Exploring the Contribution of Estrogen to Amyloid-Beta Regulation: A Novel Multifactorial Computational Modeling Approach

According to the amyloid hypothesis, Alzheimer Disease results from the accumulation beyond normative levels of the peptide amyloid-β (Aβ). Perhaps because of its pathological potential, Aβ and the enzymes that produce it are heavily regulated by the molecular interactions occurring within cells, including neurons. This regulation involves a highly complex system of intertwined normative and pathological processes, and the sex hormone estrogen contributes to it by influencing the Aβ-regulation system at many different points. Owing to its high complexity, Aβ regulation and the contribution of estrogen are very difficult to reason about. This report describes a computational model of the contribution of estrogen to Aβ regulation that provides new insights and generates experimentally testable and therapeutically relevant predictions. The computational model is written in the declarative programming language known as Maude, which allows not only simulation but also analysis of the system using temporal-logic. The model illustrates how the various effects of estrogen could work together to reduce Aβ levels, or prevent them from rising, in the presence of pathological triggers. The model predicts that estrogen itself should be more effective in reducing Aβ than agonists of estrogen receptor α (ERα), and that agonists of ERβ should be ineffective. The model shows how estrogen itself could dramatically reduce Aβ, and predicts that non-steroidal anti-inflammatory drugs should provide a small additional benefit. It also predicts that certain compounds, but not others, could augment the reduction in Aβ due to estrogen. The model is intended as a starting point for a computational/experimental interaction in which model predictions are tested experimentally, the results are used to confirm, correct, and expand the model, new predictions are generated, and the process continues, producing a model of ever increasing explanatory power and predictive value.