Dynamic modeling of gene expression in prokaryotes: application to glucose-lactose diauxie in Escherichia coli

Coexpression of genes or, more generally, similarity in the expression profiles poses an unsurmountable obstacle to inferring the gene regulatory network (GRN) based solely on data from DNA microarray time series. Clustering of genes with similar expression profiles allows for a course-grained view...

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Veröffentlicht in:Systems and synthetic biology Jg. 5; H. 1-2; S. 33 - 43
Hauptverfasser: Albert, Jaroslav, Rooman, Marianne
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.06.2011
Springer Nature B.V
Schlagworte:
Biomedical and Life Sciences
Biomedicine
Computational Biology/Bioinformatics
DNA microarrays
E coli
Escherichia coli
Gene expression
Genomes
Linear algebra
Metabolomics
Original
Original Research Article
Prokaryotes
Systems Biology
DNA microarray
Dynamic robustness
Gene regulatory networks
Gene product degradation
Gene clustering
Optimization
ISSN:1872-5325, 1872-5333, 1872-5333
Online-Zugang:Volltext
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Zusammenfassung:Coexpression of genes or, more generally, similarity in the expression profiles poses an unsurmountable obstacle to inferring the gene regulatory network (GRN) based solely on data from DNA microarray time series. Clustering of genes with similar expression profiles allows for a course-grained view of the GRN and a probabilistic determination of the connectivity among the clusters. We present a model for the temporal evolution of a gene cluster network which takes into account interactions of gene products with genes and, through a non-constant degradation rate, with other gene products. The number of model parameters is reduced by using polynomial functions to interpolate temporal data points. In this manner, the task of parameter estimation is reduced to a system of linear algebraic equations, thus making the computation time shorter by orders of magnitude. To eliminate irrelevant networks, we test each GRN for stability with respect to parameter variations, and impose restrictions on its behavior near the steady state. We apply our model and methods to DNA microarray time series’ data collected on Escherichia coli during glucose-lactose diauxie and infer the most probable cluster network for different phases of the experiment.
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ISSN:1872-5325
1872-5333
1872-5333
DOI:10.1007/s11693-011-9079-2