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Automatic network reconstruction using ASP

机译：使用ASP进行自动网络重建

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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. Our aim is to provide a declarative solution to the problem by appeal 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 benefits 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.
机译：通过从实验数据中推断功能依赖性来建立生物学模型是分子生物学中的一个重要问题。为了使生物学家摆脱这种传统的手动过程，已提出了各种方法来提高自动化程度。然而，鉴于生物技术的迅速发展，可用的方法通常仅产生单个模型，依赖于特定的假设，和/或使用专用的启发式算法，这些算法不能适应不断变化的情况或要求。我们的目标是通过克服这些困难的答案集编程（ASP）来提供一种声明性的解决方案。我们以部分作者提出的现有的自动网络重建方法为基础。这种方法具有牢固的数学基础，并且由于其组合风格提供了解释所有实验的所有模型的特征，因此非常适合ASP。与现有的启发式算法相比，ASP的使用有很多好处。首先，它是声明性的，因此对生物学专家而言是透明的。其次，它具有精巧的制作能力，因此可以轻松探索并纳入生物学限制。第三，它允许探索可能模型的整个空间。最后，我们的方法可提供出色的性能，可与现有的专用系统相匹配。

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