Inferring gene regulation from stochastic transcriptional variation across single cells at steady-state
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA835405 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19095439_1.fastq.gz | Fastqsanger.gz | |||
| SRR19095439_2.fastq.gz | Fastqsanger.gz | |||
| SRR19095440_1.fastq.gz | Fastqsanger.gz | |||
| SRR19095440_2.fastq.gz | Fastqsanger.gz | |||
| SRR19095441_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 26 |
Similar Datasets
Project description:Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady-state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful co-fluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady-state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
2022-08-04 | GSE202292 | GEO
Project description:Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
| S-EPMC9407670 | biostudies-literature
Project description:Transition state dynamics during a stochastic fate choice
| PRJNA529608 | ENA
Project description:Steady-state expression of Giardia lamblia transcripts
| PRJNA664462 | ENA
Project description:Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial number of possible states of the population. Moreover, while the modeller easily hypothesises the mechanistic aspects of the model, the quantitative parameters associated to these mechanistic transitions are difficult or impossible to measure directly. In this paper, we investigate how formal verification methods can aid parameter inference for population discrete-time Markov chains in a scenario where only a limited sample of population-level data measurements-sample distributions among terminal states-are available. We first discuss the parameter identifiability and uncertainty quantification in this setup, as well as how the existing techniques of formal parameter synthesis and Bayesian inference apply. Then, we propose and implement four different methods, three of which incorporate formal parameter synthesis as a pre-computation step. We empirically evaluate the performance of the proposed methods over four representative case studies. We find that our proposed methods incorporating formal parameter synthesis as a pre-computation step allow us to significantly enhance the accuracy, precision, and scalability of inference. Specifically, in the case of unidentifiable parameters, we accurately capture the subspace of parameters which is data-compliant at a desired confidence level.
| S-EPMC10642793 | biostudies-literature
Project description:Immune landscape of mouse heart in steady state
| PRJNA957810 | ENA
Project description:The ‘small perturbation’ approach is critical in studying the ‘steady state’ of a biological system. In our experiments, small perturbations were generated by applying a series of repeating intermittent small doses of ultraviolet radiation to a human keratinocyte cell line, HaCaT. The biological response was assessed by monitoring the gene expression profiles using a high reliability and high resolution cDNA microarray system. Following intermittent 10 J/m2 UVB small perturbations, two opposite classes of genes, down-regulated and up-regulated, exhibited an immediate response followed by relaxation between each small perturbation, but were prolonged down- or up-regulated without relaxation while larger doses (233 or 582.5 J/m2) of UVB were applied. A repeated cycle pattern of gene expression following small perturbations is an indication of the existence of steady states. This cycle pattern is suppressed when large perturbations are applied. We believe that this is a universal phenomenon. In our experiments, the functions of up-regulated genes were mainly associated with anti-proliferation, anti-mitogenesis, and apoptosis. On the other hand, down-regulated genes were mainly related to proliferation, mitogenesis, and anti-apoptosis. In conclusion, this study experimentally proves the concept of steady state at the transcription level and demonstrates the feasibility of using small perturbation approaches for investigating steady states. This study could also set a foundation of computational systems biology, which has implicitly used the concept of steady state. Keywords: time course
2009-01-30 | GSE7060 | GEO
Project description:The â??small perturbationâ?? approach is critical in studying the â??steady stateâ?? of a biological system. In our experiments, small perturbations were generated by applying a series of repeating intermittent small doses of ultraviolet radiation to a human keratinocyte cell line, HaCaT. The biological response was assessed by monitoring the gene expression profiles using a high reliability and high resolution cDNA microarray system. Following intermittent 10 J/m2 UVB small perturbations, two opposite classes of genes, down-regulated and up-regulated, exhibited an immediate response followed by relaxation between each small perturbation, but were prolonged down- or up-regulated without relaxation while larger doses (233 or 582.5 J/m2) of UVB were applied. A repeated cycle pattern of gene expression following small perturbations is an indication of the existence of steady states. This cycle pattern is suppressed when large perturbations are applied. We believe that this is a universal phenomenon. In our experiments, the functions of up-regulated genes were mainly associated with anti-proliferation, anti-mitogenesis, and apoptosis. On the other hand, down-regulated genes were mainly related to proliferation, mitogenesis, and anti-apoptosis. In conclusion, this study experimentally proves the concept of steady state at the transcription level and demonstrates the feasibility of using small perturbation approaches for investigating steady states. This study could also set a foundation of computational systems biology, which has implicitly used the concept of steady state. Keywords: time course Three UVB exposures are indicated by UV at time points of 0, 8 and 16 hours. T1, T2, T3, T4, T5, and T6 denote the sampling time points. T1, T3 and T5 are allocated 30 minutes after the corresponding UVB irradiation. T2, T4 and T6 are allocated 8 hours after each UVB irradiation. At each sampling time point, two samples (control and UV-irradiated) are collected. See supplementary file Loop_design.pdf for further explanation.
2010-06-24 | E-GEOD-7060 | biostudies-arrayexpress
Project description:Thermal transcriptome of Phaeodactylum tricornutum in steady-state growth
| PRJNA772173 | ENA
Project description:This analysis aimed to identify significantly up-regulated genes in myofibroblasts, compared with steady-state fibroblasts.
2018-02-24 | GSE111043 | GEO