Discerning mechanistically rewired biological pathways by cumulative interaction heterogeneity statistics

Travis B. Cotton, Hien H. Nguyen, Joseph I. Said, Zhengyu Ouyang, Jinfa Zhang, Mingzhou Song

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Changes in response of a biological pathway could be a consequence of either pathway rewiring, changed input, or a combination of both. Most pathway analysis methods are not designed for mechanistic rewiring such as regulatory element variations. This limits our understanding of biological pathway evolution. Here we present a Q-method to discern whether changed pathway response is caused by mechanistic rewiring of pathways due to evolution. The main innovation is a cumulative pathway interaction heterogeneity statistic accounting for rewiring-specific effects on the rate of change of each molecular variable across conditions. The Q-method remarkably outperformed differential-correlation based approaches on data from diverse biological processes. Strikingly, it also worked well in differentiating rewired chaotic systems, whose dynamics are notoriously difficult to predict. Applying the Q-method on transcriptome data of four yeasts, we show that pathway interaction heterogeneity for known metabolic and signaling pathways is indeed a predictor of interspecies genetic rewiring due to unbalanced TATA box-containing genes among the yeasts. The demonstrated effectiveness of the Q-method paves the way to understanding network evolution at the resolution of functional biological pathways.

Original languageEnglish (US)
Article number9634
JournalScientific reports
StatePublished - Apr 28 2015

All Science Journal Classification (ASJC) codes

  • General


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