---
_id: '5858'
abstract:
- lang: eng
text: Spatial patterns are ubiquitous on the subcellular, cellular and tissue level,
and can be studied using imaging techniques such as light and fluorescence microscopy.
Imaging data provide quantitative information about biological systems; however,
mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal
mathematical modelling has helped to overcome this problem. Yet, outliers and
structured noise limit modelling of whole imaging data, and models often consider
spatial summary statistics. Here, we introduce an integrated data-driven modelling
approach that can cope with measurement artefacts and whole imaging data. Our
approach combines mechanistic models of the biological processes with robust statistical
models of the measurement process. The parameters of the integrated model are
calibrated using a maximum-likelihood approach. We used this integrated modelling
approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21).
CCL21 gradients guide dendritic cells and are important in the adaptive immune
response. Using artificial data, we verified that the integrated modelling approach
provides reliable parameter estimates in the presence of measurement noise and
that bias and variance of these estimates are reduced compared to conventional
approaches. The application to experimental data allowed the parametrization and
subsequent refinement of the model using additional mechanisms. Among other results,
model-based hypothesis testing predicted lymphatic vessel-dependent concentration
of heparan sulfate, the binding partner of CCL21. The selected model provided
an accurate description of the experimental data and was partially validated using
published data. Our findings demonstrate that integrated statistical modelling
of whole imaging data is computationally feasible and can provide novel biological
insights.
article_number: '20180600'
article_processing_charge: No
author:
- first_name: Sabrina
full_name: Hross, Sabrina
last_name: Hross
- first_name: Fabian J.
full_name: Theis, Fabian J.
last_name: Theis
- first_name: Michael K
full_name: Sixt, Michael K
id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87
last_name: Sixt
orcid: 0000-0002-6620-9179
- first_name: Jan
full_name: Hasenauer, Jan
last_name: Hasenauer
citation:
ama: Hross S, Theis FJ, Sixt MK, Hasenauer J. Mechanistic description of spatial
processes using integrative modelling of noise-corrupted imaging data. Journal
of the Royal Society Interface. 2018;15(149). doi:10.1098/rsif.2018.0600
apa: Hross, S., Theis, F. J., Sixt, M. K., & Hasenauer, J. (2018). Mechanistic
description of spatial processes using integrative modelling of noise-corrupted
imaging data. Journal of the Royal Society Interface. Royal Society Publishing.
https://doi.org/10.1098/rsif.2018.0600
chicago: Hross, Sabrina, Fabian J. Theis, Michael K Sixt, and Jan Hasenauer. “Mechanistic
Description of Spatial Processes Using Integrative Modelling of Noise-Corrupted
Imaging Data.” Journal of the Royal Society Interface. Royal Society Publishing,
2018. https://doi.org/10.1098/rsif.2018.0600.
ieee: S. Hross, F. J. Theis, M. K. Sixt, and J. Hasenauer, “Mechanistic description
of spatial processes using integrative modelling of noise-corrupted imaging data,”
Journal of the Royal Society Interface, vol. 15, no. 149. Royal Society
Publishing, 2018.
ista: Hross S, Theis FJ, Sixt MK, Hasenauer J. 2018. Mechanistic description of
spatial processes using integrative modelling of noise-corrupted imaging data.
Journal of the Royal Society Interface. 15(149), 20180600.
mla: Hross, Sabrina, et al. “Mechanistic Description of Spatial Processes Using
Integrative Modelling of Noise-Corrupted Imaging Data.” Journal of the Royal
Society Interface, vol. 15, no. 149, 20180600, Royal Society Publishing, 2018,
doi:10.1098/rsif.2018.0600.
short: S. Hross, F.J. Theis, M.K. Sixt, J. Hasenauer, Journal of the Royal Society
Interface 15 (2018).
date_created: 2019-01-20T22:59:18Z
date_published: 2018-12-05T00:00:00Z
date_updated: 2023-09-13T08:55:05Z
day: '05'
ddc:
- '570'
department:
- _id: MiSi
doi: 10.1098/rsif.2018.0600
external_id:
isi:
- '000456783800011'
file:
- access_level: open_access
checksum: 56eb4308a15b7190bff938fab1f780e8
content_type: application/pdf
creator: dernst
date_created: 2019-02-05T14:46:44Z
date_updated: 2020-07-14T12:47:13Z
file_id: '5925'
file_name: 2018_Interface_Hross.pdf
file_size: 1464288
relation: main_file
file_date_updated: 2020-07-14T12:47:13Z
has_accepted_license: '1'
intvolume: ' 15'
isi: 1
issue: '149'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '12'
oa: 1
oa_version: Published Version
publication: Journal of the Royal Society Interface
publication_identifier:
issn:
- '17425689'
publication_status: published
publisher: Royal Society Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Mechanistic description of spatial processes using integrative modelling of
noise-corrupted imaging data
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 15
year: '2018'
...