Here you can find the latest BioASP applications.BioASP is a collection of open-source programs, providing solutions for analyzing biological data and models with Answer Set Programming (ASP). ASP has proven to be an excellent tool for solving a variety of biological questions. The BioASP applications implement methods for analyzing metabolic and gene regulatory networks, consistency checking, diagnosis, and repair of biological data and models. In particular, it allows for computing predictions and generating hypotheses about required extensions of biological models. BioASP programs use the bioasp python library which encapsulates the ASP solutions and provides functions for reading biological data formats pre-processing the input and post post-processing and presenting the results. The functionalities provided by the BioASP library exploit technical know-how of modeling (biological) problems in ASP and gearing ASP solvers’ parameters to them. BioASP applications integrate our practical experience and offers them via easy-to-use Python functions, thus enabling ASP non-experts to solve biological questions with ASP. Finally we provide pyasp a python library that focuses on beeing a convenience wrapper for the ASP tools. PyASP encapsulates the grounder gringo and the solvers clasp and claspD into python objects. It allows to create ASP Terms (Atoms) and to combine to problem instances, join them with logic problem encodings and pass them to the solvers objects. Further PyASP provides the methods to access the solver results for further processing.
BioASP and PyASP are powered by the ASP tools of Potassco .
Revisiting the Training of Logic Models of Protein Signaling Networks with ASP
Santiago Videla, Carito Guziolowski, Federica Eduati, Sven Thiele, Niels Grabe, Julio Saez-Rodriguez, Anne Siegel
Detecting Inconsistencies in Large Biological Networks with Answer Set Programming
Martin Gebser, Torsten Schaub, Sven Thiele and Philippe Veber.
Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming
Martin Gebser, Carito Guziolowski, Mihail Ivanchev, Torsten Schaub, Anne Siegel, Sven Thiele and Philippe Veber.
Metabolic Network Expansion with Answer Set Programming
Torsten Schaub and Sven Thiele.