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Updated: May 27, 2026
13:57
Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects
Published on: February 18, 2014
Trade-offs and constraints in allosteric sensing.
Bruno M C Martins1, Peter S Swain
1Centre for Systems Biology at Edinburgh, The University of Edinburgh, Edinburgh UK. bruno.martins@ed.ac.uk
Plos Computational Biology
|November 19, 2011
Summary
Allosteric transcription factors, simple cellular sensors, have performance constraints. These constraints suggest selection favors a balance of sensing characteristics over optimizing a single one.
Area of Science:
- Molecular Biology
- Systems Biology
- Biophysics
Background:
- Cellular sensing of extracellular changes drives signal transduction and decision-making.
- The evolutionary selection of specific sensing biochemistries remains poorly understood.
- Allosteric transcription factors represent a fundamental and simple class of microbial sensors.
Purpose of the Study:
- Investigate the sensing characteristics of allosteric transcription factors.
- Understand how biochemical parameters and sensor numbers influence sensing performance.
- Determine the trade-offs and constraints among different sensing characteristics.
Main Methods:
- Applied the Monod-Wyman-Changeux model to analyze sensor behavior.
- Quantified six key sensing characteristics: dynamic range, Hill number, intrinsic noise, information transfer capacity, static gain, and mean response time.
- Calculated characteristic distributions as a function of biochemical parameters and sensor population size.
Main Results:
- Sensing characteristics are highly interdependent; optimizing one often constrains others.
- A significant portion of possible characteristic combinations are biophysically inaccessible.
- A population of 100 allosteric transcription factors can differentiate over four concentration bands.
Conclusions:
- Allosteric sensor selection likely favors a compromise across multiple performance characteristics.
- Evolutionary pressures do not appear to optimize for peak performance in a single sensing metric.
- Understanding these constraints provides insight into the evolution of cellular sensing mechanisms.
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