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Building biological models by inferring functional dependencies from experimental data is an important issue in molecular biology. To relieve the biologist from this traditionally manual process, various approaches have been proposed to increase the degree of automation. However, available approaches often yield a single model only, rely on specific assumptions, and/or use dedicated, heuristic algorithms that are intolerant to changing circumstances or requirements in the view of the rapid progress made in biotechnology. The authors' aim is to provide a declarative solution to the problem by appeal to Answer Set Programming (ASP) overcoming these difficulties. They build upon an existing approach to Automatic Network Reconstruction proposed by part of others.
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