scholarly journals Urn models for regulated gene expression yield physically intuitive solutions for probability distributions of single-cell counts

2020 ◽  
Author(s):  
Krishna Choudhary ◽  
Atul Narang
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Hypergeometric Functions ◽  
Probability Distributions ◽  
Time Intervals ◽  
Probability Mass ◽  
Regulated Gene Expression ◽  
Short Time ◽  
Mass Functions

AbstractFitting the probability mass functions from analytical solutions of stochastic models of gene expression to the count distributions of mRNA and protein molecules in single cells can yield valuable insights into mechanisms of gene regulation. Solutions of chemical master equations are available for various kinetic schemes but, even for the models of regulation with a basic ON-OFF switch, they take complex forms with generating functions given as hypergeometric functions. Gene expression studies that have used these to fit the data have interpreted the parameters as burst size and frequency. However, this is consistent with the hypergeometric functions only if a gene stays active for short time intervals separated by relatively long intervals of inactivity. Physical insights into the probability mass functions are essential to ensure proper interpretations but are lacking for models of gene regulation. We fill this gap by developing urn models for regulated gene expression, which are of immense value to interpret probability distributions. Our model consists of a master urn, which represents the cytosol. We sample RNA polymerases and ribosomes from it and assign them to recipient urns of two or more colors, which represent time intervals with a homogeneous propensity for gene expression. Colors of the recipient urns represent sub-systems of the promoter states, and the assignments to urns of a specific color represent gene expression. We use elementary principles of discrete probability theory to derive the solutions for a range of kinetic models, including the Peccoud-Ycart model, the Shahrezaei-Swain model, and models with an arbitrary number of promoter states. For activated genes, we show that transcriptional lapses, which are events of gene inactivation for short time intervals separated by long active intervals, quantify the transcriptional dynamics better than bursts. Our approach reveals the physics underlying the solutions, which has important implications for single-cell data analysis.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

Probability Mass Functions

2019 ◽  
pp. 87-107
Author(s):  
Therese M. Donovan ◽  
Ruth M. Mickey
Keyword(s):  
Bayesian Inference ◽  
Binomial Distribution ◽  
Probability Distributions ◽  
Random Variables ◽  
Random Variable ◽  
Posterior Distributions ◽  
Bernoulli Distribution ◽  
Discrete Random Variables ◽  
Probability Mass ◽  
Mass Functions

This chapter focuses on probability mass functions. One of the primary uses of Bayesian inference is to estimate parameters. To do so, it is necessary to first build a good understanding of probability distributions. This chapter introduces the idea of a random variable and presents general concepts associated with probability distributions for discrete random variables. It starts off by discussing the concept of a function and goes on to describe how a random variable is a type of function. The binomial distribution and the Bernoulli distribution are then used as examples of the probability mass functions (pmf’s). The pmfs can be used to specify prior distributions, likelihoods, likelihood profiles and/or posterior distributions in Bayesian inference.

scholarly journals Tracking single-cell gene regulation in dynamically controlled environments using an integrated microfluidic and computational setup

2016 ◽  
Author(s):  
Matthias Kaiser ◽  
Florian Jug ◽  
Olin Silander ◽  
Siddharth Deshpande ◽  
Thomas Pfohl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Environmental Conditions ◽  
Single Cells ◽  
Fine Tuning ◽  
Growth Media ◽  
Single Cell Level ◽  
Cell Level ◽  
Operon Regulation

AbstractBacteria adapt to changes in their environment by regulating gene expression, often at the level of transcription. However, since the molecular processes underlying gene regulation are subject to thermodynamic and other stochastic fluctuations, gene expression is inherently noisy, and identical cells in a homogeneous environment can display highly heterogeneous expression levels. To study how stochasticity affects gene regulation at the single-cell level, it is crucial to be able to directly follow gene expression dynamics in single cells under changing environmental conditions. Recently developed microfluidic devices, used in combination with quantitative fluorescence time-lapse microscopy, represent a highly promising experimental approach, allowing tracking of lineages of single cells over long time-scales while simultaneously measuring their growth and gene expression. However, current devices do not allow controlled dynamical changes to the environmental conditions which are needed to study gene regulation. In addition, automated analysis of the imaging data from such devices is still highly challenging and no standard software is currently available. To address these challenges, we here present an integrated experimental and computational setup featuring, on the one hand, a new dual-input microfluidic chip which allows mixing and switching between two growth media and, on the other hand, a novel image analysis software which jointly optimizes segmentation and tracking of the cells and allows interactive user-guided fine-tuning of its results. To demonstrate the power of our approach, we study the lac operon regulation in E. coli cells grown in an environment that switches between glucose and lactose, and quantify stochastic lag times and memory at the single cell level.

