Inference of quantitative models of bacterial promoters from time-series reporter gene data

Diana Stefan 1, 2 Corinne Pinel 1, 2 Stephane Pinhal 1, 2 Eugenio Cinquemani 2 Johannes Geiselmann 1, 2 Hidde De Jong 2
2 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
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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PLoS Computational Biology, Public Library of Science, 2015, 11 (1), pp.e1004028. 〈10.1371/journal.pcbi.1004028〉
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Soumis le : samedi 20 décembre 2014 - 13:48:35
Dernière modification le : mardi 11 septembre 2018 - 09:24:02

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Diana Stefan, Corinne Pinel, Stephane Pinhal, Eugenio Cinquemani, Johannes Geiselmann, et al.. Inference of quantitative models of bacterial promoters from time-series reporter gene data. PLoS Computational Biology, Public Library of Science, 2015, 11 (1), pp.e1004028. 〈10.1371/journal.pcbi.1004028〉. 〈hal-01097632〉

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