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.

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.

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

Sample files are included with caspo and available for download

• 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