Cell Cycle and Protein Complex Dynamics in Discovering Signaling Pathways
Daniel Inostroza, Cecilia Hernández, Diego Seco, Gonzalo Navarro,
and Alvaro Olivera
Signaling pathways are responsible for the regulation of cell processes, such
as monitoring the external environment, transmitting information across
membranes, and making cell fate decisions. Given the increasing amount of
biological data available and the recent discoveries showing that many
diseases are related to the disruption of cellular signal transduction cascades,
in silico discovery of signaling pathways in cell biology has become an active
research topic in past years. However, reconstruction of signaling pathways
remains a challenge mainly because of the need for systematic approaches for
predicting causal relationships, like edge direction and activation/inhibition
among interacting proteins in the signal flow. We propose an approach for
predicting signaling pathways that integrates protein interactions, gene
expression, phenotypes, and protein complex information. Our method first finds
candidate pathways using a directed-edge-based algorithm and then defines a
graph model to include causal activation relationships among proteins, in
candidate pathways using cell cycle gene expression and phenotypes to infer
consistent pathways in yeast. Then, we incorporate protein complex coverage
information for deciding on the final predicted signaling pathways. We show
that our approach improves the predictive results of the state of the art using
different ranking metrics.