal Transduction as a Model of a Binary Coding System

“…In recent decades, the theoretical analysis of signal transduction has been broadly applied in various research fields, in parallel with significant development of information theory [1][2][3][4][5]. Informational thermodynamics for analyzing dynamic biochemical networks and systems biology have also been developed to assess the cell response to external stimuli [1][2][3][4][5][6].…”