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Cell cycle regulation

The eukaryotic cell cycle is a highly conserved process and its malfunction is a hallmark of cancer. This complex process of cell replication and division consists of a series of transitions (switches) between distinct events, which are guarded by checkpoints. We are interested in the quantitative mechanisms of how the architecture of the cell cycle switches is designed to ensure a controllable, robust and decisive transition and how perturbations/mutations can compromise this function. Using the budding yeast Saccharomyces cerevisiae as the model organism, we investigate these questions using a combination of mathematical modeling, yeast genetics, time-lapse fluorescent microscopy, single cell assays, and microfluidic devices.


    Stress response and cell fate decisions

    When a cell is stressed (e.g. DNA damage by irradiation or high demand of insulin production in β cells), it activates stress response pathways to alleviate the stress and, in mammalian cells, the stressed cell can undergo apoptosis. We are interested in the cell's strategy in dealing with various stresses and how the information is processed to make a cell fate decision. Cells are also facing fate decisions in processes such as development and stem cell differentiation and reprograming. In one recent work, we carried out a comparative study of the unfolded protein response in yeast and mammalian cells. Another system we are investigating is the tumor suppressor p53 network which coordinates the multiple responses to DNA damage.

    Network function and topology

    There is a close relationship between a network's function and its architecture (topology). Understanding this function-topology mapping would provide a framework to functionally classify and understand the complex biological networks. It would also provide a design manual for engineering networks. Using computational methods, we have been investigating the function-topology relationship for small functional modules. A recent example is the biochemical adaptation circuits. We have identified all the circuit architectures that can perform adaptation robustly. Despite of the diversity of biochemical networks, there are only two core solutions to achieve perfect adaptation.

    Systems drug design

    Drugs against multiple targets may overcome the many limitations of single target drugs and achieve a more effective and safer control of the disease. However, systematic identification of multiple drug targets and their best intervention require knowledge of the underlying disease network and calls for innovative computational methods that exploit the network's structure and dynamics. In collaboration with Prof. Luhua Lai, we developed a robust computational algorithm for finding multiple-target optimal intervention solutions in a disease network. The algorithm identifies potential drug targets and suggests optimal combinations of the target intervention that best restore the network from a diseased state to a normal state. The algorithm was applied to an inflammation-related network. The well-known side effects of the traditional non-steriodal anti-inflammatory drugs were correctly accounted for with our network model, and a number of multi-target solutions were found to be both effective and safer.