We build upon an existing approach to Automatic Network Reconstruction (see”An algorithmic framework for network reconstruction” by Durzinsky et. al. and “A mathematical approach to solve the network reconstruction problem” by Marwan et. al.). 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 transpar- ent 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.
We introduce an approach to detecting inconsistencies in large biological networks by using Answer Set Programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on Answer Set Programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies in the data by determining minimal representations of conflicts. In practice, this can be used % in two ways, either to identify unreliable data or to indicate missing reactions.
We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we propose a range of different operations for altering the experimental data and/or the biological network in order to re-establish their mutual consistency—an indispensable prerequisite for automated prediction. For accomplishing repair and prediction, we take advantage of the distinguished modeling and reasoning capacities of Answer Set Programming. We validate our framework by an empirical study on the widely investigated organism Escherichia coli.
We address the problem of repairing and reasoning from large-scale biological networks and corresponding yet often discrepant measurements. To begin with, we develop a variety of techniques for repairing the experimental data and/or the biological network in order to re-establish their mutual consistency. To a turn, we then take advantage of ASP reasoning modes to predict missing measurements. In view of our repair techniques, this can be accomplished even in an inconsistent setting. We empirically evaluate our approach by considering the well-studied organism Escherichia coli along with published data sets.
Today’s molecular biology is confronted with enormous amounts of data, generated by new high-throughput technologies, along with an increasing number of biological models available over web repositories. This poses new challenges for bioinformatics to invent methods coping with incompleteness, heterogeneity, and mutual inconsistency of data and models. To this end, we built the library BioASP, providing a framework for analyzing biological data and models with Answer Set Programming (ASP). Due to the expressive modeling language, the inherent tolerance of incomplete knowledge, and efﬁcient solving engines, ASP has proven to be an excellent tool for solving a variety of biological questions. The BioASP library implements methods for analyzing metabolic and gene regulatory networks, consistency checking, diagnosing, and repairing biological data and models. In particular, it allows for computing predictions and generating hypotheses about required expansions of biological models. To accomplish this, expert knowledge of both the biological application and the ASP paradigm needs to be combined. In fact, the functionalities provided by the BioASP library exploit technical know-how of modeling (biological) problems in ASP and gearing ASP solvers’ parameters to them. Often, such best-practice technology is the result of an exhaustive series of tests. The BioASP library integrates our practical experience and offers them via easy-to-use Python functions, thus enabling ASP nonexperts to solve biological questions with ASP.