Data from: Apparent selection pressure for dynamic range and channel capacity in bacterial chemotactic sensors
Data files
Mar 03, 2026 version files 80.25 MB
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7D_Sweep_Results.npz
80.25 MB
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README.md
2.10 KB
Mar 03, 2026 version files 80.25 MB
-
7D_Sweep_Results.npz
80.25 MB
-
README.md
2.11 KB
Abstract
Bacterial chemotactic sensing converts noisy chemical signals into running and tumbling. We analyze the static sensing limits of mixed Tar/Tsr chemoreceptor clusters in individual Escherichia coli cells using a heterogeneous Monod–Wyman–Changeux (MWC) model. By sweeping a seven-dimensional parameter space, we compute three sensing performance metrics—channel capacity, dynamic range, and effective Hill coefficient. Across E. coli–like parameter regimes, we consistently observe pronounced global maxima of channel capacity and global maxima of the related dynamic range, whereas the effective Hill coefficient does not exhibit comparable optimization. The capacity-achieving input distribution is bimodal, which implies that individual cells maximize information by sampling both low- and high-concentration regimes. Together, these results suggest that, at the individual-cell level, channel capacity and dynamic range may be selected for in E. coli receptor clusters.
Dataset DOI: 10.5061/dryad.wpzgmsc3j
Description of the data and file structure
This data was produced in a 7D parameter sweep in service of the preprint attached.
File
7D_Sweep_Results.npz
Contains results from a 7-dimensional parameter sweep of a bacterial chemotaxis receptor model.
Parameter grids (independent variables; stored as 1D arrays)
L0_grid(dimensionless)KdI1_grid(mM)KdA1_grid(mM)KdI2_grid(mM)KdA2_grid(mM)N_tar_grid(dimensionless, effective receptor count)N_tsr_grid(dimensionless, effective receptor count)
For this submitted dataset, grid lengths are:
len(L0_grid)=17len(KdI1_grid)=7len(KdA1_grid)=7len(KdI2_grid)=7len(KdA2_grid)=7len(N_tar_grid)=14len(N_tsr_grid)=13
Output variables
C_bits(bits): channel capacitynH(dimensionless): effective Hill coefficientDR_out(dimensionless): absolute dynamic range,|p_inf - p_0|DR_p(dimensionless): signed dynamic range,p_inf - p_0c50(mM): ligand concentration at midpoint activity
Additional variables
done_mask(bool, same 7D shape):Truewhere that grid point was computediters(int32, same 7D shape): Blahut-Arimoto iterations used at each pointcursor(int): flattened index for resumable sweep progresscomplete(bool): whether the entire sweep is completemeta(dict): metadata bundle; includes:bio_dots: biological reference points used in analysisanchor: anchor point used in grid construction
Code/software
Repository: https://github.com/Hail-Earendil/bacterial-chemotaxis-7d-sweep
Main script: 7D_Sweep_Code.py
Code version used to generate this dataset: commit b2c550913ad616b405a2af82d959e2d42eb7da20
Reuse instructions
Place the NPZ at:
outputs/<RUN_TAG>/7D_Sweep_Results.npz
Then run 7D_Sweep_Code.py:
export SWEEP_RUN_TAG=<RUN_TAG>
python3 7D_Sweep_Code.py
