Data from: An investigation of the channel capacity of bacterial chemotactic sensors for low chemoattractant concentrations
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
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7D_Sweep_Results.npz
80.25 MB
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README.md
2.11 KB
Jul 22, 2026 version files 90.09 MB
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7D_Sweep_Results_keymer.npz
9.84 MB
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7D_Sweep_Results.npz
80.25 MB
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README.md
2.56 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. Across a seven-dimensional parameter sweep, we compute three sensing-performance metrics —channel capacity, dynamic range, and effective Hill coefficient—in the limit where the cells are constantly at such low concentrations of chemoattractant that they need not adapt to new baseline chemoattractant concentration levels. What results are upper bounds on a more complicated trajectory mutual information rate, a quantitative understanding of the tight connection between channel capacity and dynamic range, and the finding that in this regime channel capacity is well described by a closed-form ceiling depending only on the receptor's baseline activity, which every wild-type and mutant strain in our sample achieves to within a few percent. In more realistic scenarios, adaptation plays a larger role and the exact temporal dynamics of chemoattractant concentrations seen by bacteria as they swim. This manuscript thus points to the importance of mapping out naturalistic chemoattractant concentration statistics in the wild as has been done for natural scene statistics.
Dataset DOI: 10.5061/dryad.wpzgmsc3j
Description of the data and file structure
This data was produced in two 7D parameter sweeps in service of the paper attached.
Files
7D_Sweep_Results.npz
Contains results from a 7-dimensional parameter sweep using the K_d values of Mello & Tu (2005), PNAS 102:17354.
7D_Sweep_Results_keymer.npz
Contains results from a 7-dimensional parameter sweep using the K_d values of the Wingreen-lab papers: Keymer et al. (2006), PNAS 103:1786; Endres & Wingreen (2006), PNAS 103:13040; Hansen, Endres & Wingreen (2008), PLoS Comput Biol 4:e1; and Clausznitzer et al. (2010), PLoS Comput Biol 6:e1000784.
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 7D_Sweep_Results.npz, 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
For 7D_Sweep_Results_keymer.npz, grid lengths are:
len(L0_grid)=7len(KdI1_grid)=8len(KdA1_grid)=8len(KdI2_grid)=7len(KdA2_grid)=7len(N_tar_grid)=10len(N_tsr_grid)=10
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: bacterial_chemotaxis.py
Reuse instructions
Place both NPZ files in the same directory as bacterial_chemotaxis.py, then run:
python3 bacterial_chemotaxis.py --all
Changes after Mar 3, 2026: Added 7D_Sweep_Results_keymer.npz, a second 7D parameter sweep covering the Wingreen-lab K_d region used in the revised paper. Updated README.md to describe both NPZ files.
