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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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Pattern of regulatory interactions for tar and other class 3 genes.A: FliA activates and FlgM inhibits tar. B: Idem, but with global physiological effects, measured by the activity of the pRM promoter.
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pcbi.1004028.g004: Pattern of regulatory interactions for tar and other class 3 genes.A: FliA activates and FlgM inhibits tar. B: Idem, but with global physiological effects, measured by the activity of the pRM promoter.

Mentions: We expect FliA to be an activator and FlgM an inhibitor of target genes like tar and other class 3 genes (Fig. 4A). In order to check if this regulatory pattern is consistent with the reporter gene data, we used minimal sign pattern analysis [42]. This approach exploits time-series data to invalidate patterns of regulatory interactions, based on the assumption that the activity of a gene is a monotonic function of its regulators. For the patterns that remain after the invalidation step, so-called minimal sign patterns are computed, equivalent to the regulatory structures in Fig. 4. These patterns are minimal in the sense that removing any of the regulators results in an inconsistency with the data, while adding other regulators preserves consistency (see Methods and materials and Text S5 for details on the method).


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)

Pattern of regulatory interactions for tar and other class 3 genes.A: FliA activates and FlgM inhibits tar. B: Idem, but with global physiological effects, measured by the activity of the pRM promoter.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004028.g004: Pattern of regulatory interactions for tar and other class 3 genes.A: FliA activates and FlgM inhibits tar. B: Idem, but with global physiological effects, measured by the activity of the pRM promoter.
Mentions: We expect FliA to be an activator and FlgM an inhibitor of target genes like tar and other class 3 genes (Fig. 4A). In order to check if this regulatory pattern is consistent with the reporter gene data, we used minimal sign pattern analysis [42]. This approach exploits time-series data to invalidate patterns of regulatory interactions, based on the assumption that the activity of a gene is a monotonic function of its regulators. For the patterns that remain after the invalidation step, so-called minimal sign patterns are computed, equivalent to the regulatory structures in Fig. 4. These patterns are minimal in the sense that removing any of the regulators results in an inconsistency with the data, while adding other regulators preserves consistency (see Methods and materials and Text S5 for details on the method).

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