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

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

SUBMITTER: Hross S 

PROVIDER: S-EPMC6303801 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature