2014/06/10 by Britta Velten, Velten, Britta, Erkan Uenal +3
Biochemistry, Genetics and Molecular Biology · #Developmental Biology and Gene Regulation #FOS: Biological sciences #Pluripotent Stem Cells Research #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics #Tissues and Organs (q-bio.TO) #q-bio.QM #q-bio.TO
paper · pdf · doi:10.48550/arxiv.1406.2573
NOLTA 2014: 2014 Int'l Symposium on Nonlinear Theory & its Applications, to be held in Luzern, Switzerland from September 14-18, 2014
arxiv created 2014/06/10 · openalex publication_date 2014/06/10 · arxiv updated 2014/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Advances in imaging technology now provide us with detailed 3D data on gene expression patterns in developing embryos. This information can be used to build predictive mathematical models of embryogenesis. Current modelling approaches are, however, limited by lack of methods to automatically infer the regulatory networks and the parameter values from the image-based information. Here we make a first step to the development of such methods. We use limb bud development as a model system. For a given regulatory network we developed a decision tree based algorithm to automatically determine parameter values for which the model reproduces the expression patterns. Starting from this parameter set, local optimization was performed to further reduce the chosen goodness-of-fit measure. This approach allowed us to recover the target expression patterns, as judged by eye, and thus provides a first step towards the automated inference of parameter values for a given regulatory network.