A boolean network approach to estrogen transcriptional regulation

Abstract

Gene expression governs important biological processes such as the cell's growth cycle and its response to environmental signals. Alterations of this complex network of transcriptional interactions often lead to unstable expression states and disease. Estrogen is a sex hormone known for its roles in cell proliferation. Its expression has been involved in several physiological functions such as regulating the menstrual and reproduction cycles in women. Altered expression states where estrogen levels are atypically high have been associated with an increased incidence of breast, ovarian, and cervix cancer. To better understand the implications of deregulation of the estrogen and estrogen receptor regulatory networks, in this work we generated a dynamical model of gene regulation of the estrogen receptor transcription network based on known regulatory interactions. By using an adaptation to classical Boolean Networks dynamics we identified proliferative and antiproliferative gene expression states of the network and also to identify key players that promote these altered states when perturbed. We also modeled how pairwise gene alterations may contribute to shifts between these two proliferative states and found that the coordinated subexpression of E2F1 and SMAD4 is the most important combination in terms of promoting proliferative states in the network.