Abstract
AbstractFor quantitative systems biology, simultaneous readout of multiple cellular processes as well as precise, independent control over different genes’ activities are needed. In contrast to readout systems such as fluorescent proteins, control systems such as inducible transcription-factor-promoter systems have not been characterized systematically, impeding reliable modeling and precise system-level probing of biological systems.We built a comprehensive single-copy library of inducible promoters controlling fluorescent protein (yEVenus) expression in budding yeast, including GAL1pr, GALLpr, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr and the blue light-inducible systems EL222-LIP, EL222-GLIP. To track their properties under dynamic perturbations, we performed high-throughput time-lapse microscopy. The analysis of >100 000 cell images was made possible by the recently developed convolutional neural network YeaZ. We report key coarse-grained kinetic parameters, levels of noise, and effects on cellular growth. Our multidimensional benchmarking uncovers unexpected disadvantages of widely used tools, e.g., slow off kinetics of the doxycycline-induced tetOpr system, nomonotonic activity, or high variability of PHO5pr. Our data would guide the choice of acceptable compromises for applications. Evaluating the ARG3 promoter for potential use as a new inducible system, we discovered that it has an interesting OR gate function and that it turns on in the presence of methionine in synthetic complete medium. To demonstrate the ability to finely control genetic circuits, we tuned the time between cell cycle Start and mitotic entry in budding yeast experimentally, exogenously simulating near-wild-type timing.The data presented here ought to facilitate the choices of expression systems for quantitative experiments and applications in systems and synthetic biology and to serve as a reference to benchmark new inducible systems.
Publisher
Cold Spring Harbor Laboratory