Structural and Parametric Identification of Bacterial Regulatory Networks

Released on by Diana Stefan
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  • Structural and Parametric Identification of Bacterial Regulatory Networks Book Detail

  • Author : Diana Stefan
  • Release Date : 2014
  • Publisher :
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  • Pages : 0
  • ISBN 13 :
  • File Size : 56,56 MB
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Structural and Parametric Identification of Bacterial Regulatory Networks by Diana Stefan PDF Summary

Book Description: High-throughput technologies yield large amounts of data about the steady-state levels and the dynamical changes of gene expression in bacteria. An important challenge for the biological interpretation of these data consists in deducing the topology of the underlying regulatory network as well as quantitative gene regulation functions from such data. A large number of inference methods have been proposed in the literature and have been successful in a variety of applications, although several problems remain. We focus here on improving two aspects of the inference methods. First, transcriptome data reflect the abundance of mRNA, whereas the components that regulate are most often the proteins coded by the mRNAs. Although the concentrations of mRNA and protein correlate reasonably during steady-state growth, this correlation becomes much more tenuous in time-series data acquired during growth transitions in bacteria because of the very different half-lives of proteins and mRNA. Second, the dynamics of gene expression is not only controlled by transcription factors and other specific regulators, but also by global physiological effects that modify the activity of all genes. For example, the concentrations of (free) RNA polymerase and the concentration of ribosomes vary strongly with growth rate. We therefore have to take into account such effects when trying to reconstruct a regulatory network from gene expression data. We propose here a combined experimental and computational approach to address these two fundamental problems in the inference of quantitative models of the activity of bacterial promoters from time-series gene expression data. We focus on the case where the dynamics of gene expression is measured in vivo and in real time by means of fluorescent reporter genes. Our network reconstruction approach accounts for the differences between mRNA and protein half-lives and takes into account global physiological effects. When the half-lives of the proteins are available, the measurement models used for deriving the activities of genes from fluorescence data are integrated to yield estimates of protein concentrations. The global physiological state of the cell is estimated from the activity of a phage promoter, whose expression is not controlled by any transcription factor and depends only on the activity of the transcriptional and translational machinery. We apply the approach to a central module in the regulatory network controlling motility and the chemotaxis system in Escherichia coli. This module comprises the FliA, FlgM and tar genes. FliA is a sigma factor that directs RNA polymerase to operons coding for components of the flagellar assembly. The effect of FliA is counteracted by the antisigma factor FlgM, itself transcribed by FliA. The third component of the network, tar, codes for the aspartate chemoreceptor protein Tar and is directly transcribed by the FliA-containing RNA polymerase holoenzyme. The FliA-FlgM module is particularly well-suited for studying the inference problems considered here, since the network has been well-studied and protein half-lives play an important role in its functioning. We stimulated the FliA-FlgM module in a variety of wild-type and mutant strains and different growth media. The measured transcriptional response of the genes was used to systematically test the information required for the reliable inference of the regulatory interactions and quantitative predictive models of gene regulation. Our results show that for the reliable reconstruction of transcriptional regulatory networks in bacteria it is necessary to include global effects into the network model and explicitly deduce protein concentrations from the observed expression profiles. Our approach should be generally applicable to a large variety of network inference problems and we discuss limitations and possible extensions of the method.

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