This paper describes how novel computational approaches have been used to test hypotheses for important physiological events when the traditional approaches of animal studies and experiment are not possible. The processes that regulate the onset of human labour are presently unknown, principally because there are no good animal models for human pregnancy and because it is unethical to conduct experiments on pregnant women undergoing labour. However, several hypotheses have been advanced to explain the trigger for labour, including: a functional withdrawal of the hormone progesterone, increased inflammation in the uterus, and increased signalling through the hormone oxytocin. To test these hypotheses the researchers used data on the messenger RNA concentrations of critical variables in samples of uterine muscle taken from 12 women undergoing caesarean section prior to labour and 12 women during labour. Directed graphs for each of the proposed hypotheses were then generated, where the graphs represent the direction of causal influence between different variables. Statistical testing determined how well the graphs of each hypothesis matched the experimental data. The results strongly support an inflammatory origin for the onset of human labour. This approach could be applied to other problems in human biology where the traditional approaches of experiments and animal models are not possible.
PLoS Computational Biology Vol. 1, Issue 2, p. 0132-0136