The overarching goal of our research is to establish an integrated computational/experimental framework for an in-depth view of signaling pathways central to mammalian cell cycle regulation. Our immediate goal is to develop a quantitative understanding of the temporal dynamics of the Myc/Rb/E2F network. This network represents an ideal system for combining modeling with experimental and quantitative analysis of a key biological response. First, the network, with its upstream activation and downstream execution function, provides a well-defined context for exploring design principles of complex biological networks. Second, understanding the modulation and dynamics of this network has direct medical implications given the critical role of Myc/Rb/E2F circuit in controlling cellular proliferation and cell-fate decisions. As such, the system provides a blueprint to develop mathematical models that help revealing critical regulatory properties, which can then guide experimental validation. It is commonly accepted that E2F plays a central role in regulating diverse cellular responses, including proliferation, apoptosis, and differentiation. Experimental evidence also suggests that E2F temporal dynamics serves as a critical cue for downstream responses. However, a quantitative understanding of how E2F controls such diverse outcomes is lacking. Answering this question requires quantitative analysis of E2F dynamics in single mammalian cells under normal and perturbed conditions. To this end, the central goal of the proposal is to develop a set of experimental and computational tools to quantify the temporal E2F dynamics in single cells, and to understand the corresponding implications of these dynamics in regulating cell cycle progression and apoptosis. The proposed research is highly multidisciplinary; it takes advantages of the complementary expertise by the participating investigators. Expected outcomes for the proposed work include: (1) quantitative understanding of the dynamics of a network with profound implications for normal cell physiology and cancer development, (2) genetic constructs and cell lines that enable precise modulation and quantitation of cell cycle dynamics, (3) mechanistically based, experimentally constrained mathematical models for the stochastic analysis of the Myc/Rb/E2F network. These biological insights and experimental and computational tools are likely of broad utility for the research communities of systems biology and cell biology.
The proposed research aims to gain insight into key signaling pathways that underlie the entry and coordination of mammalian cell cycle in single cells. Such understanding is critical for the development of effective strategies for cancer therapy and cancer diagnostics.
| Dong, Peng; Zhang, Carolyn; Parker, Bao-Tran et al. (2018) Cyclin D/CDK4/6 activity controls G1 length in mammalian cells. PLoS One 13:e0185637 |
| Shats, Igor; Deng, Michael; Davidovich, Adam et al. (2017) Expression level is a key determinant of E2F1-mediated cell fate. Cell Death Differ 24:626-637 |
| Zhang, Carolyn; Tsoi, Ryan; You, Lingchong (2016) Addressing biological uncertainties in engineering gene circuits. Integr Biol (Camb) 8:456-64 |
| Zhang, Carolyn; Tsoi, Ryan; Wu, Feilun et al. (2016) Processing Oscillatory Signals by Incoherent Feedforward Loops. PLoS Comput Biol 12:e1005101 |
| Lee, Tae J; Wong, Jeffrey; Bae, Sena et al. (2015) A power-law dependence of bacterial invasion on mammalian host receptors. PLoS Comput Biol 11:e1004203 |
| Srimani, Jaydeep K; Yao, Guang; Neu, John et al. (2014) Linear population allocation by bistable switches in response to transient stimulation. PLoS One 9:e105408 |
| Dong, Peng; Maddali, Manoj V; Srimani, Jaydeep K et al. (2014) Division of labour between Myc and G1 cyclins in cell cycle commitment and pace control. Nat Commun 5:4750 |
| Li, Bochong; You, Lingchong (2014) Stochastic sensitivity analysis and kernel inference via distributional data. Biophys J 107:1247-1255 |
| Tan, Cheemeng; Smith, Robert Phillip; Tsai, Ming-Chi et al. (2014) Phenotypic signatures arising from unbalanced bacterial growth. PLoS Comput Biol 10:e1003751 |
