ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data

Minas, Giorgos; Momiji, Hiroshi; Jenkins, D. C.; Costa, María José; Rand, D.A.J.; Finkenstädt, Bärbel

doi:10.1186/s12859-017-1695-8

Cited by 2 publications

(2 citation statements)

References 16 publications

(32 reference statements)

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“…A number of recent methods focus specifically on time series of gene expression [2,26,[47][48][49][50][51]. For the study of oscillatory gene expression, a wide array of studies discuss timeseries data of protein concentrations, such as in figure 1a,b, as well as the description of these data through the autonegative feedback motif (figure 1c).…”

Section: Introductionmentioning

confidence: 99%

Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Burton

Manning

Rattray

et al. 2021

J. R. Soc. Interface.

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Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

show abstract

Section: Introductionmentioning

confidence: 99%

Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Burton

Manning

Rattray

et al. 2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…A number of recent methods focus specifically on time series of gene expression [2;26;47–51] . For the study of oscillatory gene expression, a wide array of studies discuss time series data of protein concentrations, such as in Figure 1A,B, as well as the description of these data through the auto-negative feedback motif (Figure 1C).…”

Section: Introductionmentioning

confidence: 99%

Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

Burton

Manning

Rattray

et al. 2021

Preprint

View full text Add to dashboard Cite

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

show abstract

ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data

Cited by 2 publications

References 16 publications

Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

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