physical limits on the accuracy of cell sensing

2026

1. Phys. Rev. E

   

   Perfect adaptation in eukaryotic gradient sensing using cooperative allosteric binding

   Vishnu Srinivasan^†, Wei Wang, and Brian A. Camley

   Phys. Rev. E [Editors’ Suggestion] , Apr 2026

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   Eukaryotic cells generally sense chemical gradients using the binding of chemical ligands to membrane receptors. To perform chemotaxis effectively in different environments, cells need to adapt to different concentrations. We present a model of gradient sensing where the affinity of receptor-ligand binding is increased when a protein binds to the receptor’s cytosolic side. This interior protein (allosteric factor) alters the sensitivity of the cell, allowing the cell to adapt to different ligand concentrations. We propose a reaction scheme where the cell alters the allosteric factor’s availability to adapt the average fraction of bound receptors to 1/2. We calculate bounds on the chemotactic accuracy of the cell and find that the cell can reach near-optimal chemotaxis over a broad range of concentrations. We find that the accuracy of chemotaxis depends strongly on the diffusion of the allosteric compound relative to other reaction rates. From this, we also find a tradeoff between adaptation time and gradient sensing accuracy.

   @article{z9xd-xbw5,
     title = {Perfect adaptation in eukaryotic gradient sensing using cooperative allosteric binding},
     author = {Srinivasan, Vishnu and Wang, Wei and Camley, Brian A.},
     journal = {Phys. Rev. E},
     volume = {113},
     issue = {4},
     pages = {044414},
     numpages = {14},
     year = {2026},
     month = apr,
     publisher = {American Physical Society},
     doi = {10.1103/z9xd-xbw5},
     url = {https://link.aps.org/doi/10.1103/z9xd-xbw5},
   }

2024

1. Biophys. J.

   

   Trade-offs in concentration sensing in dynamic environments

   Aparajita Kashyap^†, Wei Wang, and Brian A. Camley

   Biophysical Journal, May 2024

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   When cells measure concentrations of chemical signals, they may average multiple measurements over time in order to reduce noise in their measurements. However, when cells are in an environment that changes over time, past measurements may not reflect current conditions—creating a new source of error that trades off against noise in chemical sensing. What statistics in the cell’s environment control this trade-off? What properties of the environment make it variable enough that this trade-off is relevant? We model a single eukaryotic cell sensing a chemical secreted from bacteria (e.g., folic acid). In this case, the environment changes because the bacteria swim—leading to changes in the true concentration at the cell. We develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies ranging from not time averaging at all to averaging over an arbitrarily long time or having a finite optimal averaging time. The factors that primarily control the ideal averaging are the ratio of sensing noise to environmental variation and the ratio of timescales of sensing to the timescale of environmental variation. Sensing noise depends on the receptor-ligand kinetics, while environmental variation depends on the density of bacteria and the degradation and diffusion properties of the secreted chemoattractant. Our results suggest that fluctuating environmental concentrations may be a relevant source of noise even in a relatively static environment.

   @article{kashyap2024trade,
     title = {Trade-offs in concentration sensing in dynamic environments},
     author = {Kashyap, Aparajita and Wang, Wei and Camley, Brian A.},
     journal = {Biophysical Journal},
     volume = {123},
     number = {10},
     pages = {1184--1194},
     month = may,
     year = {2024},
     publisher = {Elsevier},
     doi = {10.1016/j.bpj.2024.03.025},
   }

2. Phys. Rev. E

   

   Limits on the accuracy of contact inhibition of locomotion

   Wei Wang and Brian A. Camley

   Phys. Rev. E, May 2024

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   Cells that collide with each other repolarize away from contact, in a process called contact inhibition of locomotion (CIL), which is necessary for correct development of the embryo. CIL can occur even when cells make a micron-scale contact with a neighbor—much smaller than their size. How precisely can a cell sense cell-cell contact and repolarize in the correct direction? What factors control whether a cell recognizes it has contacted a neighbor? We propose a theoretical model for the limits of CIL where cells recognize the presence of another cell by binding the protein ephrin with the Eph receptor. This recognition is made difficult by the presence of interfering ligands that bind nonspecifically. Both theoretical predictions and simulation results show that it becomes more difficult to sense cell-cell contact when it is difficult to distinguish ephrin from the interfering ligands, or when there are more interfering ligands, or when the contact width decreases. However, the error of estimating contact position remains almost constant when the contact width changes. This happens because the cell gains spatial information largely from the boundaries of cell-cell contact. We study using statistical decision theory the likelihood of a false-positive CIL event in the absence of cell-cell contact, and the likelihood of a false negative where CIL does not occur when another cell is present. Our results suggest that the cell is more likely to make incorrect decisions when the contact width is very small or so large that it nears the cell’s perimeter. However, in general, we find that cells have the ability to make reasonably reliable CIL decisions even for very narrow (micron-scale) contacts, even if the concentration of interfering ligands is ten times that of the correct ligands.

   @article{PhysRevE.109.054408,
     title = {Limits on the accuracy of contact inhibition of locomotion},
     author = {Wang, Wei and Camley, Brian A.},
     journal = {Phys. Rev. E},
     volume = {109},
     issue = {5},
     pages = {054408},
     numpages = {15},
     year = {2024},
     month = may,
     publisher = {American Physical Society},
     doi = {10.1103/PhysRevE.109.054408},
     url = {https://link.aps.org/doi/10.1103/PhysRevE.109.054408},
   }