Spatial gene expression quantification in changing morphologies

In systems biology, an organisms’ behavior is explained from the interactions among individual components such as genes and proteins. With few exceptions, interactions among genes and proteins are not measured directly and are therefore inferred from the observed output of a biological system. A network of interacting genes and proteins is approximated with computational models. A computational model for a specific biological system predicts various properties of this system, and can be validated with experimental measurements of these properties.
This thesis describes a method for quantifying spatial gene expression in the starlet sea anemone Nematostella vectensis from microscopy pictures. By mapping the changing shape of developing embryos to a standardized format, spatial gene expression patterns from various developmental stages are compared. Moreover, standardized spatial gene expression patterns at specific time points can serve as input for dynamic computational models of gene regulation. Finally, various mechanisms for the development of Nematostella vectensis are proposed based on statistical analyses of a database for spatial gene expression.