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Area of Science:

  • Biophysics
  • Cellular Biology
  • Biochemistry

Background:

  • Cells sense external ligand concentrations using receptors, but accuracy is limited by finite receptor-ligand interactions.
  • Existing physical limits on sensing accuracy often assume constant concentrations and neglect temporal fluctuations.
  • Understanding sensing in dynamic environments is crucial for cell adaptation.

Purpose of the Study:

  • To derive new physical bounds on concentration sensing accuracy in fluctuating environments.
  • To investigate how cells can achieve optimal sensing in the presence of temporal concentration variations.
  • To propose a mechanism for downstream biochemical networks to enhance sensing capabilities.

Main Methods:

  • Formulated concentration sensing in fluctuating environments as a nonlinear field-theoretic problem.
  • Developed an approximate Gaussian solution for the field-theoretic model.
  • Derived a new physical bound for the relative error in concentration sensing.

Main Results:

  • A new physical bound on relative concentration error was derived: δc/c∼(Dacτ)^{-1/4}.
  • This bound contrasts with the traditional Berg and Purcell bound (δc/c∼(DacT)^{-1/2}).
  • Demonstrated that a downstream biochemical network can achieve this bound by adapting signaling kinetics.

Conclusions:

  • Sensing accuracy in fluctuating environments is governed by a new physical limit dependent on diffusivity, receptor size, and fluctuation timescale.
  • The derived bound offers a more realistic assessment of cellular sensing capabilities in dynamic conditions.
  • Adaptive biochemical networks downstream of receptors are key to achieving optimal concentration sensing accuracy.