In recent years, genomic sequencing and systematic analysis have revealed many molecular components that control gene expression at multiple levels, and detailed the myriad of interactions among and between these regulatory modules. However, it remains unclear how these molecular networks operate in time and space to transmit environmental information and carry out diverse cellular functions. The goal of our lab is to understand how network architecture governs the dynamics and function of regulatory responses in the context of stresses, aging, or diseases.
In particular, our laboratory focuses on the following research directions:
Decoding signal dynamics in cellular stress responses
Cells could transmit environmental information and control cellular behaviors through regulating the temporal dynamics of signaling activities. In an oft-cited example, epidermal growth factor induces transient activation of the mitogen activated protein kinase ERK and leads to cell proliferation, whereas nerve growth factor elicits sustained ERK activation and results in cell differentiation. In S.cerevisiae, we have recently discovered that the general stress responsive transcription factor Msn2 encodes both the identity and strength of external stimuli into dynamic patterns of its nuclear translocation. We further developed a synthetic system to control the dynamics of transcription factor translocation and revealed that these dynamic patterns can be decoded to generate selective gene expression. Our current work focuses on understanding: (1) the regulation
of yeast cellular functions and phenotypes by signal dynamics, (2) the functions of signal dynamics in mammalian systems, and (3) the theoretical principles of dynamic signal processing in living cells.
Using the signal control system to generate oscillatory transcription factor activation
A synthetic yeast strain has been constructed by introducing analog-sensitive mutations in PKA that renders selective inhibition of PKA activity by a small cell-permeable inhibitor, 1-NM-PP1. This chemical genetic strategy was combined with microfluidics and quantitative microscopy to control dynamic patterns of PKA input and monitor single-cell responses. This system was used to systematically investigate how the dynamics of transcription factor Msn2 are interpreted by its target genes (Hao et al., 2013; Hao and O'Shea, 2012). For example, in the movie, oscillatory patterns of transcription factor translocation were artificially generated using the synthetic system.
Understanding the dynamics of cellular aging
Cellular aging plays an important role in many diseases, such as cancers, metabolic syndromes and neurodegenerative disorders. Although there has been steady progress in identifying age-related factors such as reactive oxygen species and genomic instability, an emerging challenge is to reconcile the mechanistic contributions of these factors with the fact that genetically identical cells age at significantly different rates. Such complexity requires single-cell analyses designed to unravel the interplay of aging dynamics and variability. We developed novel microfluidic technologies that enable tracking of the replicative aging of single yeast cells in a fully automated fashion. We aim to combine this transformative approach with computational modeling to obtain a quantitative and predictive understanding of the heterogeneous aging process in single cells.
Quantifying heterogeneous dynamics in cancer cells
We have expanded our quantitative biology research into mammalian cell systems. We have developed new microfluidic and imaging technologies to track individual mammalian cells over a very long period of time. Our current work focuses on studying the heterogeneous signaling dynamics in cancer cells and how these dynamics influence cancer cell growth.