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Area of Science:

  • Biophysics
  • Cellular Biology
  • Theoretical Biology

Background:

  • Cells perform computations to respond to environmental cues, such as determining chemical ligand concentrations.
  • Theoretical principles like Landauer's principle suggest that information processing, including memory erasure, requires energy consumption.
  • The Berg-Purcell model provides a foundational framework for understanding cellular sensing of external concentrations.

Purpose of the Study:

  • To explicitly calculate the energetic cost of steady-state computation of ligand concentration in a cellular network.
  • To investigate the relationship between cellular learning of external concentrations and energy consumption.
  • To explore the implications of energetic costs for cellular networks in resource-limited environments.

Main Methods:

  • Developed a simple two-component cellular network model.
  • Implemented a noisy version of the Berg-Purcell strategy for ligand concentration sensing.
  • Performed explicit calculations of the energetic cost at steady-state.

Main Results:

  • Learning about external ligand concentrations inherently requires breaking detailed balance.
  • Cellular computation of ligand concentration necessitates energy consumption.
  • Increased accuracy in determining external concentrations correlates with higher energy expenditure.

Conclusions:

  • The energetic cost of cellular computation is a significant factor, particularly for networks operating in low-resource conditions.
  • Bacterial spore germination networks may be constrained by the energy demands of their computational processes.
  • Understanding these energetic costs is crucial for predicting the behavior and limitations of cellular systems.