Journal
SCIENTIFIC REPORTS
Volume 5, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/srep09634
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Funding
- US NSF IGERT Grant [0504304]
- NSF MRI Grant [CNS-1337884]
- NIH New Mexico IDeA Networks of Biomedical Research Excellence Grant [2P20GM103451-14]
- NIH Mountain West Clinical Translational Research Grant [1U54GM104944-2]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1337884] Funding Source: National Science Foundation
- Direct For Education and Human Resources
- Division Of Graduate Education [0504304] Funding Source: National Science Foundation
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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.
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