Trade-offs between cost and information in cellular prediction

Area of Science:

• Cellular biology
• Biophysics
• Systems biology

Background:

• Living cells sense environmental fluctuations to anticipate future changes.
• Cells encode past environmental signals to predict future states.
• Information storage is metabolically costly, necessitating efficient signal processing.

Purpose of the Study:

• To investigate how cellular networks encode past information to predict future environmental signals.
• To determine if cellular networks can reach the fundamental bound on predictive information.
• To explore the trade-off between predictive information and resource cost in cellular systems.

Main Methods:

• Theoretical analysis of information processing in cellular networks.
• Modeling of push-pull networks and temporal derivative networks.
• Application of information theory to quantify predictive information and resource costs.
• Analysis of the chemotaxis network in Escherichia coli.

Main Results:

• Cellular networks can reach the information bound for specific signal types (Markovian and non-Markovian).
• Highly informative past signals about the future are often too costly to store.
• Optimal cellular systems maximize predictive information under resource constraints, deviating from the information bound.
• The Escherichia coli chemotaxis network is optimized for predicting future concentration changes, particularly in shallow gradients.

Conclusions:

• Cellular predictive strategies are shaped by a trade-off between information content and metabolic cost.
• The Escherichia coli chemotaxis system demonstrates an optimal design for predicting environmental changes within biological constraints.
• Understanding these principles provides insights into cellular adaptation and information processing in biological systems.