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Inference of quantitative models of bacterial promoters from time-series reporter gene data.

Stefan D, Pinel C, Pinhal S, Cinquemani E, Geiselmann J, de Jong H - PLoS Comput. Biol. (2015)

Bottom Line: Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected.From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression.In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

View Article: PubMed Central - PubMed

Affiliation: INRIA Grenoble - Rhône-Alpes, Grenoble, France; Laboratoire Interdisciplinaire de Physique (LIPhy, CNRS UMR 5588), Université Joseph Fourier, Grenoble, France.

ABSTRACT
The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

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Regulation function of tar fitted to reporter gene data when reconstructing protein concentrations from the reporter gene data and including global physiological effects.The regulation function of Eqs. 2–3 was fit to the data using the promoter activity for tar (Fig. 3), concentrations of FliA and FlgM reconstructed from the activities of their promoters for physiologically realistic half-lives (Fig. 9 and Text S7), and the activity of the constitutively expressed pRM promoter quantifying global physiological effects (Fig. 7). Model predictions are in thick black and blue lines, tar reporter data are in light blue (thin line and shaded area). Three fits are shown: the best fit for measured half-lives of FliA and FlgM of 30 min and 18 min, respectively (thick, blue solid line, Q = 25.5, (k0, k1, n, θ, K) = (0.26, 5.0, 1.99, 3542, 447499)) and two other fits for comparable half-lives (blue and black dashed lines). Parameter values were estimated using a multistart global optimization algorithm (see Methods and materials for details). Their confidence intervals are reported in Text S10.
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pcbi.1004028.g010: Regulation function of tar fitted to reporter gene data when reconstructing protein concentrations from the reporter gene data and including global physiological effects.The regulation function of Eqs. 2–3 was fit to the data using the promoter activity for tar (Fig. 3), concentrations of FliA and FlgM reconstructed from the activities of their promoters for physiologically realistic half-lives (Fig. 9 and Text S7), and the activity of the constitutively expressed pRM promoter quantifying global physiological effects (Fig. 7). Model predictions are in thick black and blue lines, tar reporter data are in light blue (thin line and shaded area). Three fits are shown: the best fit for measured half-lives of FliA and FlgM of 30 min and 18 min, respectively (thick, blue solid line, Q = 25.5, (k0, k1, n, θ, K) = (0.26, 5.0, 1.99, 3542, 447499)) and two other fits for comparable half-lives (blue and black dashed lines). Parameter values were estimated using a multistart global optimization algorithm (see Methods and materials for details). Their confidence intervals are reported in Text S10.

Mentions: We first verified that a model using the reconstructed FliA and FlgM concentrations as regulators of tar, in addition to the activity of the gene expression machinery, is structurally compatible with the data. Minimal sign pattern analysis accepted the expected pattern of regulatory interactions. Second, we identified the gene regulation model of Eqs. 2–3 from the data, with the estimated FliA and FlgM concentrations for pA and pM, respectively. As shown in Fig. 10, the model better captures the quantitative trend in the data, including in WT-LB, where the improvement was moderate though, and the resulting fit still improvable (Q = 25.5). Since the half-lives were taken to be those measured for a different species in growth conditions that are similar but not identical to ours, and measurement errors were not reported, we slightly relaxed the reported values. This did not much change the quality of the fit (Fig. 10). We conclude that even approximately correct half-live values may allow the results of the inference process to be improved.


Inference of quantitative models of bacterial promoters from time-series reporter gene data.

Stefan D, Pinel C, Pinhal S, Cinquemani E, Geiselmann J, de Jong H - PLoS Comput. Biol. (2015)

Regulation function of tar fitted to reporter gene data when reconstructing protein concentrations from the reporter gene data and including global physiological effects.The regulation function of Eqs. 2–3 was fit to the data using the promoter activity for tar (Fig. 3), concentrations of FliA and FlgM reconstructed from the activities of their promoters for physiologically realistic half-lives (Fig. 9 and Text S7), and the activity of the constitutively expressed pRM promoter quantifying global physiological effects (Fig. 7). Model predictions are in thick black and blue lines, tar reporter data are in light blue (thin line and shaded area). Three fits are shown: the best fit for measured half-lives of FliA and FlgM of 30 min and 18 min, respectively (thick, blue solid line, Q = 25.5, (k0, k1, n, θ, K) = (0.26, 5.0, 1.99, 3542, 447499)) and two other fits for comparable half-lives (blue and black dashed lines). Parameter values were estimated using a multistart global optimization algorithm (see Methods and materials for details). Their confidence intervals are reported in Text S10.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4295839&req=5

pcbi.1004028.g010: Regulation function of tar fitted to reporter gene data when reconstructing protein concentrations from the reporter gene data and including global physiological effects.The regulation function of Eqs. 2–3 was fit to the data using the promoter activity for tar (Fig. 3), concentrations of FliA and FlgM reconstructed from the activities of their promoters for physiologically realistic half-lives (Fig. 9 and Text S7), and the activity of the constitutively expressed pRM promoter quantifying global physiological effects (Fig. 7). Model predictions are in thick black and blue lines, tar reporter data are in light blue (thin line and shaded area). Three fits are shown: the best fit for measured half-lives of FliA and FlgM of 30 min and 18 min, respectively (thick, blue solid line, Q = 25.5, (k0, k1, n, θ, K) = (0.26, 5.0, 1.99, 3542, 447499)) and two other fits for comparable half-lives (blue and black dashed lines). Parameter values were estimated using a multistart global optimization algorithm (see Methods and materials for details). Their confidence intervals are reported in Text S10.
Mentions: We first verified that a model using the reconstructed FliA and FlgM concentrations as regulators of tar, in addition to the activity of the gene expression machinery, is structurally compatible with the data. Minimal sign pattern analysis accepted the expected pattern of regulatory interactions. Second, we identified the gene regulation model of Eqs. 2–3 from the data, with the estimated FliA and FlgM concentrations for pA and pM, respectively. As shown in Fig. 10, the model better captures the quantitative trend in the data, including in WT-LB, where the improvement was moderate though, and the resulting fit still improvable (Q = 25.5). Since the half-lives were taken to be those measured for a different species in growth conditions that are similar but not identical to ours, and measurement errors were not reported, we slightly relaxed the reported values. This did not much change the quality of the fit (Fig. 10). We conclude that even approximately correct half-live values may allow the results of the inference process to be improved.

Bottom Line: Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected.From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression.In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

View Article: PubMed Central - PubMed

Affiliation: INRIA Grenoble - Rhône-Alpes, Grenoble, France; Laboratoire Interdisciplinaire de Physique (LIPhy, CNRS UMR 5588), Université Joseph Fourier, Grenoble, France.

ABSTRACT
The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

Show MeSH
Related in: MedlinePlus