摘要:Biological processes occur in a wide spectrum of spatial and temporal scales, thus presenting a major challenge to identify the underlying quantitative principles. Although major advancement in molecular genetics, genomics and proteomics in recent years have elucidated the vast number of molecular players that mediate cellular signaling and cell-cell interactions, providing functional maps of the key cellular networks/pathways involved, our understanding of many bio-processes remains limited, in particular regarding how complex network/pathway act dynamically within and between interacting cell types to control physiological responses in complex environments. In this talk, I'll present results from two recent studies in my lab, which attempt to dissect complex cellular dynamics at the system level by combining quantitative single-cell imaging with computational modeling. The first study explored the dose-dependent and cancer cell-type dependent response to DNA damaging drug, one of the most common anticancer chemotherapeutics used in the clinic. We not only uncovered an intriguing bimodal dynamics experimentally that regulate this important drug response process but also pinpointed a four-component molecular module out of a large cellular network that exerts the major control over this bimodal switch. And variable activation of this four-component module accounts for drug response variability in distinct cancer types. The second study investigated the interaction dynamics by which Natural Killer (NK) cells, an important effector cell in the human immune system, detect and kill cancer cells. By imaging unique fluorescent reporter contructs in live cells, we were able to determine the rate-limiting kinetics in NK-cancer cell interaction and identify mechanistically relevant heterogeneity in the process. NK cells are considered promising candidate for cancer treatment, especially for eliminating residual cancer cells after conventional therapy. Results from our current and further study would likely provide new mechanistic insight to develop this cancer immunotherapy.