Dataset Information

Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.

ABSTRACT: Bayesian and non-Bayesian moment-based inference methods are commonly used to estimate the parameters defining stochastic models of gene regulatory networks from noisy single cell or population snapshot data. However, a systematic investigation of the accuracy of the predictions of these methods remains missing. Here, we present the results of such a study using synthetic noisy data of a negative auto-regulatory transcriptional feedback loop, one of the most common building blocks of complex gene regulatory networks. We study the error in parameter estimation as a function of (i) number of cells in each sample; (ii) the number of time points; (iii) the highest-order moment of protein fluctuations used for inference; (iv) the moment-closure method used for likelihood approximation. We find that for sample sizes typical of flow cytometry experiments, parameter estimation by maximizing the likelihood is as accurate as using Bayesian methods but with a much reduced computational time. We also show that the choice of moment-closure method is the crucial factor determining the maximum achievable accuracy of moment-based inference methods. Common likelihood approximation methods based on the linear noise approximation or the zero cumulants closure perform poorly for feedback loops with large protein-DNA binding rates or large protein bursts; this is exacerbated for highly heterogeneous cell populations. By contrast, approximating the likelihood using the linear-mapping approximation or conditional derivative matching leads to highly accurate parameter estimates for a wide range of conditions.

SUBMITTER: Cao Z

PROVIDER: S-EPMC6505555 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.

Cao Zhixing Z Grima Ramon R

Journal of the Royal Society, Interface 20190401 153

Bayesian and non-Bayesian moment-based inference methods are commonly used to estimate the parameters defining stochastic models of gene regulatory networks from noisy single cell or population snapshot data. However, a systematic investigation of the accuracy of the predictions of these methods remains missing. Here, we present the results of such a study using synthetic noisy data of a negative auto-regulatory transcriptional feedback loop, one of the most common building blocks of complex gen ...[more]

PMID: 30940028

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Project description:The circadian clock system is based on interlocked positive and negative transcriptional and translational feedback loops of core clock genes and their encoded proteins. The mammalian circadian clock system has been extensively investigated using mouse models, but has been poorly investigated in diurnal ruminants. In this study, goat embryonic fibroblasts (GEFs) were isolated and used as a cell model to elucidate the caprine circadian clock system. Real-time quantitative PCR analysis showed that several clock genes and clock-controlled genes were rhythmically expressed in GEFs over a 24 h period after dexamethasone stimulation. Immunofluorescence revealed that gBMAL1 and gNR1D1 proteins were expressed in GEFs, and western blotting analysis further verified that the proteins were expressed with circadian rhythmic changes. Diurnal changes in clock and clock-controlled gene expression at the mRNA and protein levels were also observed in goat liver and kidney tissues at two representative time points in vivo. Amino acid sequences and tertiary structures of goat BMAL1 and CLOCK proteins were found to be highly homologous to those in mice and humans. In addition, a set of goat representative clock gene orthologs and the promoter regions of two clock genes of goats and mice were cloned. Dual-luciferase reporter assays showed that gRORα could activate the promoter activity of the goat BMAL1, while gNR1D1 repressed it. The elevated pGL4.10-gNR1D1-Promoter-driven luciferase activity induced by mBMAL1/mCLOCK was much higher than that induced by gBMAL1/gCLOCK, and the addition of gCRY2 or mPER2 repressed it. Real-time bioluminescence assays revealed that the transcriptional activity of BMAL1 and NR1D1 in goats and mice exhibited rhythmic changes over a period of approximately 24 h in NIH3T3 cells or GEFs. Notably, the amplitudes of gBMAL1 and gNR1D1 promoter-driven luciferase oscillations in NIH3T3 cells were higher than those in GEFs, while mBMAL1 and mNR1D1 promoter-driven luciferase oscillations in NIH3T3 cells had the highest amplitude. In sum, transcriptional and translational loops of the mammalian circadian clock system were found to be broadly conserved in goats and not as robust as those found in mice, at least in the current experimental models. Further studies are warranted to elucidate the specific molecular mechanisms involved.

Dataset Information

Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.

Publications

Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.

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