PT  - JOURNAL ARTICLE
AU  - Torsten Gross
AU  - Matthew Wongchenko
AU  - Yibing Yan
AU  - Nils Blüthgen
TI  - Robust network inference using response logic
AID  - 10.1101/547216
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 547216
4099  - http://biorxiv.org/content/early/2019/02/12/547216.short
4100  - http://biorxiv.org/content/early/2019/02/12/547216.full
AB  - Motivation A major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context.Results We present a network inference method that is based on a simple response logic with minimal presumptions. It requires that we can experimentally observe whether or not some of the system’s components respond to perturbations of some other components, and then identifies the directed networks that most accurately account for the observed propagation of the signal. To cope with the intractable number of possible networks, we developed a logic programming approach that can infer networks of hundreds of nodes, while being robust to noisy, heterogeneous or missing data. This allows to directly integrate prior network knowledge and additional constraints such as sparsity. We systematically benchmark our method on KEGG pathways, and show that it outperforms existing approaches in DREAM3 and DREAM4-challenges. Applied to a perturbation data set on PI3K and MAPK pathways in isogenic models of a colon cancer cell line, it generates plausible network hypotheses that explain distinct sensitivities towards EGFR inhibitors by different PI3K mutants.Availability and Implementation A Python/Answer Set Programming implementation can be accessed at github.com/GrossTor/response-logic.Contact nils.bluethgen{at}charite.de