»ã±¨±êÌâ (Title)£ºDeciphering complex single cell behaviors by high-throughput long-term time-lapse imaging and screening £¨Í¨¹ý¸ßͨÁ¿ÑÓʱ³ÉÏñ¼¼ÊõÀ´×êÑе¥Ï¸°û²ãÃæµÄ¸´ÔÓÐÐΪ£©

»ã±¨ÈË (Speaker)£º Ô¬Èôʯ£¨²©Ê¿£©£¨¼ÓÖÝ´óѧ²®¿ËÀû·ÖУ£©

»ã±¨¹¦·ò (Time)£º2021Äê11ÔÂ29ÈÕ(ÖÜÒ») 10: 00

»ã±¨µØÖ· (Place)£ºÐ£±¾²¿G309

Ô¼ÇëÈË(Inviter)£º°½Æ½ ½ÌÊÚ

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In no area of science complex behavior is more ubiquitous than in biology. Traditional quantitative approaches from physics encounter difficulties in describing such processes, which are in nature nonlinear, stochastic, dissipative and without detailed balance. In this talk, I will focus on how precise behaviors of single cells are emergent from individual noisy intracellular chemical reactions. We have developed both novel theoretical methods and experimental platforms for such purposes. For the theory part, we have derived fundamental limits for timing in chemical reaction systems, by greatly generalize an elegant proof for bounds on first passage time regarded as ¡°the most important result¡± in the field and took mathematicians a few decades to find. We then turn to practical reaction mechanisms and find unexpectedly that the widely used Michaelis-Menten kinetics is almost the opposite of the optimal strategy, but some simple kinetic mechanisms can get close to the best precision. A recently developed high-throughput, long-term, single cell time-lapse imaging and screening platform enables for the first time detailed characterization and isolation of dynamic phenotypes of single cells. Experimental results show that the simple but near-optimal mechanism is in fact used in all three systems that have been studied in depth using such a platform.