Biology subjects

Gligorovski, V.

Publications and source records attributed to Gligorovski, V..

3 recordsLinked to original sources

bioRxiv2020

Multidimensional single-cell benchmarking of inducible promoters for precise dynamic control in budding yeast

For quantitative systems biology, simultaneous readout of multiple cellular processes as well as precise, independent control over different genes activities are essential. In contrast to readout systems such as fluorescent proteins, control systems such as inducible transcription-factor-promoter systems have only been characterized in an ad hoc fashion, impeding precise system-level manipulations of biological systems and reliable modeling. We designed and performed systematic benchmarks involving easy-to-communicate units to characterize and compare inducible transcriptional systems. We built a comprehensive single-copy library of inducible systems controlling standardized fluorescent protein expression in budding yeast, including GAL1pr, GALL, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr, Z3EV system, the blue-light optogenetic systems El222-LIP, El222-GLIP and the red-light inducible PhyB-PIF3 system. To analyze these systems dynamic properties, 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 kinetic parameters, scaling of noise levels, impacts on growth, and, crucially, the fundamental leakiness of each system. Our multidimensional benchmarking additionally uncovers unexpected disadvantages of widely used tools, e.g., nonmonotonic activity of the MET3 and GALL promoters, slow off kinetics of the doxycycline and estradiol-inducible systems tetOpr and Z3EV, and high variability of PHO5pr and red-light activated PhyB-PIF3 system. We introduce two new tools for controlling gene expression: strongLOV, a more light-sensitive El222 mutant, and ARG3pr that functions as an OR gate induced by the lack of arginine or presence of methionine. To demonstrate the ability to finely control genetic circuits, we experimentally tuned the time between cell cycle Start and mitotic entry in budding yeast, artificially simulating near-wild-type timing. The characterizations presented here define the compromises that need to be made for quantitative experiments in systems and synthetic biology. To calibrate perturbations across laboratories and to allow new inducible systems to be benchmarked, we deposited single-copy reporter yeast strains, plasmids, and computer analysis code in public repositories. Furthermore, this resource can be accessed and expanded through the website https://promoter-benchmark.epfl.ch/.

synthetic biology↗

bioRxiv2020

Optimizing checkpoint strategies based on first principles predicts experimental DNA damage checkpoint override times

Why biological quality-control systems fail is often mysterious. Specifically, checkpoints such as the DNA damage checkpoint or the spindle assembly checkpoint are overriden after prolonged arrests allowing cells to continue dividing despite the continued presence of errors.1-4 Although critical for biological systems, checkpoint override is poorly understood quantitatively by experiment or theory. Override may represent a trade-off between risk and speed, a fundamental principle explaining biological phenomena.5,6 Here, we derive the first, general theory of optimal checkpoint strategies, balancing risk and opportunities for growth. We demonstrate that the mathematical problem of finding the optimal strategy maps onto the question of calculating the optimal absorbing boundary for a random walk, which we show can be solved efficiently recursively. The theory predicts the optimal override strategy without any free parameters based on two inputs, the statistics i) of error correction and ii) of survival. We apply the theory to the prominent example of the DNA damage checkpoint in budding yeast (Saccharomyces cerevisiae) experimentally. Using a novel fluorescent construct which allowed cells with DNA breaks to be isolated by flow cytometry, we quantified i) the probability distribution function of repair for a double-strand DNA break (DSB), including for the critically important, rare events deep in the tail of the distribution, as well as ii) the survival probability if the checkpoint was overridden. Based on these two measurements, the optimal checkpoint theory predicted remarkably accurately the DNA damage checkpoint override times as a function of DSB numbers, which we measured precisely at the single-cell level. Our multi-DSB results refine well-known bulk culture measurements7 and show that override is a more general phenomenon than previously thought. Further, we show for the first time that override is an advantageous strategy in cells with wild-type DNA repair genes. The universal nature of the balance between risk and self-replication opportunity is in principle relevant to many other systems, including other checkpoints, developmental decisions8, or reprogramming of cancer cells9, suggesting potential further applications of the theory.

systems biology↗

bioRxiv2020

YeaZ: A convolutional neural network for highly accurate, label-free segmentation of yeast microscopy images

The processing of microscopy images constitutes a bottleneck for large-scale experiments. A critical step is the establishment of cell borders ( segmentation), which is required for a range of applications such as growth or fluorescent reporter measurements. For the model organism budding yeast (Saccharomyces cerevisiae), a number of methods for segmentation exist. However, in experiments involving multiple cell cycles, stress, or various mutants, cells crowd or exhibit irregular visible features, which necessitate frequent manual corrections. Furthermore, budding events are visually subtle but important to detect. Convolutional neural networks (CNNs) have been successfully employed for a range of image processing applications. They require large, diverse training sets. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10000 cells) including mutants, stressed cells, and time courses, ii) a corresponding U-Net-based CNN, iii) a Python-based graphical user interface (GUI) to efficiently use the system, and iv) a web application to test it (www.quantsysbio.com). A key feature is a cell-cell boundary test which avoids the need for additional input from fluorescent channels. A bipartite graph matching algorithm tracks cells in time with high reliability. Our network is highly accurate and outperforms existing methods on benchmark images recorded by others, suggesting it transfers well to other conditions. Furthermore, new buds are detected early with high reliability. We apply the system to detect differences in geometry between wild-type and cyclin mutant cells. Our results indicate that morphogenesis control occurs unexpectedly early in the cell cycle and is gradual, demonstrating how the efficient processing of large numbers of cells uncovers new biology. Our system can serve as a resource to the community, expanded continuously with new images. Furthermore, the techniques we develop here are likely to be useful for other organisms as well. The identification of cell borders ( segmentation) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10000 cells) including mutants, stressed cells, and time courses, ii) a corresponding convolutional neural network (CNN), iii) a graphical user interface and a web application (www.quantsysbio.com) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient, large-scale image processing uncovers new biology, we analyzed the geometries of {approx}2200 wild-type and cyclin mutant cells and found that morphogenesis control occurs unexpectedly early and gradually.

bioinformatics↗