Biology subjects

Ron, E.

Publications and source records attributed to Ron, E..

4 recordsLinked to original sources

bioRxiv2026

Integrating Single-Cell Experiments and Stochastic Models to Understand and Predict Glucocorticoid Receptor Transport and DUSP1 mRNA Expression Dynamics

Glucocorticoids activate the glucocorticoid receptor (GR) to suppress inflammation, yet it remains unclear how GR transport dynamics and downstream gene regulation are coordinated within single cells. We combine immunocytochemistry (ICC) and single-molecule fluorescence in situ hybridization (smFISH) to quantify endogenous GR transport and DUSP1 transcription dynamics across thousands of individual cells following dexamethasone (Dex) stimulation. Performing multiple rounds of statistical inference based on Chemical Master Equations (CME), we determine the most likely mechanisms and reaction rates for Dex-driven GR nuclear import; compartment-specific GR degradation; GR-dependent control of the DUSP1 promoter; and DUSP1 transcription, elongation, transport, and degradation. Our inferred model suggests that nuclear GR degradation is the dominant mechanism of receptor clearance, that GR primarily regulates promoter activation, and that time-dependent AU-rich element (ARE)-mediated mRNA degradation contributes heavily to DUSP1 clearance. With these mechanisms, the fully-parameterized model quantitatively predicts joint distributions of GR translocation and decay dynamics, DUSP1 transcription site activity, and nuclear and cytoplasmic DUSP1 mRNA heterogeneity among clonal cells as functions of time and across seven orders of magnitude for Dex induction concentrations. Our results establish an integrated quantitative framework to link receptor dynamics to gene expression heterogeneity and predict single-cell hormone-responsive transcription programs.

cell biology↗

bioRxiv2024

Sequential design of single-cell experiments to identify discrete stochastic models for gene expression.

Control of gene regulation requires quantitatively accurate predictions of heterogeneous cellular responses. When inferred from single-cell experiments, discrete stochastic models can enable such predictions, but such experiments are highly adjustable, allowing for almost infinitely many potential designs (e.g., at different induction levels, for different measurement times, or considering different observed biological species). Not all experiments are equally informative, experiments are time-consuming or expensive to perform, and research begins with limited prior information with which to construct models. To address these concerns, we developed a sequential experiment design strategy that starts with simple preliminary experiments and then integrates chemical master equations to compute the likelihood of single-cell data, a Bayesian inference procedure to sample posterior parameter distributions, and a finite state projection based Fisher information matrix to estimate the expected information for different designs for subsequent experiments. Using simulated then real single-cell data, we determined practical working principles to reduce the overall number of experiments needed to achieve predictive, quantitative understanding of single-cell responses.

systems biology↗

bioRxiv2023

Using mechanistic models and machine learning to design single-color multiplexed Nascent Chain Tracking experiments

mRNA translation is the ubiquitous cellular process of reading messenger-RNA strands into functional proteins. Over the past decade, large strides in microscopy techniques have allowed observation of mRNA translation at a single-molecule resolution for self-consistent time-series measurements in live cells. Dubbed Nascent chain tracking (NCT), these methods have explored many temporal dynamics in mRNA translation uncaptured by other experimental methods such as ribosomal profiling, smFISH, pSILAC, BONCAT, or FUNCAT-PLA. However, NCT is currently restricted to the observation of one or two mRNA species at a time due to limits in the number of resolvable fluorescent tags. In this work, we propose a hybrid computational pipeline, where detailed mechanistic simulations produce realistic NCT videos, and machine learning is used to assess potential experimental designs for their ability to resolve multiple mRNA species using a single fluorescent color for all species. Through simulation, we show that with careful application, this hybrid design strategy could in principle be used to extend the number of mRNA species that could be watched simultaneously within the same cell. We present a simulated example NCT experiment with seven different mRNA species within the same simulated cell and use our ML labeling to identify these spots with 90% accuracy using only two distinct fluorescent tags. The proposed extension to the NCT color palette should allow experimentalists to access a plethora of new experimental design possibilities, especially for cell signalling applications requiring simultaneous study of multiple mRNAs.

systems biology↗

bioRxiv2020

Harnessing machine learning to unravel protein degradation in Escherichia coli

Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (Stable Isotope Labeling with Amino acids in Cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins, and identified dozens of novel proteins that are fast-degrading. Finally, we used structural, physicochemical and protein-protein interaction network descriptors to train a machine-learning classifier to discriminate fast-degrading proteins from the rest of the proteome. Our combined computational-experimental approach provides means for proteomic-based discovery of fast degrading proteins in bacteria and the elucidation of the factors determining protein half-lives and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings could identify new potential antibacterial drug targets.

microbiology↗