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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 replacing protein concentrations by promoter activities and including global physiological effects.The regulation function of Eqs. 2–3 was fitted using the promoter activities for tar, fliA, and flgM shown in Fig. 3, where the latter two replace the concentrations of FliA and FlgM, respectively. Moreover, global physiological effects are quantified by the activity of the constitutively expressed pRM promoter (Fig. 7). Model predictions are in dark blue (thick solid line), tar reporter data are in light blue (thin blue line and shaded area). The parameters were estimated using a multistart global optimization algorithm (see Methods and materials for details). The best fit returns the value Q = 30.9 for the objective function, for the parameter vector (k0,k1,n,θ,K) = (0.24,13.9,1.2,353,14615). Confidence intervals for the parameter values are reported in Text S10.
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pcbi.1004028.g008: Regulation function of tar fitted to reporter gene data when replacing protein concentrations by promoter activities and including global physiological effects.The regulation function of Eqs. 2–3 was fitted using the promoter activities for tar, fliA, and flgM shown in Fig. 3, where the latter two replace the concentrations of FliA and FlgM, respectively. Moreover, global physiological effects are quantified by the activity of the constitutively expressed pRM promoter (Fig. 7). Model predictions are in dark blue (thick solid line), tar reporter data are in light blue (thin blue line and shaded area). The parameters were estimated using a multistart global optimization algorithm (see Methods and materials for details). The best fit returns the value Q = 30.9 for the objective function, for the parameter vector (k0,k1,n,θ,K) = (0.24,13.9,1.2,353,14615). Confidence intervals for the parameter values are reported in Text S10.

Mentions: We also checked if the proposed extension improves the capability of the model to quantitatively account for the time-varying activity of a FliA-controlled promoter. To this end, we multiplied Eq. 1 with fconst(t), the measured activity of a constitutive promoter:f(t)=fconst(t)k0+k1pA,free(t)nθn+pA,free(t)n.(3)The fits shown in Fig. 8, obtained with the parameter estimation approach outlined in the previous section, are somewhat better than those obtained with a model accounting for the effects of FliA and FlgM only, especially for the ΔrpoS-M9 and ΔcpxR-M9 conditions. The better fit is also reflected in a lower value of the fitting error (Q = 30.9 vs Q = 33.4). Notice that the extended model has the same number of parameters as the model without global physiological effects in Eqs. 1–2, so that the improvement is not simply due to an increase in the degree of freedom of the model. The parameter estimates are quite similar to those of the previous model.


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 replacing protein concentrations by promoter activities and including global physiological effects.The regulation function of Eqs. 2–3 was fitted using the promoter activities for tar, fliA, and flgM shown in Fig. 3, where the latter two replace the concentrations of FliA and FlgM, respectively. Moreover, global physiological effects are quantified by the activity of the constitutively expressed pRM promoter (Fig. 7). Model predictions are in dark blue (thick solid line), tar reporter data are in light blue (thin blue line and shaded area). The parameters were estimated using a multistart global optimization algorithm (see Methods and materials for details). The best fit returns the value Q = 30.9 for the objective function, for the parameter vector (k0,k1,n,θ,K) = (0.24,13.9,1.2,353,14615). Confidence intervals for the parameter values are reported in Text S10.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004028.g008: Regulation function of tar fitted to reporter gene data when replacing protein concentrations by promoter activities and including global physiological effects.The regulation function of Eqs. 2–3 was fitted using the promoter activities for tar, fliA, and flgM shown in Fig. 3, where the latter two replace the concentrations of FliA and FlgM, respectively. Moreover, global physiological effects are quantified by the activity of the constitutively expressed pRM promoter (Fig. 7). Model predictions are in dark blue (thick solid line), tar reporter data are in light blue (thin blue line and shaded area). The parameters were estimated using a multistart global optimization algorithm (see Methods and materials for details). The best fit returns the value Q = 30.9 for the objective function, for the parameter vector (k0,k1,n,θ,K) = (0.24,13.9,1.2,353,14615). Confidence intervals for the parameter values are reported in Text S10.
Mentions: We also checked if the proposed extension improves the capability of the model to quantitatively account for the time-varying activity of a FliA-controlled promoter. To this end, we multiplied Eq. 1 with fconst(t), the measured activity of a constitutive promoter:f(t)=fconst(t)k0+k1pA,free(t)nθn+pA,free(t)n.(3)The fits shown in Fig. 8, obtained with the parameter estimation approach outlined in the previous section, are somewhat better than those obtained with a model accounting for the effects of FliA and FlgM only, especially for the ΔrpoS-M9 and ΔcpxR-M9 conditions. The better fit is also reflected in a lower value of the fitting error (Q = 30.9 vs Q = 33.4). Notice that the extended model has the same number of parameters as the model without global physiological effects in Eqs. 1–2, so that the improvement is not simply due to an increase in the degree of freedom of the model. The parameter estimates are quite similar to those of the previous model.

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