Reverse engineering gene regulatory networks by modular response analysis – a benchmark

2018 ◽  
Vol 62 (4) ◽  
pp. 535-547 ◽  
Author(s):  
Bertram Klinger ◽  
Nils Blüthgen
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Signal To Noise Ratio ◽  
Protein Composition ◽  
Genetic Network ◽  
Response Analysis ◽  
Perturbation Data ◽  
Gene Regulatory ◽  
Modular Response Analysis

Gene regulatory networks control the cellular phenotype by changing the RNA and protein composition. Despite its importance, the gene regulatory network in higher organisms is only partly mapped out. Here, we investigate the potential of reverse engineering methods to unravel the structure of these networks. Particularly, we focus on modular response analysis (MRA), a method that can disentangle networks from perturbation data. We benchmark a version of MRA that was previously successfully applied to reconstruct a signalling-driven genetic network, termed MLMSMRA, to test cases mimicking various aspects of gene regulatory networks. We then investigate the performance in comparison with other MRA realisations and related methods. The benchmark shows that MRA has the potential to predict functional interactions, but also shows that successful application of MRA is restricted to small sparse networks and to data with a low signal-to-noise ratio.

Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm

2016 ◽  
Vol 14 (03) ◽  
pp. 1650010 ◽  
Author(s):  
Sudip Mandal ◽  
Abhinandan Khan ◽  
Goutam Saha ◽  
Rajat Kumar Pal
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Real Life ◽  
Bat Algorithm ◽  
Genetic Network ◽  
Model Parameters ◽  
Mathematical Tool ◽  
Gene Regulatory ◽  
Artificial Network

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

scholarly journals Reverse engineering gene regulatory networks

2009 ◽  
Vol 26 (1) ◽  
pp. 76-97 ◽  
Author(s):  
Yufei Huang ◽  
I. Tienda-Luna ◽  
Yufeng Wang
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

2019 ◽  
Vol 36 (1) ◽  
pp. 197-204 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Structural Equation ◽  
Regulatory Networks ◽  
Supplementary Information ◽  
Specific Gene ◽  
Joint Inference ◽  
Perturbation Data ◽  
Gene Regulatory ◽  
The Difference

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

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