scholarly journals Integrative inference of brain cell similarities and differences from single-cell genomics

2018 ◽  
Author(s):  
Joshua Welch ◽  
Velina Kozareva ◽  
Ashley Ferreira ◽  
Charles Vanderburg ◽  
Carly Martin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Human Subjects ◽  
Cell Types ◽  
Joint Analysis ◽  
Specific Gene ◽  
Cell Type ◽  
Type Definition ◽  
Cell Type Specific

SummaryDefining cell types requires integrating diverse measurements from multiple experiments and biological contexts. Recent technological developments in single-cell analysis have enabled high-throughput profiling of gene expression, epigenetic regulation, and spatial relationships amongst cells in complex tissues, but computational approaches that deliver a sensitive and specific joint analysis of these datasets are lacking. We developed LIGER, an algorithm that delineates shared and dataset-specific features of cell identity, allowing flexible modeling of highly heterogeneous single-cell datasets. We demonstrated its broad utility by applying it to four diverse and challenging analyses of human and mouse brain cells. First, we defined both cell-type-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis, an anatomically complex brain region that plays important roles in sex-specific behaviors. Second, we analyzed gene expression in the substantia nigra of seven postmortem human subjects, comparing cell states in specific donors, and relating cell types to those in the mouse. Third, we jointly leveraged in situ gene expression and scRNA-seq data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we integrated mouse cortical scRNA-seq profiles with single-cell DNA methylation signatures, revealing mechanisms of cell-type-specific gene regulation. Integrative analyses using the LIGER algorithm promise to accelerate single-cell investigations of cell-type definition, gene regulation, and disease states.

scholarly journals Independence of 3D chromatin conformation and gene regulation during Drosophila dorsoventral patterning

2020 ◽  
Author(s):  
Elizabeth Ing-Simmons ◽  
Roshan Vaid ◽  
Mattias Mannervik ◽  
Juan M. Vaquerizas
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Regulatory Elements ◽  
Chromatin State ◽  
Specific Gene ◽  
Chromatin Conformation ◽  
Dorsoventral Patterning ◽  
Tissue Specific ◽  
Chromatin Organisation

ABSTRACTThe relationship between the 3D organisation of chromatin inside the nucleus and the regulation of gene expression remains unclear. While disruption of domains and domain boundaries can lead to mis-expression of developmental genes, acute depletion of key regulators of genome organisation, such as CTCF and cohesin, and major reorganisation of genomic regions have relatively small effects on gene expression. Therefore, it is unclear whether changes in gene expression and chromatin state drive chromatin reorganisation, or whether changes in chromatin organisation facilitate cell type-specific activation of genes and their regulatory elements. Here, using the Drosophila melanogaster dorsoventral patterning system as a model, we demonstrate the independence of 3D chromatin organisation and developmental gene regulation. We define tissue-specific enhancers and link them to expression patterns at the single-cell level using single cell RNA-seq. Surprisingly, despite tissue-specific differences in chromatin state and gene expression, 3D chromatin organisation is maintained across tissues. Our results provide strong evidence that tissue-specific chromatin conformation is not required for tissue-specific gene expression, but rather acts as an architectural framework to facilitate proper gene regulation during development.

scholarly journals Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes

2021 ◽  
Author(s):  
Fangming Xie ◽  
Ethan J. Armand ◽  
Zizhen Yao ◽  
Hanqing Liu ◽  
Anna Bartlett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Mouse Brain ◽  
Cell Types ◽  
Regulatory Elements ◽  
Simple Correlation ◽  
False Prediction ◽  
Different Cell Types

Integrating single-cell transcriptomes and epigenomes across diverse cell types can link genes with the cis-regulatory elements (CREs) that control expression. Gene co-expression across cell types confounds simple correlation-based analysis and results in high false prediction rates. We developed a procedure that controls for co-expression between genes and integrates multiple molecular modalities, and used it to identify >10,000 gene-CRE pairs that contribute to gene expression programs in different cell types in the mouse brain.

