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Notch mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or receiving roles

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § Results › A bias in apical size prefigures transcriptional onset within clusters ↔ MultivariateModels.ipynb, lines 30 to 39 · score 0.69 · logistic regression, random forests, multivariate models, splits, classification · Report a mismatch
  2. [2] § Materials & Methods › Quantification and Statistical Analysis › Area fold-change analysis ↔ FoldChangeNCAreas.ipynb, the whole file · a weak match · score 0.66 · Fold change, transcribing NCs, 2.5 min, apical area, windows · Report a mismatch
  3. [3] § Materials & Methods › Quantification and Statistical Analysis › Modelling ↔ ImposedPerimeter/RunImposedPerimeterModel.m, lines 15 to 59 · score 0.62 · inhibiting activator expression, Hill coefficients, Notch, spl · Report a mismatch
  4. [4] § Materials & Methods › Quantification and Statistical Analysis › Modelling ↔ ImposedPerimeter/RunImposedPerimeterModel.m, lines 15 to 59 · score 0.59 · Hill coefficients, Hill function, Delta, inhibiting, thresholds, Activated · Report a mismatch
  5. [5] § Materials & Methods › Quantification and Statistical Analysis › Modelling ↔ SOP_DefaultParams_rho_adapted_weighted.m, lines 5 to 94 · score 0.57 · Hill coefficients, Hill function, Delta, inhibiting, thresholds, Activated · Report a mismatch
  6. [6] § Results › Lateral inhibition model incorporating cell perimeter and tension-differences can replicate signalling properties ↔ SOP_DefaultParams_rho_adapted_weighted.m, lines 5 to 94 · score 0.57 · cis inhibition, lateral inhibition, dimensional, connectivity, weighted, trans · Report a mismatch
  7. [7] § Materials & Methods › Quantification and Statistical Analysis › Cross-correlation analysis ↔ CrossCorrelationsOnCombinedData.m, lines 1 to 7 · score 0.56 · NC apical area, NB apical area, correlation, Cross, transcribing · Report a mismatch
  8. [8] § Results › Lateral inhibition model incorporating cell perimeter and tension-differences can replicate signalling properties ↔ DynamicPerimeter/IterateOverDifferentThresholdsDynamicPerimeterModelForPaper.m, lines 176 to 180 · score 0.54 · success probability, perimeter model, initial perimeter, simulation, dynamic · Report a mismatch

