--- _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' ...