Trade-offs in concentration sensing in dynamic environments.

Area of Science:

• Cellular biology
• Biophysics
• Chemical sensing

Background:

• Cells use time averaging to reduce measurement noise during chemical signal detection.
• Dynamic environments introduce errors when past measurements are no longer representative of current conditions.

Purpose of the Study:

• To investigate how environmental statistics influence the trade-off between sensing noise and environmental variation in cellular chemical sensing.
• To determine the environmental properties that make this trade-off significant for cells.

Main Methods:

• Modeling a single eukaryotic cell sensing bacterial chemoattractants (e.g., folic acid).
• Employing analytical calculations and stochastic simulations to analyze sensing dynamics.
• Investigating the impact of bacterial motility on chemoattractant concentration.

Main Results:

• Identified a wide range of optimal cellular sensing strategies, from no time averaging to finite or infinite averaging times.
• Found that the ratio of sensing noise to environmental variation is a key factor controlling optimal averaging.
• Determined that the ratio of sensing to environmental timescales also critically influences sensing strategy.

Conclusions:

• Cellular sensing strategies are adaptable to environmental fluctuations.
• Environmental variation, driven by factors like bacterial density and chemoattractant diffusion, significantly impacts optimal sensing.
• Fluctuating environmental concentrations can be a substantial source of noise, even in seemingly stable environments.