Glioblastoma (GBM) is the most common brain tumor in adults, which is very aggressive, with a very poor prognosis that affects men twice as much as women, suggesting that female hormones (estrogen) play a protective role. With an in silico approach, we highlighted that the expression of the membrane G-protein-coupled estrogen receptor (GPER) had an impact on GBM female patient survival. In this context, we explored for the first time the role of the GPER agonist G-1 on GBM cell proliferation. Our results suggested that G-1 exposure had a cytostatic effect, leading to reversible G2/M arrest, due to tubulin polymerization blockade during mitosis. However, the observed effect was independent of GPER. Interestingly, G-1 potentiated the efficacy of temozolomide, the current standard chemotherapy treatment, since the combination of both treatments led to prolonged mitotic arrest, even in a temozolomide less-sensitive cell line. In conclusion, our results suggested that G-1, in combination with standard chemotherapy, might be a promising way to limit the progression and aggressiveness of GBM.

Modelling complex biological systems is necessary for their study and understanding. Biomodels is a repository of peer-reviewed models represented in the Systems Biology Markup Language (SBML). Most of these models are quantitative, but in some cases, qualitative models—such as Boolean networks (BNs)—are better suited. This paper focuses on the automatic transformation of quantitative SBML models to Boolean networks. We propose SBML2BN, a pipeline dedicated to this task. Our approach takes advantage of several SBML elements (reactions, rules, events) as well as a numerical simulation of the concentration of the species over time to constrain both the structure and the dynamics of the Boolean networks to synthesise. Finding all the BNs complying with the given structure and dynamics was formalised as an optimisation problem solved in the answer-set programming framework. We run SBML2BN on more than 200 quantitative SBML models, and we provide evidence that one can automatically construct Boolean networks which are compatible with the structure and the dynamics of an SBML model. In case the SBML model includes rules or events, we also show how the evaluation criteria are impacted when taking these elements into account.