Tradeoffs in concentration sensing in dynamic environments
Aparajita Kashyap, Wei Wang, and Brian A. Camley

TL;DR
This paper models how cells balance noise reduction and environmental variability in chemical sensing, revealing diverse optimal strategies depending on environmental and biological factors.
Contribution
It introduces a mathematical framework for understanding the tradeoff between measurement noise and environmental change in cellular sensing.
Findings
Cells can optimize sensing strategies based on environmental dynamics.
The ratio of sensing noise to environmental variation influences optimal averaging time.
Environmental fluctuation can be a significant noise source even in static environments.
Abstract
When cells measure concentrations of chemical signals, they may average multiple measurements over time in order to reduce noise in their measurements. However, when cells are in a environment that changes over time, past measurements may not reflect current conditions - creating a new source of error that trades off against noise in chemical sensing. What statistics in the cell's environment control this tradeoff? What properties of the environment make it variable enough that this tradeoff is relevant? We model a single eukaryotic cell sensing a chemical secreted from bacteria (e.g. folic acid). In this case, the environment changes because the bacteria swim - leading to changes in the true concentration at the cell. We develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies,…
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Taxonomy
TopicsMonoclonal and Polyclonal Antibodies Research · Gene Regulatory Network Analysis · Advanced Biosensing Techniques and Applications
MethodsDiffusion
