BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

Gómez-Schiavon, Mariana; Chen, Liangfu; West, Anne E.; Buchler, Nicolas E.

doi:10.1186/s13059-017-1297-9

Cited by 43 publications

(74 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, we adopted a Markov chain Monte Carlo algorithm with Metropolis-Hastings sampling (i.e., MCMC-MH) to compute the posterior distributions of the model parameters [15,45]. However, there is room to further improve the speed of Bayesian inference.…”

Section: Discussionmentioning

confidence: 99%

“…We then used IS-HME to compute the evidence of each model given the underlying data set. We compared the IS-HME evidence to maximum likelihood metrics used for model selection, such as the Bayesian Information Criterion (BIC) and Akaike Information Crite-rion (AIC) [15]. Both BIC and AIC are approximations to the Bayesian evidence and become equivalent in the limit of large sample sizes; see Discussion.…”

Section: Model Selection Using the Full Bayesian Frameworkmentioning

confidence: 99%

“…Inference requires (1) appropriate models of stochastic gene expression, (2) numerical methods to calculate the time-dependent mRNA distribution in a population of cells given any underlying model and associated parameters, and (3) calculating the likelihood that measured data were sampled from the calculated distribution. We recently developed a Bayesian approach (BayFISH) that uses this likelihood to infer best-fitting parameters from single cell data and quantify their uncertainty using the posterior distribution [15]. Although Bayesian inference is the most complete and rigorous approach, it requires significantly more computation than other approximate methods, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Lin

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding how stochastic gene expression is regulated in biological systems using snapshots of single-cell transcripts requires state-of-the-art methods of computational analysis and statistical inference. A Bayesian approach to statistical inference is the most complete method for model selection and uncertainty quantification of kinetic parameters from single-cell data. This approach is impractical because current numerical algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piece-wise deterministic Markov process. We combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We validated our method of accelerated Bayesian inference on several synthetic data sets. Our results show that kinetic parameters can be reasonably constrained for modestly sampled data sets, if the model is known a priori. If the model is unknown,the Bayesian evidence can be used to rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics, e.g., Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC), often used for model selection.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Model Selection Using the Full Bayesian Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Lin

2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In yeast, decay rates were also determined with a temperature‐sensitive yeast strain that requires heat (or cold) shock that affects cell physiology (Grigull, Mnaimneh, Pootoolal, Robinson, & Hughes, ; Wang et al, ). Recently, several reports demonstrated that imaging technologies, such as smFISH (single molecule FISH) (Gómez‐Schiavon, Chen, West, & Buchler, ; Iyer, Park, & Kim, ; Raj, van den Bogaard, Rifkin, van Oudenaarden, & Tyagi, ; Trcek, Larson, Moldón, Query, & Singer, ), the MS2‐tagging system (Hocine, Raymond, Zenklusen, Chao, & Singer, ; Tutucci et al, ), and TREAT (Horvathova et al, ) can determine the kinetics of mRNAs by counting the absolute number of transcripts in single cells. These imaging technologies measure the rate of transcription and/or decay for specific transcripts, but these methods are typically restricted to a small number of mRNAs.…”

Section: Methods For Measurement Of the Kinetics Of Mrna Dynamicsmentioning

confidence: 99%

Contributions of regulated transcription and mRNA decay to the dynamics of gene expression

Yamada

Akimitsu

2018

WIREs RNA

View full text Add to dashboard Cite

Organisms have acquired sophisticated regulatory networks that control gene expression in response to cellular perturbations. Understanding of the mechanisms underlying the coordinated changes in gene expression in response to external and internal stimuli is a fundamental issue in biology. Recent advances in highthroughput technologies have enabled the measurement of diverse biological information, including gene expression levels, kinetics of gene expression, and interactions among gene expression regulatory molecules. By coupling these technologies with quantitative modeling, we can now uncover the biological roles and mechanisms of gene regulation at the system level. This review consists of two parts. First, we focus on the methods using uridine analogs that measure synthesis and decay rates of RNAs, which demonstrate how cells dynamically change the regulation of gene expression in response to both internal and external cues. Second, we discuss the underlying mechanisms of these changes in kinetics, including the functions of transcription factors and RNA-binding proteins. Overall, this review will help to clarify a system-level view of gene expression programs in cells.

show abstract

“…To match FSP model solutions to single-cell data, one needs to compute and maximize the likelihood of the smFISH data given the FSP model [8, 9, 19, 28, 29]. We assume that measurements at each time point are independent, as justified by the fact that fixation of cells for measurement precludes temporal cell-to-cell correlations.…”

Section: Likelihood Of Smfish Data For Fsp Modelsmentioning

confidence: 99%

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Fox

Neuert

Munsky

2019

Preprint

View full text Add to dashboard Cite

24Modern biological experiments are becoming increasingly complex, and designing these experi-25 ments to yield the greatest possible quantitative insight is an open challenge. Increasingly, compu-26 tational models of complex stochastic biological systems are being used to understand and predict 27 biological behaviors or to infer biological parameters. Such quantitative analyses can also help 28 to improve experiment designs for particular goals, such as to learn more about specific model 29 mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment 30 design is to use the Fisher information matrix (FIM), which quantifies the expected information a 31 particular experiment will reveal about model parameters. The Finite State Projection based FIM 32 (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory 33 systems, whose complex response distributions do not satisfy standard assumptions of Gaussian 34 variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response 35 genes in S. cerevisae under time-varying MAPK induction. We validate this FSP-FIM analysis 36 and use it to optimize the number of cells that should be quantified at particular times to learn as 37 much as possible about the model parameters. We then demonstrate how the FSP-FIM approach 38 can be extended to explore how different measurement times or genetic modifications can help to 39 minimize uncertainty in the sensing of extracellular environments, such as external salinity mod-40 ulations. This work demonstrates the potential of quantitative models to not only make sense of 41 modern biological data sets, but to close the loop between quantitative modeling and experimental 42 data collection. 44The standard approach to design experiments has been to rely entirely on expert knowl-45 edge and intuition. However, as experimental investigations become more complex and 46 seek to examine systems with more subtle non-linear interactions, it becomes much harder 47 to improve experimental designs using intuition alone. This issue has become especially 48 relevant in modern single-cell-single-molecule investigations of gene regulatory processes. 49Performing such powerful, yet complicated experiments involves the selection from among 50 a large number of possible experimental designs, and it is often not clear which designs 51 will provide the most relevant information. A systematic approach to solve this problem is 52 model-driven experiment design, in which one uses an assumed (and potentially incorrect) 53 mathematical model of the system to estimate and optimize the value of potential exper-54 imental settings. In recent years, model-driven experiment design has gained traction for 55 biological models of gene expression, whether in the Bayesian setting [1] or using Fisher 56 information for deterministic models [2], and even in the stochastic, single-cell setting [3-57 6]. Despite the promise and active development of model-driven e...

show abstract

BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

Cited by 43 publications

References 44 publications

Accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Contributions of regulated transcription and mRNA decay to the dynamics of gene expression

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Contact Info

Product

Resources

About