Integrating literature-constrained and data-driven inference of signalling networks.
BACKGROUND
Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks.


RESULTS
We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. Our method extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a network of growth and inflammatory signalling. We obtain a model with superior data fit in the human liver cancer HepG2 and propose potential missing pathways.


BACKGROUND
CNORfeeder is in the process of being submitted to Bioconductor and in the meantime available at www.cellnopt.org.


BACKGROUND
saezrodriguez@ebi.ac.uk


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Information Engineering, University of Padova, Padova, 31050, Italy.
Federica
F
Eduati
Javier
J
De Las Rivas
Barbara
B
Di Camillo
Gianna
G
Toffolo
Julio
J
Saez-Rodriguez
D000465:Algorithms; D045744:Cell Line, Tumor; D006801:Humans; D010750:Phosphoproteins / Q000378:metabolism; D060066:Protein Interaction Maps; D015398:Signal Transduction; D012984:Software
D010750:Phosphoproteins