title: Qualitative reasoning for biological network inference from systematic perturbation experiments.
abstract: The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy. silvana.badaloni@unipd.it
authors.forename: Silvana
authors.initials: S
authors.lastname: Badaloni
authors.forename: Barbara
authors.initials: B
authors.lastname: Di Camillo
authors.forename: Francesco
authors.initials: F
authors.lastname: Sambo
mesh_terms: D000465:Algorithms; D008954:Models, Biological; D015398:Signal Transduction