Leveraging experimental vasculature data for high-resolution brain tumor simulations.

Code ↔ Paper

11 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 11 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted

[1] § Materials and methods › Model parameters › Cell motility magnitude and persistence ↔ src/lib/action/cellorientation.cpp, lines 31 to 93 · score 0.83 · persistent random walk, Wiener process, random movement, random vector, magnitude, motility · Report a mismatch
[2] § Results and discussion › Vascular data processing for large-scale simulations › Microscopy stack grouping ↔ data/local_density_analysis/extract_local_vessel_properties.py, lines 63 to 126 · score 0.79 · removing single voxels, binary mask, vessel voxels, network length, convolution, erosion · Report a mismatch
[3] § Materials and methods › Model parameters › Cell motility magnitude and persistence ↔ src/lib/action/cellorientation.h, lines 22 to 73 · score 0.77 · persistent random walk, Wiener process, random movement, magnitude, motility, cell · Report a mismatch
[4] § Results and discussion › Influence of vessel network topology on tumor growth › Tumor growth behavior ↔ data/local_density_analysis/extract_local_vessel_properties.py, lines 63 to 126 · score 0.68 · vessel properties, tumor center, local density, network length, radius, simulations · Report a mismatch
[5] § Materials and methods › Model description ↔ src/lib/functions/energy/energyfactory.h, lines 31 to 120 · score 0.64 · cellular Potts model, individual cell, algorithms, stencil · Report a mismatch
[6] § Results and discussion › Influence of vessel network topology on tumor growth › Tumor growth behavior ↔ generate_figures.py, lines 269 to 313 · score 0.64 · network length fraction, local network length, local density, tumor cell, logistic, curve · Report a mismatch
[7] § Materials and methods › Model parameters › Cell division rate ↔ src/lib/action/celldivision.h, lines 34 to 88 · score 0.61 · randomly oriented, cell division, probability, surface, ID, properties · Report a mismatch
[8] § Materials and methods › Model parameters › Cell division rate ↔ src/lib/action/celldivision.cpp, lines 26 to 82 · score 0.59 · cell divides, cell division, oriented, probability, voxels, properties · Report a mismatch
[9] § Results and discussion › Vascular data processing for large-scale simulations › Processing into 3D volumes ↔ mousebrainsimulator/src/mousebrainsimulator/main.py, the whole file · a weak match · score 0.57 · binary mask, blood vessel, filter, locations, substacks, resolution · Report a mismatch
[10] § Materials and methods › Loading of blood vessel data into CiS ↔ mousebrainsimulator/src/mousebrainsimulator/main.py, the whole file · a weak match · score 0.56 · binary mask, blood vessel, workers, spaces, split, rank · Report a mismatch
[11] § Results and discussion › Influence of vessel network topology on tumor growth › Tumor growth behavior ↔ generate_figures.py, lines 269 to 313 · score 0.54 · network length fraction, local network length, tumor cell, logistic, curve, density · Report a mismatch

