Construction of gene interaction and regulatory networks in bovine skeletal muscle from expression data

2005 ◽  
Vol 45 (8) ◽  
pp. 821 ◽  
Author(s):  
A. Reverter ◽  
W. Barris ◽  
N. Moreno-Sánchez ◽  
S. McWilliam ◽  
Y. H. Wang ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Mixed Model ◽  
Gene Interaction ◽  
Expression Data ◽  
Experimental Conditions ◽  
The Usa ◽  
Biological Interpretation ◽  
Bovine Skeletal Muscle

We propose a data-driven reverse engineering approach to isolate the components of a gene interaction and regulatory network. We apply this method to the construction of a network for bovine skeletal muscle. Key nodes in the network include muscle-specific genes and transcription factors. muscle-specific genes are identified from data mining the USA National Cancer Institute, Cancer Genome Anatomy Project database, while transcription factors are predicted by accurate function annotation. A total of 5 microarray studies spanning 78 hybridisations and 23 different experimental conditions provided raw expression data. A recently-reported analytical method based on multivariate mixed-model equations is used to compute gene co-expression measures across 624 genes. The resulting network included 102 genes (of which 40 were muscle-specific genes and 7 were transcription factors) that clustered in 7 distinct modules with clear biological interpretation.

A gene coexpression network for bovine skeletal muscle inferred from microarray data

2006 ◽  
Vol 28 (1) ◽  
pp. 76-83 ◽  
Author(s):  
Antonio Reverter ◽  
Nicholas J. Hudson ◽  
Yonghong Wang ◽  
Siok-Hwee Tan ◽  
Wes Barris ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Large Scale ◽  
Mixed Model ◽  
Structural Proteins ◽  
Joint Analysis ◽  
Experimental Conditions ◽  
Model Equations ◽  
Highly Correlated ◽  
Bovine Skeletal Muscle ◽  
Fast Twitch

We present the application of large-scale multivariate mixed-model equations to the joint analysis of nine gene expression experiments in beef cattle muscle and fat tissues with a total of 147 hybridizations, and we explore 47 experimental conditions or treatments. Using a correlation-based method, we constructed a gene network for 822 genes. Modules of muscle structural proteins and enzymes, extracellular matrix, fat metabolism, and protein synthesis were clearly evident. Detailed analysis of the network identified groupings of proteins on the basis of physical association. For example, expression of three components of the z-disk, MYOZ1, TCAP, and PDLIM3, was significantly correlated. In contrast, expression of these z-disk proteins was not highly correlated with the expression of a cluster of thick (myosins) and thin (actin and tropomyosins) filament proteins or of titin, the third major filament system. However, expression of titin was itself not significantly correlated with the cluster of thick and thin filament proteins and enzymes. Correlation in expression of many fast-twitch muscle structural proteins and enzymes was observed, but slow-twitch-specific proteins were not correlated with the fast-twitch proteins or with each other. In addition, a number of significant associations between genes and transcription factors were also identified. Our results not only recapitulate the known biology of muscle but have also started to reveal some of the underlying associations between and within the structural components of skeletal muscle.

scholarly journals Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Adriano V Werhli ◽  
Dirk Husmeier
Keyword(s):  
Prior Knowledge ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Reconstruction Error ◽  
Expression Data ◽  
Multiple Sources ◽  
Promoter Regions ◽  
Experimental Conditions ◽  
Microarray Expression Data ◽  
Gene Regulatory

There have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited amount of independent experimental conditions and noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. In the present paper, we consider a Bayesian approach to systematically integrate expression data with multiple sources of prior knowledge. Each source is encoded via a separate energy function, from which a prior distribution over network structures in the form of a Gibbs distribution is constructed. The hyperparameters associated with the different sources of prior knowledge, which measure the influence of the respective prior relative to the data, are sampled from the posterior distribution with MCMC. We have evaluated the proposed scheme on the yeast cell cycle and the Raf signalling pathway. Our findings quantify to what extent the inclusion of independent prior knowledge improves the network reconstruction accuracy, and the values of the hyperparameters inferred with the proposed scheme were found to be close to optimal with respect to minimizing the reconstruction error.

scholarly journals EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

2021 ◽  
Author(s):  
Hakimeh Khojasteh ◽  
Mohammad Hossein Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Data ◽  
Class Imbalance Problem ◽  
Experimental Conditions ◽  
Topological Features ◽  
Gene Regulatory

The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a challenging task. In this paper, we propose EnGRNT approach to infer GRNs with high accuracy that uses ensemble-based methods. The proposed approach, as well as the gene expression data, considers the topological features of GRN. We applied our approach to the simulated Escherichia coli dataset. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. The obtained results recommend the application of EnGRNT on the imbalanced datasets.

