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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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Related in: MedlinePlus

Regulation function of tar fitted to reporter gene data when reconstructing protein concentrations from the reporter gene data for physiologically realistic half-lives and including global physiological effects.As in Fig. 10, but the half-lives have now also been estimated from the data, within a physiologically plausible range. Model predictions are in thick solid and dashed blue lines, tar reporter data are in light blue (thin line and shaded area). Two example fits are shown, namely the best fit for estimated half-lives of FliA and FlgM (solid line, Q = 21.0, (k0,k1,n,θ,K) = (0.22,6.6,1.38,6252,47467)) and another example of a high-ranking fit (dashed line). In the case of the best fit, the half-lives of FliA are equal to (60,30,24,30,60) min in the (ΔrpoS, ΔcpxR, ΔcsgD-M9, ΔcsgD-LB, WT-LB) conditions, respectively, while the half-lives of FlgM are equal to (45,7,24,11,9) min. Confidence intervals for the parameter values are reported in Text S10.
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pcbi.1004028.g012: Regulation function of tar fitted to reporter gene data when reconstructing protein concentrations from the reporter gene data for physiologically realistic half-lives and including global physiological effects.As in Fig. 10, but the half-lives have now also been estimated from the data, within a physiologically plausible range. Model predictions are in thick solid and dashed blue lines, tar reporter data are in light blue (thin line and shaded area). Two example fits are shown, namely the best fit for estimated half-lives of FliA and FlgM (solid line, Q = 21.0, (k0,k1,n,θ,K) = (0.22,6.6,1.38,6252,47467)) and another example of a high-ranking fit (dashed line). In the case of the best fit, the half-lives of FliA are equal to (60,30,24,30,60) min in the (ΔrpoS, ΔcpxR, ΔcsgD-M9, ΔcsgD-LB, WT-LB) conditions, respectively, while the half-lives of FlgM are equal to (45,7,24,11,9) min. Confidence intervals for the parameter values are reported in Text S10.

Mentions: Fig. 11 shows the results for the structural inference of tar regulators. As can be seen, almost all combinations of half-lives are compatible with activation of tar by FliA and the gene expression machinery as well as with inhibition by FlgM. This means that the returned structure of interactions is robust over the range of half-lives, a desirable property for network inference. Fig. 12 illustrates that the obtained quantitative regulation function of tar activity fits the data better than in all other previously considered situations (Q = 21.0), while the parameter values are similar to those obtained in the previous sections. Although we substantially relaxed the possible half-live values of FliA and FlgM, it is remarkable that the optimal values are close to the reported values for LB medium (Fig. 12). This emphasizes the importance of active degradation of FliA and secretion of FlgM for the dynamics of the motility network. Moreover, while the proportion of FliA released by FlgM varies across conditions, most FliA is predicted to be free over the duration of the experiment (Text S9). This is also intuitively expected, as FlgM is actively exported in the exponential growth phase considered.


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 for physiologically realistic half-lives and including global physiological effects.As in Fig. 10, but the half-lives have now also been estimated from the data, within a physiologically plausible range. Model predictions are in thick solid and dashed blue lines, tar reporter data are in light blue (thin line and shaded area). Two example fits are shown, namely the best fit for estimated half-lives of FliA and FlgM (solid line, Q = 21.0, (k0,k1,n,θ,K) = (0.22,6.6,1.38,6252,47467)) and another example of a high-ranking fit (dashed line). In the case of the best fit, the half-lives of FliA are equal to (60,30,24,30,60) min in the (ΔrpoS, ΔcpxR, ΔcsgD-M9, ΔcsgD-LB, WT-LB) conditions, respectively, while the half-lives of FlgM are equal to (45,7,24,11,9) min. 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.g012: Regulation function of tar fitted to reporter gene data when reconstructing protein concentrations from the reporter gene data for physiologically realistic half-lives and including global physiological effects.As in Fig. 10, but the half-lives have now also been estimated from the data, within a physiologically plausible range. Model predictions are in thick solid and dashed blue lines, tar reporter data are in light blue (thin line and shaded area). Two example fits are shown, namely the best fit for estimated half-lives of FliA and FlgM (solid line, Q = 21.0, (k0,k1,n,θ,K) = (0.22,6.6,1.38,6252,47467)) and another example of a high-ranking fit (dashed line). In the case of the best fit, the half-lives of FliA are equal to (60,30,24,30,60) min in the (ΔrpoS, ΔcpxR, ΔcsgD-M9, ΔcsgD-LB, WT-LB) conditions, respectively, while the half-lives of FlgM are equal to (45,7,24,11,9) min. Confidence intervals for the parameter values are reported in Text S10.
Mentions: Fig. 11 shows the results for the structural inference of tar regulators. As can be seen, almost all combinations of half-lives are compatible with activation of tar by FliA and the gene expression machinery as well as with inhibition by FlgM. This means that the returned structure of interactions is robust over the range of half-lives, a desirable property for network inference. Fig. 12 illustrates that the obtained quantitative regulation function of tar activity fits the data better than in all other previously considered situations (Q = 21.0), while the parameter values are similar to those obtained in the previous sections. Although we substantially relaxed the possible half-live values of FliA and FlgM, it is remarkable that the optimal values are close to the reported values for LB medium (Fig. 12). This emphasizes the importance of active degradation of FliA and secretion of FlgM for the dynamics of the motility network. Moreover, while the proportion of FliA released by FlgM varies across conditions, most FliA is predicted to be free over the duration of the experiment (Text S9). This is also intuitively expected, as FlgM is actively exported in the exponential growth phase considered.

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