  In this study, we explored the utility of a descriptive and predictive
bionetwork model for phospholipase C-coupled calcium signaling pathways, built
with non-kinetic experimental information. Boolean models generated from these
data yield oscillatory activity patterns for both the endoplasmic reticulum
resident inositol-1,4,5-trisphosphate receptor (IP3R) and the plasma-membrane
resident canonical transient receptor potential channel 3 (TRPC3). These
results are specific as randomization of the Boolean operators ablates
oscillatory pattern formation. Furthermore, knock-out simulations of the IP3R,
TRPC3, and multiple other proteins recapitulate experimentally derived results.
The potential of this approach can be observed by its ability to predict
previously undescribed cellular phenotypes using in vitro experimental data.
Indeed our cellular analysis of the developmental and calcium-regulatory
protein, DANGER1a, confirms the counter-intuitive predictions from our Boolean
models in two highly relevant cellular models. Based on these results, we
theorize that with sufficient legacy knowledge and/or computational biology
predictions, Boolean networks provide a robust method for predictive-modeling
of any biological system.
