Quantifying E2F1 protein dynamics in single cells

Background:E2F1 protein,a major effector of the Rb/E2F pathway plays a central role in regulating cell-fate decisions involved in proliferation,apoptosis,and differentiation.Its expression is highly dynamic and tightly modulated through a combination of transcriptional,translational and posttranslational controls.However,the mechanisms by which its expression and activity can promote different cellular outcomes remain to be fully elucidated.To better document E2F1 expression in live cells,we have engineered a series of fluorescent E2F1 protein reporters that quantitatively capture E2F1 protein dynamics.Methods:Reporter constructs,under the control of the mouse or human E2F1 proximal promoter,were designed to express an E2F1-Venus fusion protein incapable of binding DNA.In addition,constructs either included or excluded the y untranslated region(3fUTR)of the E2F1 gene.These constructs were introduced into fibroblasts and epithelial cells,and expression of the fusion reporter protein was validated and quantified in single cells using live imaging.Results:In all cases,expression of the reporter protein effectively recapitulated the behavior of E2F1 under various conditions,including cell cycle progression and genotoxic stress.No or little fluorescent signal of the reporter was detected in G〇,but as the cycle progressed,expression of the reporter protein steadily increased in the nucleus,peaking a few hours before cell division,but declining to baseline 2-3 h prior to the onset of mitosis.The absence of the E2F13 fUTR in the constructs led to considerably higher steady-state levels of the fusion protein,which although normally regulated,exhibited a slightly less complex dynamic profile during the cell cycle or genotoxic stress.Lastly,the presence or absence of Rb failed to impact the overall detection and levels of the reporter proteins.Conclusions:Our validated E2F1 protein reporters complement nicely other reporters of the Rb/E2F pathway and provide a unique tool to follow the complex dynamics of E2F1 expression in real time in single cells.

Construction, visualization, and analysis of aiological network models in Dynetica

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.

Predictive power of cell-to-cell variability

Much of our current knowledge of biology has been constructed based on population-average measurements. However, advances in single-cell analysis have demonstrated the omnipresent nature of cell-to-cell variability in any population. On one hand, tremendous efforts have been made to examine how such variability arises, how it is regulated by cellular networks, and how it can affect cell-fate decisions by single cells. On the other hand, recent studies suggest that the variability may carry valuable information that can facilitate the elucidation of underlying regulatory networks or the classification of cell states. To this end, a major challenge is determining what aspects of variability bear significant biological meaning. Addressing this challenge requires the development of new computational tools, in conjunction with appropriately chosen experimental platforms, to more effectively describe and interpret data on cell- cell variability. Here, we discuss examples of when population heterogeneity plays critical roles in determining biologically and clinically significant phenotypes, how it serves as a rich information source of regulatory mechanisms, and how we can extract such information to gain a deeper understanding of biological systems.