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Sci Rep. 2015 Apr 28;5:9634. doi: 10.1038/srep09634.

Discerning mechanistically rewired biological pathways by cumulative interaction heterogeneity statistics.

Author information

1
Department of Computer Science, New Mexico State University, NM 88003, Las Cruces, USA.
2
Department of Plant and Environmental Sciences, New Mexico State University, NM 88003, Las Cruces, USA.

Abstract

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.

PMID:
25921728
PMCID:
PMC4894439
DOI:
10.1038/srep09634
[Indexed for MEDLINE]
Free PMC Article

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