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Testing the limits of gradient sensing.

Vinal Lakhani1,2, Timothy C Elston1,3

  • 1Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

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Cells can detect faint chemical signals, crucial for processes like development and movement. This study reveals that common noise-reduction strategies like time-averaging and receptor internalization do not significantly improve gradient sensing accuracy in yeast.

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Area of Science:

  • Cellular Biology
  • Biophysics
  • Computational Biology

Background:

  • Gradient sensing is vital for cellular processes including chemotaxis, chemotropism, development, and wound healing.
  • Cells, like yeast, can detect extremely shallow chemical gradients amidst molecular noise.
  • Proposed mechanisms for noise reduction include time-averaging and receptor internalization.

Purpose of the Study:

  • To test the effectiveness of proposed noise reduction mechanisms in cellular gradient sensing.
  • To determine the limits of gradient sensing accuracy in yeast cells.
  • To develop novel simulation methods for studying gradient sensing under steady-state and transient conditions.

Main Methods:

  • Utilized a Particle-Based Reaction-Diffusion model to simulate ligand-receptor dynamics.
  • Developed new simulation techniques to establish and analyze chemical gradients.
  • Incorporated reported reaction rates for yeast pheromone response.

Main Results:

  • Neither time-averaging nor receptor endocytosis significantly enhanced gradient sensing accuracy on relevant timescales.
  • The cell membrane acts as a physical barrier, sharpening chemical gradients across the cell.
  • Simulation methods allowed for the study of both steady-state and transient gradient formation.

Conclusions:

  • Time-averaging and receptor internalization are not primary mechanisms for improving gradient sensing accuracy in yeast during polarized growth initiation.
  • Cellular compartmentalization by the membrane plays a significant role in gradient perception.
  • The developed simulation framework is applicable to various biological contexts involving gradient sensing.