Diseases such as cancer are often related to collaborative effects involving interactions of multiple genes within complex pathways, or to combinations of multiple SNPs. To understand the structure of such mechanisms, it is helpful to analyze genes in terms of the purely cooperative, as opposed to independent, nature of their contributions towards a phenotype (Anastassiou, 2007).
Two papers currently published in Molecular Systems Biology address this question:
- Using an information-theoretic definition of synergy, Dimitris Anastassiou exposes a computational approach to identify ab initio sets of interacting genes linked to a given disease state or phenotype (Anastassiou, 2007). This definition of synergy, derived form a generalization of the concept of mutual information, can connect two levels of organization (for example: genes and disease phenotype) and reveal the structure of the cooperative effects underlying a phenotypic state.
- Jim Collins and colleagues apply network inference techniques to identify key pathways involved in prostate cancer progression (Ergün et al, 2007). A compendium of 1144 expression profiles spanning multiple cancer types is used to train the “mode-of-action by network identification” (MNI) algorithm. When applied on the test set of prostate cancer profiles, the androgen receptor and several of its cognate target genes are identified as top genetic mediators. This signaling pathway would not have been detected by expression change alone or by pathway analysis using Gene Set Enrichment Analysis (GSEA).