### 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** (i.e. inclusion minimal sets of knock-ins and knock-outs) 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.

### Installation

If you are already using Python with NumPy, you should be able to install **caspo** from pypi simply by running:

```
$ pip install caspo
```

If you are not using Python and/or NumPy, please visit the wiki for detailed instructions.

### Usage

Ask for help by running:

```
$ caspo --help
usage: caspo [-h] [--quiet] [--out O] [--version]
{control,visualize,design,learn,test,analyze} ...
Reasoning on the response of logical signaling networks with ASP
optional arguments:
-h, --help show this help message and exit
--quiet do not print anything to standard output
--out O output directory path (Default to './out')
--version show program's version number and exit
caspo subcommands:
for specific help on each subcommand use: caspo {cmd} --help
{control,visualize,design,learn,test,analyze}
```

Also, you may want to check out some examples at our notebook

### 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

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

Learning Boolean logic models of signaling networks with ASP. (2014). Theoretical Computer Science. 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