Paper

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The authors' code

Python · 126 lines · 4.7 KB · no license · 2 matches

import glob
import os
import shutil
import sys
import numpy as np
import json
import jsbeautifier
import pyvista as pv
from copy import deepcopy,copy
from skimage.morphology import skeletonize_3d,binary_dilation,binary_erosion,binary_opening
import matplotlib.pyplot as plt
from scipy.signal import convolve
def loadjson(filename):
with open(filename, "r") as f:
config = json.load(f)
return config
def processed_vtk_to_numpy(pv_obj):
dimensions = pv_obj.dimensions
return np.array(pv_obj["cells"]).reshape(dimensions)
def grid_3d_to_vti(data, filename="test_file.vti"):
data = data.astype(np.uint32)
grid = pv.ImageData()
grid.dimensions = data.shape
grid.origin = (0, 0, 0)
grid.spacing = (1, 1, 1) # (0.65, 0.65, 2)
grid["cells"] = data.ravel(order="C")
grid.save(filename, binary=True)
def mask_3d_array_within_radius(array, position, radius):
# Get the shape of the input array
x_size, y_size, z_size = array.shape
# Create a grid of coordinates for each index in the array
x, y, z = np.indices((x_size, y_size, z_size))
# Calculate the distance from the position (x0, y0, z0)
x0, y0, z0 = position
distance = (x - x0)**2
distance += (y - y0)**2 #np.sqrt((x - x0)**2 + (y - y0)**2 + (z - z0)**2)
distance += (z - z0)**2
distance = np.sqrt(distance)
# Create a mask where the distance is less than or equal to the radius
mask = distance <= radius
# Apply the mask to the array, values outside the radius will be 0
result = np.where(mask, array, 0)
return result, np.sum(mask)
#Define dirs
path_to_vti_dir = "../binary_masks"
simu_dir = "../simulations"
path_to_simus_run_file = "../chunks_to_simulate.txt"
N_per_dim = 2
N_simu_folders = int(N_per_dim**3)
box_padding=250#µm
with open(f"local_vessel_properties.csv","w") as f, open(path_to_simus_run_file, "r") as g:
f.write("#chunk name,folder nbr,vessel voxel count,skeleton voxel count,volume\n")
for line in g:
chunk_name = line[:-5]#Ignore "\n" at EoL
path_to_vti = f"{path_to_vti_dir}/{chunk_name}.vti"
for folder_nbr in range(N_simu_folders):
print(f"Analyzing simu {folder_nbr} of {chunk_name}")
path_to_simu_folder = f"{simu_dir}/{chunk_name}/{folder_nbr:05d}"
config = loadjson(f"{path_to_simu_folder}/config.json")
tumor_entry = config["Filling"]["cells"][1]
vessel_voxel_count = -1
network_length = -1
volume_of_surrounding = -1
tumor_center = copy(tumor_entry["center"])
tumor_center = (tumor_center[0],tumor_center[1],tumor_center[2]) ####<---- CAREFUL
box_start = [tumor_center[i] - box_padding for i in range(3)]
box_end = [tumor_center[i] + box_padding for i in range(3)]
print(box_start,box_end)
whole_vessel_structure = processed_vtk_to_numpy(pv.read(path_to_vti)) == 1
print(whole_vessel_structure.shape)
print("Getting surrounding")
tumor_surrounding = whole_vessel_structure[box_start[0]:box_end[0],box_start[1]:box_end[1],box_start[2]:box_end[2]]
tumor_surrounding_tmp, volume_of_surrounding = mask_3d_array_within_radius(tumor_surrounding, [tumor_center[i]-box_start[i] for i in range(3)], box_padding)
vessel_voxel_count = np.sum(tumor_surrounding_tmp)
print("Skeletonizing surrounding")
# Remove single voxels
print("Opening")
tumor_surrounding = binary_opening(tumor_surrounding)
for erosion_count in range(2):
print(f"Erosion nbr {erosion_count}")
tumor_surrounding = binary_erosion(tumor_surrounding)
tumor_surrounding = binary_opening(tumor_surrounding)
print("Building skeleton...")
tumor_surrounding_skeleton = skeletonize_3d(tumor_surrounding).astype(bool)
print("Skeleton built")
# Remove some remoining artifacts
print("Convoluting...")
kernel = np.ones((3,3,3),dtype= int)
convolution = convolve(tumor_surrounding_skeleton.astype(int), kernel)[1:-1,1:-1,1:-1] * tumor_surrounding_skeleton
tumor_surrounding_skeleton = (convolution == 2) | (convolution == 3)
print("Done")
tumor_surrounding_skeleton, _ = mask_3d_array_within_radius(tumor_surrounding_skeleton, [tumor_center[i]-box_start[i] for i in range(3)], box_padding)
network_length = np.sum(tumor_surrounding_skeleton)
f.write(f"{chunk_name},{folder_nbr:05d},{vessel_voxel_count:1.5f},{network_length:1.5f},{volume_of_surrounding:1.5f}\n")

extract_local_vessel_properties.py at commit c9d7e6d, no license · at the source

Overview

Authors: Eric Behle¹, Julian Herold², Alexander Schug¹

ORCID iDs: Alexander Schug

Institutions: Forschungszentrum Jülich (Germany); Jülich Supercomputing Centre (Germany); Karlsruhe Institute of Technology (Germany)

