Section: New Software and Platforms

Caspo

Cell ASP Optimizer

Functional Description

Cell ASP Optimizer (caspo) is a pipeline for automated reasoning on logical signaling networks. The main underlying issue is that inherent experimental noise is considered, so that many different logical networks can be compatible with a set of experimental observations. Caspo-learn performs an automated inference of logical networks from experimental data. It identifies admissible large-scale families of logic models without any a priori bias, thus saving a lot of efforts. Next, once a family a logical networks has been identified, caspo-design can suggest or design new experiments in order to reduce the uncertainty associated to this family. Finally, caspo-control computes 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. In 2015, the tool was extended to compute experimental design proposition [23] , and to handle time-series datasets [31] .

• Participants: Santiago Videla, Carito Guziolowski, Sven Thiele, Thomas Cokelaer, Torsten Schaub, Anne Siegel, Loic Paulevé and Max Ostrowski

• Partners: Ecole Centrale de Nantes - University of Potsdam - EMBL - LRI - Laboratoire de Recherche en Informatique

• Contact: Anne Siegel

• URL: http://bioasp.github.io/caspo/