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

TL;DR

This paper introduces a dynamic model for gene expression in prokaryotes, specifically applied to E. coli during glucose-lactose diauxie, enabling efficient inference of gene cluster networks from microarray data.

Contribution

It presents a novel dynamic modeling approach that reduces parameter estimation complexity and incorporates stability testing to infer gene cluster networks from time series data.

Findings

Identified the most probable gene cluster networks during different diauxie phases.

Reduced computation time for parameter estimation by using polynomial interpolation.

Validated the model's ability to infer biologically relevant gene interactions.

Abstract

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.