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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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Heatmap of the fitting residuals for simulated data generated for different protein half-lives and for different strengths of global physiological effects.A: For all different combinations of 33 half-lives of FlgM (horizontal axis) and FliA (vertical axis), the residual of the fit for a model ignoring protein kinetics is represented by the color code reported in the right bar. For clarity of presentation, the residual values Q have been normalized with respect to the maximum value of Q over all half-life combinations. The combination corresponding to the measured half-lives in LB medium is marked with a light blue square (18 min for FlgM, 30 min for FliA). B: For 26 different values of the strength parameter α, defined in Eq. 4, the residual of the fit by a model ignoring global physiological effects is represented by the color code. The residual values Q have been normalized with respect to the maximum value of Q over the different strengths of physiological effects. The value corresponding to the real data is marked with a light blue rectangle (α = 1).
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pcbi.1004028.g013: Heatmap of the fitting residuals for simulated data generated for different protein half-lives and for different strengths of global physiological effects.A: For all different combinations of 33 half-lives of FlgM (horizontal axis) and FliA (vertical axis), the residual of the fit for a model ignoring protein kinetics is represented by the color code reported in the right bar. For clarity of presentation, the residual values Q have been normalized with respect to the maximum value of Q over all half-life combinations. The combination corresponding to the measured half-lives in LB medium is marked with a light blue square (18 min for FlgM, 30 min for FliA). B: For 26 different values of the strength parameter α, defined in Eq. 4, the residual of the fit by a model ignoring global physiological effects is represented by the color code. The residual values Q have been normalized with respect to the maximum value of Q over the different strengths of physiological effects. The value corresponding to the real data is marked with a light blue rectangle (α = 1).

Mentions: To evaluate the importance of protein half-lives, we simulated FliA and FlgM concentration profiles for half-lives ranging between 7 minutes and 16 hours. The other relevant parameters in the model (k0, k1, n, θ, K) were fixed in agreement with the best fit obtained for the reference half-lives of 30 min for FliA and 18 min for FlgM, shown in Fig. 10. More precisely, the relative position of the parameter values within the interval of physiologically plausible values, which may depend on the FliA and FlgM concentrations, as explained in the Methods and materials, was conserved across conditions. Activity profiles of tar were then generated in accordance with Eqs. 2–3 based on the experimentally measured pRM activities. We then attempted to identify from these simulated data a gene regulation model accounting for the global physiological effects, but using promoter activities in place of FliA and FlgM concentrations. The results are reported in Fig. 13.


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)

Heatmap of the fitting residuals for simulated data generated for different protein half-lives and for different strengths of global physiological effects.A: For all different combinations of 33 half-lives of FlgM (horizontal axis) and FliA (vertical axis), the residual of the fit for a model ignoring protein kinetics is represented by the color code reported in the right bar. For clarity of presentation, the residual values Q have been normalized with respect to the maximum value of Q over all half-life combinations. The combination corresponding to the measured half-lives in LB medium is marked with a light blue square (18 min for FlgM, 30 min for FliA). B: For 26 different values of the strength parameter α, defined in Eq. 4, the residual of the fit by a model ignoring global physiological effects is represented by the color code. The residual values Q have been normalized with respect to the maximum value of Q over the different strengths of physiological effects. The value corresponding to the real data is marked with a light blue rectangle (α = 1).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004028.g013: Heatmap of the fitting residuals for simulated data generated for different protein half-lives and for different strengths of global physiological effects.A: For all different combinations of 33 half-lives of FlgM (horizontal axis) and FliA (vertical axis), the residual of the fit for a model ignoring protein kinetics is represented by the color code reported in the right bar. For clarity of presentation, the residual values Q have been normalized with respect to the maximum value of Q over all half-life combinations. The combination corresponding to the measured half-lives in LB medium is marked with a light blue square (18 min for FlgM, 30 min for FliA). B: For 26 different values of the strength parameter α, defined in Eq. 4, the residual of the fit by a model ignoring global physiological effects is represented by the color code. The residual values Q have been normalized with respect to the maximum value of Q over the different strengths of physiological effects. The value corresponding to the real data is marked with a light blue rectangle (α = 1).
Mentions: To evaluate the importance of protein half-lives, we simulated FliA and FlgM concentration profiles for half-lives ranging between 7 minutes and 16 hours. The other relevant parameters in the model (k0, k1, n, θ, K) were fixed in agreement with the best fit obtained for the reference half-lives of 30 min for FliA and 18 min for FlgM, shown in Fig. 10. More precisely, the relative position of the parameter values within the interval of physiologically plausible values, which may depend on the FliA and FlgM concentrations, as explained in the Methods and materials, was conserved across conditions. Activity profiles of tar were then generated in accordance with Eqs. 2–3 based on the experimentally measured pRM activities. We then attempted to identify from these simulated data a gene regulation model accounting for the global physiological effects, but using promoter activities in place of FliA and FlgM concentrations. The results are reported in Fig. 13.

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