Combining multiple types of biological data in constraint-based learning of gene regulatory networks

Abstract Motivation The inference of gene regulatory networks (GRNs) from DNA microarray measurements forms a core element of systems biology-based phenotyping. In the recent past, numerous computational methodologies have been formalized to enable the deduction of reliable and testable predictions in today’s biology. However, little focus has been aimed at quantifying how well existing state-of-the-art GRNs correspond to measured gene-expression profiles. Results Here, we present a computational framework that combines the formulation of probabilistic graphical modeling, standard statistical estimation, and integration of high-throughput biological data to explore the global behavior of biological systems and the global consistency between experimentally verified GRNs and corresponding large microarray compendium data. The model is represented as a probabilistic bipartite graph, which can handle highly complex network systems and accommodates partial measurements of diverse biological entities, e.g. messengerRNAs, proteins, metabolites and various stimulators participating in regulatory networks. This method was tested on microarray expression data from the M3D database, corresponding to sub-networks on one of the best researched model organisms, Escherichia coli. Results show a surprisingly high correlation between the observed states and the inferred system’s behavior under various experimental conditions. Availability and implementation Processed data and software implementation using Matlab are freely available at https://github.com/kotiang54/PgmGRNs. Full dataset available from the M3D database.

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Inference of gene regulatory networks using pseudo-time series data

Bioinformatics ◽

10.1093/bioinformatics/btab099 ◽

2021 ◽

Author(s):

Yuelei Zhang ◽

Xiao Chang ◽

Xiaoping Liu

Keyword(s):

Time Series ◽

Gene Regulatory Networks ◽

Biological Networks ◽

Gene Networks ◽

Regulatory Networks ◽

Time Series Data ◽

Biological Data ◽

Supplementary Information ◽

Series Data ◽

Gene Regulatory

Abstract Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availabilityand implementation The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online.

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A Canonical Correlation Analysis-Based Dynamic Bayesian Network Prior to Infer Gene Regulatory Networks from Multiple Types of Biological Data

Journal of Computational Biology ◽

10.1089/cmb.2014.0296 ◽

2015 ◽

Vol 22 (4) ◽

pp. 289-299 ◽

Cited By ~ 13

Author(s):

Brittany Baur ◽

Serdar Bozdag

Keyword(s):

Correlation Analysis ◽

Bayesian Network ◽

Canonical Correlation Analysis ◽

Gene Regulatory Networks ◽

Canonical Correlation ◽

Regulatory Networks ◽

Dynamic Bayesian Network ◽

Biological Data ◽

Gene Regulatory

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A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

BioMed Research International ◽

10.1155/2013/703849 ◽

2013 ◽

Vol 2013 ◽

pp. 1-15 ◽

Cited By ~ 17

Author(s):

Xin Lai ◽

Animesh Bhattacharya ◽

Ulf Schmitz ◽

Manfred Kunz ◽

Julio Vera ◽

...

Keyword(s):

Systems Biology ◽

Gene Regulatory Networks ◽

Mirna Expression ◽

Regulatory Networks ◽

Expression Profiles ◽

Mechanistic Model ◽

Biological Data ◽

Combining Data ◽

Gene Regulatory ◽

Using Data

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

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