GSCA: Reconstructing biological pathway topologies using a cultural algorithms approach

Abstract

With the increasing availability of gene sets and pathway resources, novel approaches that combine both resources to reconstruct networks from gene sets are of interest. Currently, few computational approaches explore the search space of candidate networks using a parallel search. In particular, search agents employed by evolutionary computational approaches may better escape false peaks compared to previous approaches. It may also be hypothesized that gene sets may model signal transduction events, which refer to linear chains or cascades of reactions starting at the cell membrane and ending at the cell nucleus. These events may be indirectly observed as a set of unordered and overlapping gene sets. Thus, the goal is to reverse engineer the order information within each gene set to reconstruct the underlying source network using prior knowledge to limit the search space.We propose the Gene Set Cultural Algorithm (GSCA) to reconstruct networks from unordered gene sets. We introduce a robust heuristic based on the arborescence of a directed graph that performs well for random topological sort orderings across gene sets simulated for four E. coli networks and five Insilico networks from the DREAM3 and DREAM4 initiatives, respectively. Furthermore, GSCA performs favorably when reconstructing networks from randomly ordered gene sets for the aforementioned networks. Finally, we note that from a set of 23 gene sets discretized from a set of 300 S. cerevisiae expression profiles, GSCA reconstructs a network preserving most of the weak order information found in the KEGG Cell Cycle pathway, which was used as prior knowledge.