Elowitz Lab
Published December 1, 2024 | Version v3
Dataset Open

Contextual computation by competitive protein dimerization networks

Creators

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Broad Institute

Description

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) can perform diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.

External Files

Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/kas2z-0fe41/</p>

equilibration_time.zip, 2.2 MB

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expression_noise_robustness.zip, 192.9 MB

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intrinsic_noise.zip, 2.3 MB

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optimization_trials_dualannealing_3D_4D.zip, 21.4 MB

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optimization_trials_randomK_1D.zip, 343.0 MB

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optimization_trials_randomK_1D_testing_connectivity.zip, 8.3 MB

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optimization_trials_randomK_2D.zip, 3.8 MB

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param_screen_1D.zip, 113.9 GB

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param_screen_1D_limited_param_range.zip, 23.0 GB

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param_screen_2D.zip, 137.6 GB

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param_screen_analysis_1D.zip, 1.7 GB

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param_screen_analysis_1D_limited_param_range.zip, 18.9 MB

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param_screen_analysis_2D.zip, 285.4 MB

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separation_of_timescales.zip, 945.3 MB

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supplemental_videos.zip, 1.5 MB

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transcription_factor_coexpression.zip, 464.2 kB

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Files

expressivity_paper_README.pdf

Files (486.1 MB)

Name Size
md5:2a8e83f8a53bacf95308329ba25f99aa
3.0 MB Download
md5:d5ec703ca307fdafae0568c5f5e82ae8
3.0 MB Preview Download
md5:69ebf4bbb0d947d06869311e5db22d01
18.8 MB Download
md5:84b71cae3fa81e01c7684e7a74519d4d
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md5:7999b18c097cdb6a117049d4bbdc6887
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md5:cd50edc4cbd3b04f40fe3903065a57a4
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md5:165d2c380e5b23690ee2722f8c811dc7
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Additional details

Identifiers

DOI
10.22002/1gffr-va537

Related works

Is described by
Text: 10.1101/2023.10.30.564854 (DOI)
Text: 10.1016/j.cell.2025.01.036 (DOI)

Dates

Created
2023-10-20
Updated
2024-12-04
Updated README