A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations

Biological processes are the result of the actions and interactions of many genes and the proteins that they encode. Our knowledge of interactions for many biological processes is limited, especially for cancer where genomic alterations may create entirely novel pathways not present in normal tissue. Perturbing gene expression (for example, by deleting a gene) has long been used as a tool in molecular biology to elucidate interactions but is very expensive and labor intensive. The search for new genes that may participate can be a daunting “fishing expedition.” We have devised a tool that automatically infers interactions using high-throughput gene expression data. When a gene is silenced, it causes other genes to be switched on or off, which provide clues about the pathway(s) in which the gene acts. Our method uses the genomewide on/off states as a fingerprint to detect interactions among a set of silenced genes. We were able to elucidate a network of interactions for several genes implicated in metastatic colon cancer. Genes newly connected to the network were found to operate in cancer cell invasion in human cells, validating the approach. Thus, the method enables an efficient discovery of the networks that underlie biological processes such as carcinogenesis.