Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.0010019
Title: Inflammatory aetiology of human myometrial activation tested using directed graphs
Authors: Bisits A.M.
Smith R.
Mesiano S.
Yeo G. 
Kwek K. 
MacIntyre D.
Chan E.-C.
Issue Date: 2005
Citation: Bisits A.M., Smith R., Mesiano S., Yeo G., Kwek K., MacIntyre D., Chan E.-C. (2005). Inflammatory aetiology of human myometrial activation tested using directed graphs. PLoS Computational Biology 1 (2) : 132-136. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.0010019
Abstract: There are three main hypotheses for the activation of the human uterus at labour: functional progesterone withdrawal, inflammatory stimulation, and oxytocin receptor activation. To test these alternatives we have taken information and data from the literature to develop causal pathway models for the activation of human myometrium. The data provided quantitative RT-PCR results on key genes from samples taken before and during labour. Principal component analysis showed that pre-labour samples form a homogenous group compared to those during labour. We therefore modelled the alternative causal pathways in non-labouring samples using directed graphs and statistically compared the likelihood of the different models using structural equations and D-separation approaches. Using the computer program LISREL, inflammatory activation as a primary event was highly consistent with the data (p = 0.925), progesterone withdrawal, as a primary event, is plausible (p = 0.499), yet comparatively unlikely, oxytocin receptor mediated initiation is less compatible with the data (p = 0.091). DGraph, a software program that creates directed graphs, produced similar results (p = 0.684, p = 0.280, and p = 0.04, respectively). This outcome supports an inflammatory aetiology for human labour. Our results demonstrate the value of directed graphs in determining the likelihood of causal relationships in biology in situations where experiments are not possible. ? 2005 Bisits et al.
Source Title: PLoS Computational Biology
URI: https://scholarbank.nus.edu.sg/handle/10635/161876
ISSN: 1553734X
DOI: 10.1371/journal.pcbi.0010019
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