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Understanding the self-regulatory mechanisms controlling the spatial and temporal structure of multicellular organisms represents one of the major challenges in molecular biology. Although high-throughput data have become available with the advances in experimental technologies at a large scale, measuring gene expression levels at a high spatial resolution remains extremely difficult. As a result, the study of genetic regulatory networks in the light of spatial expression patterns still relies mainly on qualitative data. This leads to the question of how to fit the parameters of a gene regulatory network model such that a purely qualitatively defined pattern can be reproduced. This article addresses this issue and presents a general approach to generate patterns reflecting basic geometric shapes. In combination with an appropriate ordinary differential equation (ODE)-based modeling and simulation framework, a formalism to quantify qualitative patterns and integrate this concept into an evolutionary algorithm for parameter estimation is presented and tested for stripe-like patterns on two test systems.