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Optimal Prediction by Cellular Signaling Networks.

Nils B Becker¹, Andrew Mugler², Pieter Rein Ten Wolde³

¹Bioquant, Universtität Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.

Physical Review Letters

|January 2, 2016

Summary

This summary is machine-generated.

Cells use linear signaling networks to predict environmental changes. Optimal prediction involves noise suppression, extrapolation, and selective readout of past signals for improved fitness.

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Area of Science:

Cellular signaling and information processing
Systems biology and biophysics

Background:

Living cells must anticipate environmental fluctuations to survive and thrive.
Cellular signaling networks play a crucial role in processing environmental cues.

Purpose of the Study:

To investigate the predictive accuracy of linear signaling networks in cells.
To identify the mechanisms cells employ for anticipating future environmental signals.

Main Methods:

Analysis of linear signaling network properties, including response kernels.
Mathematical modeling of single-layer and multilayer networks.
Simulations of bacterial chemotaxis in E. coli.

Main Results:

Maximal predictive power is achieved through input-noise suppression, linear extrapolation, and selective readout of correlated past signals.
Single-layer networks yield exponential kernels for Markovian signals; multilayer networks yield oscillatory kernels for non-Markovian signals.
Networks utilize signal derivatives at low noise and correlated past values at high noise for prediction.
Negative feedback and incoherent feed-forward motifs implement optimal response functions.
E. coli demonstrates reliable prediction of concentration changes during chemotaxis.

Conclusions:

Linear signaling networks possess inherent capabilities for accurate environmental signal prediction.
Network architecture and noise levels dictate optimal prediction strategies.
The integration time in cellular responses balances speed and noise suppression.