### Reasoning on the response of logical signaling networks

The manual identification of logic rules underlying a biological system is
often hard, error-prone and time consuming.
Further, it has been shown that, if the inherent experimental noise is considered, many different logical networks
can be compatible with a set of experimental observations.
Thus, automated **inference of logical networks from experimental data** would allow for
identifying admissible large-scale logic models saving a lot of efforts and without any a priori bias.
Next, once a family a logical networks has been identified, one can suggest or **design new experiments** in order to reduce the uncertainty provided by this family.
Finally, one can **look for intervention strategies** that force a set of target species or compounds into a desired steady state.
Altogether, this constitutes a pipeline for automated reasoning on logical signaling networks.
Hence, the aim of **caspo** is to implement such a pipeline providing a powerful and easy-to-use software tool for systems biologists.

### Documentation

Detailed documentation about how to install and use **caspo** is available at http://caspo.readthedocs.io.

### Samples

Sample files are included with **caspo** and available for download

### Related publications

Designing experiments to discriminate families of logic models. (2015). Frontiers in Bioengineering and Biotechnology 3:131. DOI

Learning Boolean logic models of signaling networks with ASP. (2015). Theoretical Computer Science. DOI

Reasoning on the Response of Logical Signaling Networks with ASP. (2014). John Wiley & Sons, Inc. DOI

Minimal intervention strategies in logical signaling networks with ASP. (2013). Theory and Practice of Logic Programming. DOI

Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming. (2013). Bioinformatics. DOI

Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming. (2012) The 10th Conference on Computational Methods in Systems Biology. DOI