  Building biological models by inferring functional dependencies from
experimental data is an im- portant 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 ap- proaches
often yield a single model only, rely on specific assumptions, and/or use
dedicated, heuris- tic algorithms that are intolerant to changing circumstances
or requirements in the view of the rapid progress made in Biotechnology. Our
aim is to provide a declarative solution to the problem by ap- peal to Answer
Set Programming (ASP) overcoming these difficulties. We build upon an existing
approach to Automatic Network Reconstruction proposed by part of the authors.
This approach has firm mathematical foundations and is well suited for ASP due
to its combinatorial flavor providing a characterization of all models
explaining a set of experiments. The usage of ASP has several ben- efits over
the existing heuristic algorithms. First, it is declarative and thus
transparent for biological experts. Second, it is elaboration tolerant and thus
allows for an easy exploration and incorporation of biological constraints.
Third, it allows for exploring the entire space of possible models. Finally,
our approach offers an excellent performance, matching existing,
special-purpose systems.

Machine Learning