Paper

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The authors' code

MATLAB · 166 lines · 5.1 KB · AGPL-3.0 · 2 matches

  1. % Script that runs imposed perimeter model shown in Figure 6 for a specific
  2. % parameter choice
  4. % Adapted from Troost et al., 2023
  6. %% RUN SCRIPT for SOP Weighted Lateral Inhibition Model
  8. clear; close all; clc;
  10. %% Model setup
  12. model.ratio = 'rho';
  13. model.zone = 'Gaussian-like proneural genes';
  15. %% Simulation parameters
  16. params = SOP_DefaultParams_rho_adapted_weighted(model.zone);
  18. % Lattice geometry
  19. params.P = 8; % rows
  20. params.Q = 8; % columns
  21. params.Tmax = 20; % simulation duration
  23. % Control SOP patterning
  24. % params.nSOP = 2; % number of SOPs
  25. % params.minDist = 3; % minimum distance (in graph hops)
  27. % Parameter scaling
  28. params.beta.d = 1.62; % base Delta production
  29. params.beta.E = 1.62; % base E(spl) production %%% NOTE %%%
  30. params.beta.n = 1.52; % base Notch production
  32. % Transcription and thresholds
  33. params.T.E = 0.0051; % Hill function threshold for inhibitng activator expression by E(spl)
  34. % params.c.s = 2; % Hill coefficient for promoting E(spl) expression by trans-activation signal %%% NOTE %%%
  35. params.c.E = 3; % Hill coefficient for inhibitng activator expression by E(spl) %%% NOTE %%%
  36. params.Kappa.t = 0.8; % transactivation rate
  37. params.Kappa.InhCombined = 0.4; % base cis interaction scaling %%% NOTE %%%
  39. k = params.P * params.Q;
  41. % Perimeter and delamination/enhancement
  42. rng(21)
  43. params.perimeter = 1 + 0.1.*2.*(rand(k,1) - 0.5);
  44. params.perimeter(params.SOP.cells) = 0.85; % smaller center cell
  47. params.delam.trigger = 'time'; % (default: no event)
  48. params.delam.time = 0; % (model time units) -- used if trigger='time'
  49. params.delam.E_threshold = 0.015; % (used if trigger='E', compares E > threshold)
  50. params.delam.SOP_new_perimeter = 0.05;
  51. params.delam.Ecell_new_perimeter = 1.4; %% Note: this is fold change (multiplied with p0) so the differences maintain
  52. params.delam.smooth = true; %true;
  53. params.delam.duration = 10;
  55. params.beta.A = 1.62; % base Activator production rate (1.62 in paper)
  56. params.c.s = 3; % Hill coefficient for promoting E(spl) expression by trans-activation signal
  57. params.T.s = 0.8; % Hill function threshold for promoting E(spl) expression by trans-activation signal
  58. params.cis_size_exponent = 1; % how much smaller cells increase cis inhibition
  59. params.trans_size_exponent = 3; % trans interaction scaling
  61. %% INITIAL CONDITIONS
  62. y0 = SOP_InitialConditions_rho_adapted_weighted(model.zone, params, k);
  63. %
  64. %% RUN SIMULATION
  65. fprintf('\nRunning SOP weighted lateral inhibition simulation...\n\n');
  66. [yout, tout, params] = SOP_multicell_LI_adapted_weighted_with_trigger_PerimSave(model, params, y0);
  68. %% Map model time to real seconds
  69. real_time_per_model_unit = 150; % 1 model unit = 150s
  71. t_real = tout * real_time_per_model_unit;
  73. % Optional: resample every 30 s (like your experimental frames)
  74. frame_interval = 150; % seconds
  75. t_uniform = 0:frame_interval:max(t_real);
  76. y_uniform = interp1(t_real, yout, t_uniform, 'pchip');
  79. %%
  80. figure(10); clf; hold on;
  82. tmin = t_uniform/60;
  83. SOPcells = params.SOP.cells(:);
  85. % Extract blocks
  86. D_SOP = y_uniform(:, SOPcells); % Delta
  87. DA_SOP = y_uniform(:, k + SOPcells); % Activated Delta
  88. N_SOP = y_uniform(:, 2*k + SOPcells); % Notch
  89. E_SOP = y_uniform(:, 3*k + SOPcells); % E(spl)
  90. Aac_SOP = y_uniform(:, 4*k + SOPcells); % Activator
  92. % Colors
  93. cD = [0 0.4 1];
  94. cDA = [0.1 0.7 1];
  95. cN = [0.8 0.2 0];
  96. cE = [0.6 0 0.8];
  97. cA = [0 0.6 0];
  99. % Plot mean ± SEM
  100. shadedMeanPlot(tmin, D_SOP, cD, 'Delta');
  101. shadedMeanPlot(tmin, DA_SOP, cDA, 'Activated Delta');
  102. shadedMeanPlot(tmin, N_SOP, cN, 'Notch');
  103. shadedMeanPlot(tmin, E_SOP, cE, 'E(spl)');
  104. shadedMeanPlot(tmin, Aac_SOP, cA, 'Activator');
  106. legend('Location','best');
  107. xlabel('Time (min)');
  108. ylabel('Level (mean ± SD)');
  109. title('SOP cells');
  110. xlim([0 max(tmin)]);
  111. ylim([0 1.8]);
  112. saveas(figure(10),'NB_levels.pdf')
  113. %%
  114. figure(12); clf; hold on;
  115. NNcells = params.SOP.nearest_neighbors(:);
  118. % Extract blocks
  119. D_NN = y_uniform(:, NNcells);
  120. DA_NN = y_uniform(:, k + NNcells);
  121. N_NN = y_uniform(:, 2*k + NNcells);
  122. E_NN = y_uniform(:, 3*k + NNcells);
  123. Aac_NN = y_uniform(:, 4*k + NNcells);
  125. % Plot mean ± SEM
  126. shadedMeanPlot(tmin, D_NN, cD, 'Delta');
  127. shadedMeanPlot(tmin, DA_NN, cDA, 'Activated Delta');
  128. shadedMeanPlot(tmin, N_NN, cN, 'Notch');
  129. shadedMeanPlot(tmin, E_NN, cE, 'E(spl)');
  130. shadedMeanPlot(tmin, Aac_NN, cA, 'Activator');
  132. legend('Location','best');
  133. xlabel('Time (min)');
  134. ylabel('Level (mean ± SD)');
  135. title('Nearest neighbour cells');
  136. xlim([0 max(tmin)]);
  137. ylim([0 1.8]);
  138. saveas(figure(12),'NN_levels.pdf')
  139. %%
  140. figure(14); clf; hold on;
  142. % Plot mean ± SEM
  143. shadedMeanPlot(tmin, E_SOP, 'g', 'E(spl) SOP');
  144. shadedMeanPlot(tmin, E_NN, 'b', 'E(spl) NN');
  146. legend('Location','best');
  147. xlabel('Time (min)');
  148. ylabel('Level (mean ± SD)');
  149. title('E(spl) levels');
  150. xlim([0 max(tmin)]);
  151. ylim([0 0.15]);
  152. % hold on
  153. % x1 =[5 5];
  154. % y1 = [0 0.25];
  155. % plot(x1,y1,'--k')
  156. saveas(figure(14),'E_levels.pdf')
  158. figure; hold on
  159. yyaxis right
  160. shadedMeanPlot(tmin, E_SOP, 'g', 'E(spl) SOP');
  161. shadedMeanPlot(tmin, E_NN, 'b', 'E(spl) NN');
  162. ylim([0 0.18])
  164. yyaxis left
  165. ylim([0 1.6])
  166. saveas(figure,'E_levels_and_perimeters.pdf')