scholarly journals Bayesian estimation for stochastic gene expression using multifidelity models

2018 ◽  
Author(s):  
Huy D. Vo ◽  
Zachary Fox ◽  
Ania Baetica ◽  
Brian Munsky
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Simulated Data ◽  
Second Phase ◽  
Stochastic Gene Expression ◽  
Finite State ◽  
Efficient Data ◽  
Computationally Intensive ◽  
Discrete Stochastic Models

AbstractThe finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an Adaptive Delayed Acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a Hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare both algorithms to an adaptive Metropolis algorithm with full FSP-based likelihood evaluations on three example models and simulated data to show that the new ADAMH variants achieve substantial speedup in comparison to the full FSP approach. By reducing the computational costs of parameter estimation, we expect the ADAMH approach to enable efficient data-driven estimation for more complex gene regulation models.

scholarly journals Genome-wide analysis of the association of transposable elements with gene regulation suggests that Alu elements have the largest overall regulatory impact

2017 ◽  
Author(s):  
Lu Zeng ◽  
Stephen M. Pederson ◽  
Danfeng Cao ◽  
Zhipeng Qu ◽  
Zhiqiang Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Transposable Elements ◽  
Regulatory Sequences ◽  
Alu Elements ◽  
Genome Wide ◽  
A Genome ◽  
Regulatory Impact ◽  
Species Specific ◽  
Regulated Gene Expression

ABSTRACTNearly half of the human genome is made up of transposable elements (TEs) and there is evidence that TEs are involved in gene regulation. Here, we have integrated publicly available genomic, epigenetic and transcriptomic data to investigate this in a genome-wide manner. A bootstrapping statistical method was applied to minimize the confounder effects from different repeat types. Our results show that although most TE classes are primarily associated with reduced gene expression, Alu elements are associated with up regulated gene expression. Furthermore, Alu elements had the highest probability of any TE class of contributing to regulatory regions of any type defined by chromatin state. This suggests a general model where clade specific SINEs may contribute more to gene regulation than ancient/ancestral TEs. Finally, non-coding regions were found to have a high probability of TE content within regulatory sequences, most notably in repressors. Our exhaustive analysis has extended and updated our understanding of TEs in terms of their global impact on gene regulation, and suggests that the most recently derived types of TEs, i.e. clade or species specific SINES, have the greatest overall impact on gene regulation.

scholarly journals Virulence gene regulation inside and outside

2000 ◽  
Vol 355 (1397) ◽  
pp. 657-665 ◽  
Author(s):  
Victor J. DiRita ◽  
N. Cary Engleberg ◽  
Andrew Heath ◽  
Alita Miller ◽  
J. Adam Crawford ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Regulatory System ◽  
Virulence Gene ◽  
Virulence Gene Expression ◽  
Regulatory Cascade ◽  
Transcription Activators ◽  
Regulated Gene Expression

Much knowledge about microbial gene regulation and virulence is derived from genetic and biochemical studies done outside of hosts. The aim of this review is to correlate observations made in vitro and in vivo with two different bacterial pathogens in which the nature of regulated gene expression leading to virulence is quite different. The first is Vibrio cholerae , in which the concerted action of a complicated regulatory cascade involving several transcription activators leads ultimately to expression of cholera toxin and the toxin–coregulated pilus. The regulatory cascade is active in vivo and is also required for maintenance of V . cholerae in the intestinal tract during experimental infection. Nevertheless, specific signals predicted to be generated in vivo , such as bile and a temperature of 37°C, have a severe downmodulating effect on activation of toxin and pilus expression. Another unusual aspect of gene regulation in this system is the role played by inner membrane proteins that activate transcription. Although the topology of these proteins suggests an appealing model for signal transduction leading to virulence gene expression, experimental evidence suggests that such a model may be simplistic. In Streptococcus pyogenes , capsule production is critical for virulence in an animal model of necrotizing skin infection. Yet capsule is apparently produced to high levels only from mutation in a two–component regulatory system, CsrR and CsrS. Thus it seems that in V . cholerae a complex regulatory pathway has evolved to control virulence by induction of gene expression in vivo , whereas in S. pyogenes at least one mode of pathogenicity is potentiated by the absence of regulation.

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