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Until we have this experimental data, mathematical models of binding sites will help us predict TFBSs and in turn help us infer regulatory effects.
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We have used this experimental data to evaluate via parameter sensitivity analysis and representative simulations the mathematical model of yeast glycolytic oscillations developed by Wolf et al. [ 18].
Due to treatment with a high dose of sitagliptin, we should have included this experimental group to observe any adverse effects in the animals.
Consequently, we have selected this experimental system as a proof-of-concept example to compare the performance of data-driven mathematical models with that of knowledge-driven biological models.
Therefore, we have used this experimental set-up for further studying molecular details governing the observed differences in the regulatory mechanisms of the various groups of genes (manuscript in preparation).
However, during the past two decades, the application of molecular genetic markers has dominated this experimental landscape [ 2].
When considering our results, it remains unclear what influence the preservation of ligaments or the application of pretension to the implants has in this experimental setup.
In the past few years, next-generation sequencing (NGS) has supplanted this experimental strategy for variant detection for monogenic traits, and many articles report successful gene identification.
The potential benefits of this have some experimental support.
Apparently, the low amounts of precipitating ions added to DMEM may have accounted for this experimental variability.
If this is the case with corazonin neurons, this would suggest that there are fundamental differences in corazonin circuits between males and females, and may explain the differences we have observed utilizing this experimental strategy.
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