Journal: Biophysical journal, volume 125, issue 7, pages 1701-1712

Dates: received 25 June 2025; accepted 3 March 2026; published online 25 March 2026; in print 7 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.bpj.2026.03.005 · PMID 41887211 · PMCID PMC13351720 · OpenAlex W7140274101

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: mouse (organism), other condition (population), computational (subfield)

MeSH: Blood Vessels*, Brain Neoplasms*, Computer Simulation*, Models, Biological*, Neovascularization, Pathologic*, Animals, Brain, Cell Proliferation, Mice (* major topic)

Topic: Angiogenesis and VEGF in Cancer (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Gauss Centre for Supercomputing; Helmholtz Association; National Institute of Corrections

Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

Cancer remains a leading cause of mortality. Multidisciplinary studies probe its complex pathology to increase treatment options. Computational modeling of tumor growth on high-performance computing resources offers microscopic insight into its progress and a valuable avenue for advancing our understanding. However, the effective initialization and parameterization of the underlying models require high-resolution data from real tissue structures. Here, we leveraged high-performance computing resources and a massive data set of a mouse brain’s entire vascular network. We processed these image stacks into detailed three-dimensional representations, identified brain regions of interest, and conducted a series of large-scale simulations to investigate how tumor growth is influenced by local vascular network characteristics. By simulating tumor growth with subcellular resolution, we can probe to which extent vessel density and vessel network length influence tumor growth. We determined that vessel density is more likely to affect the growth rate than the network length. Finally, our results allowed us to extrapolate tumor cell growth predictions for the entire mouse brain, highlighting the critical role of vascular topology in tumor progression. Such increasingly realistic simulations of cancer cells and their microenvironment enable researchers to increasingly bridge the gap between basic biology and clinical practice, ultimately supporting the development of more effective personalized cancer therapies.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

nastja/nastja

License: MPL-2.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: d621cd35c5ce10d2cca11d72d91d29e2fc0a9e27, 5 March 2026

Languages: C/C++ (231), C++ (133), Shell (28), Python (17), CUDA (5), C (5)

Size: 679 files, 419 scripts

Software Heritage: archived

Found in: “Data and code availability”

Holds: README, license file, tests, continuous integration, documentation

Not found: CITATION.cff, environment file

Tools: NumPy (9 files), Matplotlib (2 files), pandas (2 files), SciPy (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

421 files

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LICENSE, License, 373 lines
README.md, Text, 69 lines

At the source: gitlab.com/nastja/nastja

gitlab.jsc.fz-juelich.de/behle2/mousebrainvasculaturepaper

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: c9d7e6dfc3f1fd4f31748f59011bf540ee83a9d9, 26 May 2025

Languages: Python (14), Shell (5)

Size: 76 files, 19 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README, environment (mousebrainsimulator/pyproject.toml, mousebrainsimulator/setup.cfg, mousebrainsimulator/setup.py)

Not found: license file, CITATION.cff, tests, continuous integration, documentation

Tools: NumPy (9 files), Matplotlib (5 files), scikit-image (4 files), SciPy (4 files), pandas (2 files), scikit-learn (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