RunImposedPerimeterModel.m at commit 10955e7, under AGPL-3.0 · at the source

Overview

Authors: Prachi Richa1, Charalambos Roussos1, Chengxi Zhu2, Martin O. Lenz2, Shahar Kasirer3, David Sprinzak4, Sarah Bray1
ORCID iDs: Prachi Richa, Martin O. Lenz, Sarah Bray
  1. Department of Physiology Development and Neuroscience, and University of Cambridge, Downing Street, Cambridge, CB2 3DY
  2. Cambridge Advanced Imaging Centre, University of Cambridge, Downing Street, Cambridge, CB2 3DY
  3. School of Physics and Astronomy, and Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, 69978, Israel
  4. School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, 69978, Israel
Institutions: University of Cambridge (United Kingdom); Tel Aviv University (Israel)
Journal: bioRxiv (preprint)
Dates: published online 28 March 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.64898/2026.03.27.712410 · OpenAlex W7141848586
Open access: green, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: cellular / molecular (subfield)
Methods: Preprocessing, Statistics, Machine learning, Evoked potentials, fMRI & imaging
Topic: Developmental Biology and Gene Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Wellcome Trust (212936/Z/18/Z)
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

During neurogenesis, neuroblasts are selected from proneural-competent cells through lateral inhibition, a process controlled by the evolutionarily conserved Notch signalling pathway. By tracking transcription from Notch-target genes and cell morphologies in real time, we discovered that the presumptive neuroblast never initiates target-gene transcription. This implies a pre-existing bias directs Notch signalling. The bias correlates with a heterogeneity in apical cell areas which is further reinforced during neuroblast selection. Additionally, the length and duration of neuroblast-neighbour cell contacts predict the likelihood of transcription. Using mathematical modelling we show that lateral inhibition seeded with subtle morphological differences can bias cells toward signal-sending or receiving roles before transcriptional feedback occurs. Notch activation further alters apical cell area, reinforcing the initial bias. We propose that signalling and cell mechanics work together to ensure the robust selection of a single neural precursor.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

crou607/neurogenesis_analysis

License: AGPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 10955e7fc55709e180c1b6e4711ac2800f277a3e, 9 June 2026
Languages: MATLAB (18), Jupyter (8)
Size: 28 files, 26 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, 8 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (8 files), pandas (8 files), NumPy (5 files), seaborn (5 files), Statistics and Machine Learning Toolbox (4 files), scikit-learn (4 files), SciPy (4 files), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files
At the source: github.com/crou607/neurogenesis_analysis.git

The paper's code and data availability statement is in the Data section.

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Data

No dataset and no data link were found in the paper.

Data and code availability

All code used for the above-mentioned analyses and modelling can be found at: https://github.com/crou607/neurogenesis_analysis.git

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, journal, dates, 7 authors, 1 funder, 44 references.

Cite

This paper

Richa, P., Roussos, C., Zhu, C., Lenz, M. O., Kasirer, S., Sprinzak, D., & Bray, S. (2026). Notch mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or receiving roles. bioRxiv (preprint). https://doi.org/10.64898/2026.03.27.712410

BibTeX

@article{richa2026notch,
author = {Richa, Prachi and Roussos, Charalambos and Zhu, Chengxi and Lenz, Martin O. and Kasirer, Shahar and Sprinzak, David and Bray, Sarah},
title = {{Notch mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or receiving roles}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.27.712410},
url = {https://doi.org/10.64898/2026.03.27.712410}
}

RIS

TY - JOUR
AU - Richa, Prachi
AU - Roussos, Charalambos
AU - Zhu, Chengxi
AU - Lenz, Martin O.
AU - Kasirer, Shahar
AU - Sprinzak, David
AU - Bray, Sarah
TI - Notch mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or receiving roles
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/28
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.27.712410
UR - https://doi.org/10.64898/2026.03.27.712410
ER -

CSL-JSON

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"id": "10.64898/2026.03.27.712410",
"type": "article",
"title": "Notch mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or receiving roles",
"container-title": "bioRxiv (preprint)",
"author": [
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"family": "Richa",
"given": "Prachi"
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"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.27.712410",
"ISSN": "2692-8205",
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"URL": "https://doi.org/10.64898/2026.03.27.712410",
"issued": {
"date-parts": [
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2026,
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}
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