Discrete Networks as a Suitable Approach for the Analysis of Genetic Regulation

2009 ◽  
pp. 530-540
Author(s):  
Elizabeth Santiago-Cortés
Keyword(s):  
Transcription Factors ◽  
Dynamical Systems ◽  
Regulatory Networks ◽  
Dynamical Behavior ◽  
Genetic Regulation ◽  
Genetic Regulatory Networks ◽  
Theoretical Studies ◽  
Biological Interpretation ◽  
Complex Dynamical Behavior ◽  
Discrete Networks

Biological systems are composed of multiple interacting elements; in particular, genetic regulatory networks are formed by genes and their interactions mediated by transcription factors. The establishment of such networks is critical to guarantee the reliability of transcriptional performance in any organism. The study of genetic regulatory networks as dynamical systems is a helpful methodology to understand the transcriptional behavior of the genome. From a number of theoretical studies, it is known that networks present a complex dynamical behavior that includes stability, redundancy, homeostasis, and multistationarity. In this chapter we present some particular biological processes modeled as discrete networks to show that the theoretical properties of networks have a clear biological interpretation.

scholarly journals Transcriptome Analysis of Insulin Signaling-Associated Transcription Factors in C. elegans Reveal Their Genome-Wide Target Genes Specificity and Complexity

2021 ◽  
Vol 22 (22) ◽  
pp. 12462
Author(s):  
Neha Kaushik ◽  
Soumya Rastogi ◽  
Sonia Verma ◽  
Deepak Pandey ◽  
Ashutosh Halder ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Insulin Signaling ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Large Set ◽  
Sequencing Analysis ◽  
Experimental Conditions ◽  
Functional Annotations ◽  
C Elegans

Insulin/IGF-1-like signaling (IIS) plays a crucial, conserved role in development, growth, reproduction, stress tolerance, and longevity. In Caenorhabditis elegans, the enhanced longevity under reduced insulin signaling (rIIS) is primarily regulated by the transcription factors (TFs) DAF-16/FOXO, SKN-1/Nrf-1, and HSF1/HSF-1. The specific and coordinated regulation of gene expression by these TFs under rIIS has not been comprehensively elucidated. Here, using RNA-sequencing analysis, we report a systematic study of the complexity of TF-dependent target gene interactions during rIIS under analogous genetic and experimental conditions. We found that DAF-16 regulates only a fraction of the C. elegans transcriptome but controls a large set of genes under rIIS; SKN-1 and HSF-1 show the opposite trend. Both of the latter TFs function as activators and repressors to a similar extent, while DAF-16 is predominantly an activator. For expression of the genes commonly regulated by TFs under rIIS conditions, DAF-16 is the principal determining factor, dominating over the other two TFs, irrespective of whether they activate or repress these genes. The functional annotations and regulatory networks presented in this study provide novel insights into the complexity of the gene regulatory networks downstream of the IIS pathway that controls diverse phenotypes, including longevity.

Inferring the in vivo cellular program of developing bovine skeletal muscle from expression data

2013 ◽  
Vol 13 (3-4) ◽  
pp. 109-125 ◽  
Author(s):  
Nicholas J. Hudson ◽  
Russell E. Lyons ◽  
Antonio Reverter ◽  
Paul L. Greenwood ◽  
Brian P. Dalrymple
Keyword(s):  
Skeletal Muscle ◽  
Expression Data ◽  
Bovine Skeletal Muscle

INFERRING THE REGULATORY INTERACTION MODELS OF TRANSCRIPTION FACTORS IN TRANSCRIPTIONAL REGULATORY NETWORKS

2012 ◽  
Vol 10 (05) ◽  
pp. 1250012 ◽  
Author(s):  
SHERINE AWAD ◽  
NICHOLAS PANCHY ◽  
SEE-KIONG NG ◽  
JIN CHEN
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Data ◽  
Regulatory Interaction ◽  
Transcriptional Regulatory Networks ◽  
Interaction Models ◽  
Transcriptional Regulatory

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TF–target interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TF–target interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TF–target interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TF–target regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.

scholarly journals Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

2012 ◽  
Vol 28 (12) ◽  
pp. i233-i241 ◽  
Author(s):  
Christopher A. Penfold ◽  
Vicky Buchanan-Wollaston ◽  
Katherine J. Denby ◽  
David L. Wild
Keyword(s):  
Time Series ◽  
Transcription Factors ◽  
Reverse Engineering ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Supplementary Information ◽  
Series Data ◽  
Experimental Conditions ◽  
Biological Studies ◽  
Gene Regulatory

Abstract Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. Availability: The methods outlined in this article have been implemented in Matlab and are available on request. Contact: [email protected] Supplementary Information: Supplementary data is available for this article.

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