Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem

Signaling networks are essential for cells to control processes such as growth and response to stimuli. Although many "omic" data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases. We overcome these problems and use "omic" data to simultaneously reconstruct multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees each of which is rooted in a different cell surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies.