Reasoning on the response of logical signaling networks with Answer Set Programming
Project description
The aim of caspo is to implement a pipeline for automated reasoning on logical signaling networks. Main features include, learning of logical networks from experiments, design new experiments in order to reduce the uncertainty, and finding intervention strategies to control the biological system. For more details visit caspo’s website.
2.2.0 (2015-08-06)
Update subcommands to work with clingo 4.5.x
Output detailed differences for caspo design
Lazy optima computation in caspo learn
Add multi-threading from command line
Compute IO behaviors using multi-threading (multiprocessing)
2.1.1 (2014-08-30)
Bugfix in console handler for caspo design
2.1.0 (2014-08-28)
Bugfix in ASP encoding for caspo control
Implements caspo design from a list of experiments
Optionally relax hard constraint in caspo design
Implements random learner in caspo learn
2.0.1 (2014-04-18)
Improve ASP encoding for caspo design
Use to_str method available in pyzcasp 1.0.1
2.0.0 (2014-03-24)
Complete refactoring (no backwards compatibility)
- Introduce subcommands:
learn: learning logical networks
control: finding intervention strategies
analyze: compute I/O behaviors and basic stats
design: experimental design to discriminate I/O behaviors
visualize: dump results from other subcommands to dot files
Uses latest potassco tools: clingo >= 4.3.0, gringo >= 4.3.0 and clasp >= 3.0.2
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