The Rb/E2F network has a critical role in regulating cell cycle progression and cell fate decisions. It is dysfunctional in virtually all human cancers, because of genetic lesions that cause overexpression of activators, inactivation of repressors, or both. Paradoxically, the downstream target of this network, E2F1, is rarely strongly overexpressed in cancer. E2F1 can induce both proliferation and apoptosis but the factors governing these critical cell fate decisions remain unclear. Previous studies have focused on qualitative mechanisms such as differential cofactors, posttranslational modification or state of other signaling pathways as modifiers of the cell fate decisions downstream of E2F1 activation. In contrast, the importance of the expression levels of E2F1 itself in dictating the downstream phenotypes has not been rigorously studied, partly due to the limited resolution of traditional population-level measurements. Here, through single-cell quantitative analysis, we demonstrate that E2F1 expression levels have a critical role in determining the fate of individual cells. Low levels of exogenous E2F1 promote proliferation, moderate levels induce G1, G2 and mitotic cell cycle arrest, and very high levels promote apoptosis. These multiple anti-proliferative mechanisms result in a strong selection pressure leading to rapid elimination of E2F1-overexpressing cells from the population. RNA-sequencing and RT-PCR revealed that low levels of E2F1 are sufficient to induce numerous cell cycle-promoting genes, intermediate levels induce growth arrest genes (i.e., p18, p19 and p27), whereas higher levels are necessary to induce key apoptotic E2F1 targets APAF1, PUMA, HRK and BIM. Finally, treatment of a lung cancer cell line with a proteasome inhibitor, MLN2238, resulted in an E2F1-dependent mitotic arrest and apoptosis, confirming the role of endogenous E2F1 levels in these phenotypes. The strong anti-proliferative activity of moderately overexpressed E2F1 in multiple cancer types suggests that targeting E2F1 for upregulation may represent an attractive therapeutic strategy in cancer.
Mathematical modeling has become an increasingly important aspect of biological research. Computer simulations help to improve our understanding of complex systems by testing the validity of proposed mechanisms and generating experimentally testable hypotheses. However, significant overhead is generated by the creation, debugging, and perturbation of these computational models and their parameters, especially for researchers who are unfamiliar with programming or numerical methods. Dynetica 2.0 is a user-friendly dynamic network simulator designed to expedite this process. Models are created and visualized in an easy-to-use graphical interface, which displays all of the species and reactions involved in a graph layout. System inputs and outputs, indicators, and intermediate expressions may be incorporated into the model via the versatile "expression variable" entity. Models can also be modular, allowing for the quick construction of complex systems from simpler components. Dynetica 2.0 supports a number of deterministic and stochastic algorithms for performing time-course simulations. Additionally, Dynetica 2.0 provides built-in tools for performing sensitivity or dose response analysis for a number of different metrics. Its parameter searching tools can optimize specific objectives of the time course or dose response of the system. Systems can be translated from Dynetica 2.0 into MATLAB code or the Systems Biology Markup Language (SBML) format for further analysis or publication. Finally, since it is written in Java, Dynetica 2.0 is platform independent, allowing for easy sharing and collaboration between researchers.
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