Systematic intervention of transcription for identifying network response to disease and cellular phenotypes

A major challenge in post-genomic research has been to understand how physiological and pathological phenotypes arise from the networks of expressed genes. Here, we addressed this issue by developing an algorithm to mimic the behavior of regulatory networks in silico and to identify the dynamic response to disease and changing cellular conditions.

With regulatory pathway and gene expression data as input, the algorithm provides quantitative assessments of a wide range of responses, including susceptibility to disease, potential usefulness of a given drug, or consequences to such external stimuli as pharmacological interventions or caloric restriction. The algorithm is particularly amenable to the analysis of systems that are difficult to recapitulate in vitro, yet they may have important clinical value. The hypotheses derived from the algorithm were biologically relevant and were successfully validated via independent experiments, as illustrated here in the analysis of the leukemia-associated BCR-ABL pathway and the insulin/IGF pathway related to longevity. The algorithm correctly identified the leukemia drug target and genes important for longevity, and also provided new insights into our understanding of these two processes.

The software package is available upon request to the authors.