51. Inference of quantitative models of bacterial promoters from time-series reporter gene data
- Author
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Stefan, Diana, Pinel, Corinne, Pinhal, Stéphane, Cinquemani, Eugenio, Geiselmann, Johannes, de Jong, Hidde, Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, and Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes
- Subjects
lcsh:Biology (General) ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,lcsh:QH301-705.5 ,Research Article - 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., Author Summary A wide variety of methods for the reverse engineering of regulatory networks and the identification of quantitative regulation functions are available. We investigate some common assumptions that are made in the application of these methods to time-series transcriptomics data, in the context of a central module in the motility network of E. coli. We show that these assumptions, which hypothesize that mRNA concentrations are good proxies for protein concentrations and that the gene expression machinery is equally active across different physiological conditions, are often not valid and may lead to biased inference results. We also show how models of gene expression can be used in combination with suitable experimental controls to correct for this bias and improve the inference process. The contribution of our work is thus not the addition of another method to the rich store of available reverse engineering algorithms, but lies in the critical examination of the information provided by the experimental data and new ways to exploit this information in the algorithms. The proposed approach is relevant for a wide range of applications using time-series transcriptomics data. For the motility system under study, it has underlined the importance of global physiological effects, the active degradation of the transcription factor FliA as well as the secretion of the anti-sigma factor FlgM for the network dynamics.
- Published
- 2015