Stable Gene Regulatory Network Modeling From Steady-State Data

Abstract

Gene regulatory networks represent an abstract mapping of gene regulations in living cells. They aim to capture dependencies among molecular entities such as transcription factors, proteins and metabolites. In most applications, the regulatory network structure is unknown, and has to be reverse engineered from experimental data consisting of expression levels of the genes usually measured as messenger RNA concentrations in microarray experiments. Steady-state gene expression data are obtained from measurements of the variations in expression activity following the application of small perturbations to equilibrium states in genetic perturbation experiments. In this paper, the least absolute shrinkage and selection operator-vector autoregressive (LASSO-VAR) originally proposed for the analysis of economic time series data is adapted to include a stability constraint for the recovery of a sparse and stable regulatory network that describes data obtained from noisy perturbation experiments. The approach is applied to real experimental data obtained for the SOS pathway in Escherichia coli and the cell cycle pathway for yeast Saccharomyces cerevisiae. Significant features of this method are the ability to recover networks without inputting prior knowledge of the network topology, and the ability to be efficiently applied to large scale networks due to the convex nature of the method.

An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information

“…This dataset was generated by setting a certain time step in the experiment (the step length was 50), and sampling the gene interval under different ultraviolet light intensities. There were four experiments in total, and the preprocessing of the dataset retained the first time point (0) ( Larvie et al , 2016 ; Ronen et al , 2002 ).…”

Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops

“…We show that the motif profile and topological properties of FFLatt network graphs demonstrate a biological stability comparable with other models, such as the NetworkX and GNW algorithms. It is particularly important for network inference methods working with steady-state gene expression data as many of them, for instance Least-Squares with Cut-Off (LSCO; ( Tjärnberg et al, 2013 ), LASSO ( Tibshirani, 1996 ; Friedman et al, 2010 ), LASSO-VAR ( Larvie et al, 2016 ), and GENIE3 ( Huynh-Thu et al, 2010 ) aim to infer a stable static network from steady-state data. To summarize, the FFLatt graph generation algorithm provides an opportunity to simulate biologically meaningful network graphs that can be wired with realistic biological dynamics.…”

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks

“…This network structure of interactions is the GRN. In this work, we will make the additional, very common assumption [5,16], of taking the steady-state expression level which means we will work with static instead of dynamic models.…”