20 files

data/generate_simu_folders.py, Python, 107 lines
data/get_all_final_csvs.py, Python, 10 lines
data/global_density_analysis/extract_global_density_params.py, Python, 46 lines
data/global_density_analysis/extract_global_density_params_refined.py, Python, 68 lines
data/local_density_analysis/extract_local_vessel_properties.py, Python, 126 lines, 2 matches
data/run_all_simulations.sh, Shell, 18 lines
data/simulation_final_cellcounts/unzip_csvs.sh, Shell, 10 lines
data/simulation_source_config/run_job.sh, Shell, 17 lines
generate_figure_data.py, Python, 144 lines
generate_figures.py, Python, 390 lines, 2 matches
mousebrainsimulator/run_whole_pipeline.py, Python, 2 lines
mousebrainsimulator/setup.py, Python, 6 lines
mousebrainsimulator/src/mousebrainsimulator/__init__.py, Python, 1 line
mousebrainsimulator/src/mousebrainsimulator/io.py, Python, 105 lines
mousebrainsimulator/src/mousebrainsimulator/main.py, Python, 151 lines, 2 matches
mousebrainsimulator/src/mousebrainsimulator/process_raw_files.py, Python, 581 lines
mousebrainsimulator/src/mousebrainsimulator/rawdata_processing/__init__.py, Python, 1 line
perform_entire_analysis.sh, Shell, 32 lines
perform_figure_generation.sh, Shell, 15 lines
README.md, Text, 41 lines

At the source: gitlab.jsc.fz-juelich.de/behle2/mousebrainvasculaturepaper

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
438 scripts, each with its path and the digest of its content;
11 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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

Data and code availability

The code of CiS can be found at https://gitlab.com/nastja/nastja.

The data and analysis scripts required for reproducing our analysis can be found at https://gitlab.jsc.fz-juelich.de/behle2/mousebrainvasculaturepaper.

Due to the size of the entire simulation data (roughly 1.1 TB), we only include the last output file of each simulation within this repository.

The full data are available upon reasonable request.

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, language, journal, volume, issue, pages, dates, 3 authors, 9 MeSH terms, 3 funders, 29 references.

Cite

This paper

Behle, E., Herold, J., & Schug, A. (2026). Leveraging experimental vasculature data for high-resolution brain tumor simulations. Biophysical journal, 125(7), 1701-1712. https://doi.org/10.1016/j.bpj.2026.03.005

BibTeX

@article{behle2026leveraging, author = {Behle, Eric and Herold, Julian and Schug, Alexander}, title = {{Leveraging experimental vasculature data for high-resolution brain tumor simulations}}, journal = {Biophysical journal}, year = {2026}, month = mar, volume = {125}, number = {7}, pages = {1701--1712}, publisher = {The Biophysical Society}, issn = {0006-3495}, doi = {10.1016/j.bpj.2026.03.005}, url = {https://doi.org/10.1016/j.bpj.2026.03.005}, pmid = {41887211}, pmcid = {PMC13351720} }

RIS

TY - JOUR AU - Behle, Eric AU - Herold, Julian AU - Schug, Alexander TI - Leveraging experimental vasculature data for high-resolution brain tumor simulations T2 - Biophysical journal J2 - Biophys J PY - 2026 DA - 2026/03/25 VL - 125 IS - 7 SP - 1701 EP - 1712 SN - 0006-3495 PB - The Biophysical Society DO - 10.1016/j.bpj.2026.03.005 UR - https://doi.org/10.1016/j.bpj.2026.03.005 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.bpj.2026.03.005", "type": "article-journal", "title": "Leveraging experimental vasculature data for high-resolution brain tumor simulations", "container-title": "Biophysical journal", "author": [ { "family": "Behle", "given": "Eric" }, { "family": "Herold", "given": "Julian" }, { "family": "Schug", "given": "Alexander" } ], "container-title-short": "Biophys J", "volume": "125", "issue": "7", "page": "1701-1712", "DOI": "10.1016/j.bpj.2026.03.005", "PMID": "41887211", "PMCID": "PMC13351720", "ISSN": "0006-3495", "publisher": "The Biophysical Society", "URL": "https://doi.org/10.1016/j.bpj.2026.03.005", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 25 ] ] } }

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