The overarching goal of our laboratory is to develop an integrated understanding of mammalian cell fate decisions between dormancy and proliferation and their connections to cancer and aging. We study such mechanisms at two levels: gene pathway level and genome level. 

Understanding the cellular state of quiescence (versus proliferation, differentiation, and senescence)

Currently, we are particularly interested in the control mechanisms of cellular quiescence. Quiescence is a reversible, non-proliferative, "sleep-like" cellular state that plays a critical function in the health of higher organisms. The activation and proliferation of quiescent cell types (e.g., fibroblasts, lymphocytes, and stem cells) are fundamental to tissue repair and regeneration. The balance between cellular quiescence and proliferation is tightly regulated; dysregulation of this balance leads to many diseases, including fibrosis, autoimmune disease, cancer, and aging. Despite its importance, cellular quiescence is poorly understood.

The "reactability" of quiescent cells distinguishes them from cells at irreversibly arrested non-proliferative states such as senescence and terminal differentiation. Strikingly, these distinct cellular states (quiescence, proliferation, differentiation, and senescence) involve the same set of molecular regulators in the Rb-E2F gene network, suggesting that different Rb-E2F network outputs in response to different input signals may yield distinct cell fates. Our earlier work has shown that the Rb-E2F pathway network functions as a "bistable switch"; the Rb-E2F bistable switch converts graded and transient growth signals into an all-or-none E2F activation, which controls the reentry of the cell cycle from quiescence. Our recent work suggests that the Rb-E2F bistable switch controls cellular quiescence and its heterogeneity, and increasing the activation threshold of this Rb-E2F switch leads to quiescence deepening towards senescence. We use modeling to help us dissect and visualize the dynamic properties of the Rb-E2F pathway network, which are hard to grasp intuitively. We test model-generated hypotheses with carefully designed experiments. Experimental results enable us to update and improve our models, to achieve an integrated understanding of cellular dormancy and growth in normal and cancer cells during aging. 

Identifying regulatory network "signatures" at the genomic level

We develop bioinformatics tools, coupled with statistical and machine learning models, to identify biological "signatures" (genes, proteins, metabolites, etc.) underlying human diseases -- e.g., identifying cancer pathway signatures to better understand cancer heterogeneity and guide targeted therapies -- and to reconstruct regulatory networks that are responsible for specific cellular states (e.g., dormancy, growth, death) and signal responses.

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