BioASP

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. The BioASP collection is available as the bioasp python package which encapsulates the ASP solutions, provides functions for reading biological data formats pre-processing the input, post-processing and presenting the results. The functionalities provided by the BioASP applications 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 BioASP provides 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 .

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Don't hesitate to ask questions, report bugs, etc. to bioasp@gmail.com.

The BioASP applications

Identification of Functional Gene Units

Integrating heterogeneous knowledge is necessary to elucidate the regulations in biological systems. In particular, such an integration is widely used to identify functional units, that are sets of genes that can be triggered by the same external stimuli, as biological stresses, and that are linked to similar responses of the system. Although several models and algorithms shown great success for detecting functional units on well-known biological species, they fail in identifying them when applied to more exotic species, such as extremophiles, that are by nature unrefined. Shortest Genome Segments (SGS) is a new model of functional units with a predictive power that is comparable to existing methods but overcomes this crucial limitation. In contrary to existing methods, SGS are stable in (i) computational time and (ii) ability to predict functional units when one deteriorates the biological knowledge, which simulates cases that occur for exotic species.
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Related publications:

An ASP application in integrative biology: identification of functional gene units
Philippe Bordron, Damien Eveillard, Alejandro Maass, Anne Siegel, Sven Thiele.


Learning Boolean logic models for Signaling Networks

A fundamental question in Systems Biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data that guarantees global optimality of solutions as well as provides a complete set of solutions.
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Related publications:

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.

Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming
Carito Guziolowski, Santiago Videla, Federica Eduati, Sven Thiele, Thomas Cokelaer, Anne Siegel, Julio Saez-Rodriguez.


Sign Consistency on Influence Graphs - Diagnosis, Repair, Prediction

Building upon a notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We present an approach to check the consistency of large-scale data sets, provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions. Further, we address the problem of repairing networks and corresponding yet often discrepant measurements in order to re-establish their mutual consistency and predict unobserved variations even under inconsistency.
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Related publications:

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 Completion

A qualitative approach to elaborating the bio-synthetic capacities of metabolic networks. Large-scale metabolic networks as well as measured data sets suffer from substantial incompleteness. This approach builds upon a formal method for analyzing large-scale metabolic networks mapping its principles into Answer Set Programming. The approach can then be used to check whether a drafted network provides the synthesis routes to comply with the required functionality. If this fails, the draft network can be completed by importing reactions from metabolic reference network stemming from other organisms until the obtained network provides the measured functionality.
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Related publications:

Metabolic Network Expansion with Answer Set Programming
Torsten Schaub and Sven Thiele.

Extending the Metabolic Network of Ectocarpus Siliculosus using Answer Set Programming
Guillaume Collet, Damien Eveillard, Martin Gebser, S. Prigent, Torsten Schaub, Anne Siegel and Sven Thiele.


Minimal Intervention Strategies

Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitative methods that permit fine-grained (kinetic) analysis, qualitative approaches allow for addressing large-scale networks. This is accomplished by more abstract representations such as logical networks. We elaborate upon such a qualitative approach aiming at the computation of minimal interventions in logical signaling networks relying on Kleene’s three-valued logic and fixpoint semantics. We address this problem within answer set programming and show that it greatly outperforms previous work using dedicated algorithms.
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Related publications:

Minimal Intervention Strategies in Logical Signaling Networks with Answer Set Programming
Roland Kaminski, Torsten Schaub, Anne Siegel and Santiago Videla S.