Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

S. Hross, F.J. Theis, M.K. Sixt, J. Hasenauer, Journal of the Royal Society Interface 15 (2018).

Download
OA 1.46 MB

Journal Article | Published | English
Author
; ; ;
Department
Abstract
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.
Publishing Year
Date Published
2018-12-05
Journal Title
Journal of the Royal Society Interface
Volume
15
Issue
149
Article Number
20180600
ISSN
IST-REx-ID

Cite this

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
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, 15(149). https://doi.org/10.1098/rsif.2018.0600
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 15, no. 149 (2018). https://doi.org/10.1098/rsif.2018.0600.
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, 2018.
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).
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.
All files available under the following license(s):
Creative Commons License:
CC-BYCreative Commons Attribution 4.0 International Public License (CC-BY 4.0)
Main File(s)
File Name
Access Level
OA Open Access
Last Uploaded
2019-02-05T14:46:44Z


Export

Marked Publications

Open Data IST Research Explorer

Search this title in

Google Scholar