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Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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Limits of sensing temporal concentration changes by single cells.

Thierry Mora1, Ned S Wingreen

  • 1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.

Physical Review Letters
|September 28, 2010
PubMed
Summary
This summary is machine-generated.

This study extends concentration sensing limits to dynamic environments, finding maximum likelihood estimation offers twice the accuracy of linear regression for sensing concentration ramps in biological systems.

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Area of Science:

  • Biophysics
  • Cellular Biology
  • Biochemical Engineering

Background:

  • Single-celled organisms sense molecular concentrations, but accuracy is limited by molecular noise.
  • Previous work established limits for static concentration sensing.
  • Biological sensing often involves dynamic concentration changes, such as during chemotaxis.

Purpose of the Study:

  • Generalize previous findings to concentration ramp sensing.
  • Calculate uncertainty bounds for different sensing devices.
  • Compare linear regression and maximum likelihood estimation strategies.

Main Methods:

  • Theoretical calculation of lower bounds on uncertainty.
  • Analysis of three sensing models: single receptor, absorbing sphere, monitoring sphere.
  • Comparison of linear regression and maximum likelihood estimation algorithms.

Main Results:

  • Derived uncertainty bounds for ramp sensing.
  • Maximum likelihood estimation demonstrated up to twice the accuracy of linear regression.
  • Identified potential biological signatures for maximum likelihood estimation implementation.

Conclusions:

  • Maximum likelihood estimation is a more accurate strategy for biological concentration ramp sensing.
  • The findings provide a framework for understanding biological sensing mechanisms.
  • Suggests specific experimental signatures to identify maximum likelihood estimation in vivo.