Reverse Engineering Gene Networks Using Global-Local Shrinkage Rules

Gene Networks ◽

Regulatory Networks ◽

Cell Activation ◽

Network Inference ◽

Structure Learning ◽

Multiple Scales ◽

Real Data ◽

AbstractInferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

Reverse engineering gene networks using global–local shrinkage rules

Interface Focus ◽

10.1098/rsfs.2019.0049 ◽

2019 ◽

Vol 10 (1) ◽

pp. 20190049 ◽

Cited By ~ 2

Author(s):

Viral Panchal ◽

Daniel F. Linder

Keyword(s):

T Cell Activation ◽

Gene Networks ◽

Regulatory Networks ◽

Cell Activation ◽

Network Inference ◽

Structure Learning ◽

Multiple Scales ◽

Real Data ◽

Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global–local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

Integration of multiple data sources for gene network inference using genetic perturbation data

10.1101/158394 ◽

2017 ◽

Cited By ~ 3

Author(s):

Xiao Liang ◽

William Chad Young ◽

Ling-Hong Hung ◽

Adrian E. Raftery ◽

Ka Yee Yeung

Keyword(s):

Gene Networks ◽

Regulatory Networks ◽

Network Inference ◽

Data Sources ◽

Expression Data ◽

Gene Network Inference ◽

External Data ◽

Multiple Data ◽

AbstractBackgroundThe inference of gene regulatory networks is of great interest and has various applications. The recent advances in high-throughout biological data collection have facilitated the construction and understanding of gene regulatory networks in many model organisms. However, the inference of gene networks from large-scale human genomic data can be challenging. Generally, it is difficult to identify the correct regulators for each gene in the large search space, given that the high dimensional gene expression data only provides a small number of observations for each gene.ResultsWe present a Bayesian approach integrating external data sources with knockdown data from human cell lines to infer gene regulatory networks. In particular, we assemble multiple data sources including gene expression data, genome-wide binding data, gene ontology, known pathways and use a supervised learning framework to compute prior probabilities of regulatory relationships. We show that our integrated method improves the accuracy of inferred gene networks. We apply our method to two different human cell lines, which illustrates the general scope of our method.ConclusionsWe present a flexible and systematic framework for external data integration that improves the accuracy of human gene network inference while retaining efficiency. Integrating various data sources of biological information also provides a systematic way to build on knowledge from existing literature.

High-dimensional Bayesian network inference from systems genetics data using genetic node ordering

10.1101/501460 ◽

2018 ◽

Author(s):

Lingfei Wang ◽

Pieter Audenaert ◽

Tom Michoel

Keyword(s):

Genetic Variation ◽

Bayesian Network ◽

Gene Networks ◽

Regulatory Networks ◽

Network Inference ◽

High Dimensional ◽

Systems Genetics ◽

Gene Regulatory ◽

Bayesian Network Inference

AbstractStudying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher over-lap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.

Overview of Gene Regulatory Network Inference Based on Differential Equation Models

Current Protein and Peptide Science ◽

10.2174/1389203721666200213103350 ◽

2020 ◽

Vol 21 (11) ◽

pp. 1054-1059

Author(s):

Bin Yang ◽

Yuehui Chen

Keyword(s):

Differential Equation ◽

Regulatory Networks ◽

Network Inference ◽

New Drugs ◽

Gene Regulatory Network Inference ◽

Ode Models ◽

Differential Equation Models ◽

Linear Ode ◽

: Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500451 ◽

2017 ◽

Vol 15 (02) ◽

pp. 1650045 ◽

Cited By ~ 1

Author(s):

Olga V. Petrovskaya ◽

Evgeny D. Petrovskiy ◽

Inna N. Lavrik ◽

Vladimir A. Ivanisenko

Keyword(s):

Mathematical Modeling ◽

Gene Networks ◽

Gene Network ◽

Regulatory Networks ◽

Network Modeling ◽

Computer Experiments ◽

Linear Control ◽

Expression Of Genes ◽

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

Universal attenuators and their interactions with feedback loops in gene regulatory networks

10.1101/074716 ◽

2016 ◽

Author(s):

Dianbo Liu ◽

Luca Albergante ◽

Timothy J Newman

Keyword(s):

Gene Expression ◽

Gene Networks ◽

Regulatory Networks ◽

Human Cancer ◽

Cancer Cell Line ◽

Feedback Loops ◽

Human Cancer Cell ◽

E Coli ◽

AbstractUsing a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analysing the preponderance of LRCs in the GRNs of E. coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as “universal attenuators”. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.In briefWe present a general principle that linear regulatory chains exponentially attenuate the range of expression in gene regulatory networks. The discovery of a universal interplay between linear regulatory chains and genetic feedback loops in microorganisms and a human cancer cell line is analysed and discussed.HighlightsWithin gene networks, linear regulatory chains act as exponentially strong attenuators of upstream variationBecause of their exponential behaviour, linear regulatory chains beyond a few genes provide no additional functionality and are rarely observed in gene networks across a range of different organismsNovel interactions between four-gene linear regulatory chains and feedback loops were discovered in E. coli, M. tuberculosis and human cancer cells, suggesting a universal mechanism of control.

CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer

Cancer Informatics ◽

10.4137/cin.s19965 ◽

2015 ◽

Vol 14s4 ◽

pp. CIN.S19965 ◽

Cited By ~ 2

Author(s):

Simone Rubinacci ◽

Alex Graudenzi ◽

Giulio Caravagna ◽

Giancarlo Mauri ◽

James Osborne ◽

...

Keyword(s):

Gene Networks ◽

Regulatory Networks ◽

Dynamical Behavior ◽

Gene Activation ◽

Spatial Dynamics ◽

Multiscale Simulation ◽

Activation Patterns ◽

Gene Regulatory ◽

Cellular Phenotypes

We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of multicellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled using different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamical behavior of gene networks, in terms of gene activation patterns characterizing the different cellular phenotypes of real cells and, especially, on the overall robustness to perturbations and biological noise. The integration of this approach within Chaste's modular simulation framework provides a powerful tool to model multicellular systems, possibly allowing for the formulation of novel hypotheses on gene regulation, cell differentiation, and, in particular, cancer emergence and development. In order to demonstrate the usefulness of CoGNaC over a range of modeling paradigms, two example applications are presented. The first of these concerns the characterization of the gene activation patterns of human T-helper cells. The second example is a multiscale simulation of a simplified intestinal crypt, in which, given certain conditions, tumor cells can emerge and colonize the tissue.

Using Bayesian Networks to Construct Gene Regulatory Networks from Microarray Data

Jurnal Teknologi ◽

10.11113/jt.v58.1255 ◽

2012 ◽

pp. 1-6

Author(s):

Ai Kung Tan ◽

Mohd Saberi Mohamad

Keyword(s):

Saccharomyces Cerevisiae ◽

Cell Cycle ◽

Bayesian Networks ◽

Bayesian Network ◽

Gene Networks ◽

Regulatory Networks ◽

Cell Cycle Gene ◽

Gene Regulatory ◽

Cell Cycle Gene Expression

In this research, Bayesian network is proposed as the model to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset due to its capability of handling microarray datasets with missing values. The goal of this research is to study and to understand the framework of the Bayesian networks, and then to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset by developing Bayesian networks using hill-climbing algorithm and Efron’s bootstrap approach and then the performance of the constructed gene networks of Saccharomyces cerevisiae are evaluated and are compared with the previously constructed sub-networks by Dejori [14]. At the end of this research, the gene networks constructed for Saccharomyces cerevisiae not only have achieved high True Positive Rate (more than 90%), but the networks constructed also have discovered more potential interactions between genes. Therefore, it can be concluded that the performance of the gene regulatory networks constructed using Bayesian networks in this research is proved to be better because it can reveal more gene relationships. Dalam penyelidikan ini, Bayesian network adalah dicadangkan sebagai model untuk membina gene regulatory networks dari kitar sel S. cerevisiae set data disebabkan keupayaannya untuk mengendali set data microarray yang mempunyai nilai-nilai yang hilang. Tujuan penyelidikan ini adalah untuk mempelajari dan memahami rekabentuk untuk Bayesian network, dan kemudian untuk membina gene regulatory networks dari data Saccharomyces cerevisiae cell-cycle gene expression dan data Escherichia coli dengan membina model Bayesian networks dengan menggunakan algoritma hill-climbing serta Efron’s bootstrap approach dan gene networks yang dibina untuk Saccharomyces cerevisiae dibandingkan dengan sub-networks yang dibina oleh Dejori [14]. Pada akhir kajian ini, gene networks yang dibina untuk Saccharomyces cerevisiae bukan sahaja telah mencapai True Positive Rate yang tinggi (lebih dari 90%), tetapi gene networks yang dibina juga telah menemui lebih banyak interaksi berpotensi antara gen. Oleh kerana itu, dapat disimpulkan bahawa prestasi gene networks yang dibina menggunakan Bayesian network dalam kajian ini adalah terbukti lebih baik kerana ia boleh mendedahkan lebih banyak hubungan antara gen.

Gene regulatory networks controlling vertebrate retinal regeneration

Science ◽

10.1126/science.abb8598 ◽

2020 ◽

Vol 370 (6519) ◽

pp. eabb8598 ◽

Cited By ~ 3

Author(s):

Thanh Hoang ◽

Jie Wang ◽

Patrick Boyd ◽

Fang Wang ◽

Clayton Santiago ◽

...

Keyword(s):

Gene Networks ◽

Regulatory Networks ◽

Chromatin Accessibility ◽

Specific Gene ◽

Muller Glia ◽

Müller Glia ◽

Retinal Regeneration ◽

Adult Mice ◽

Injury induces retinal Müller glia of certain cold-blooded vertebrates, but not those of mammals, to regenerate neurons. To identify gene regulatory networks that reprogram Müller glia into progenitor cells, we profiled changes in gene expression and chromatin accessibility in Müller glia from zebrafish, chick, and mice in response to different stimuli. We identified evolutionarily conserved and species-specific gene networks controlling glial quiescence, reactivity, and neurogenesis. In zebrafish and chick, the transition from quiescence to reactivity is essential for retinal regeneration, whereas in mice, a dedicated network suppresses neurogenic competence and restores quiescence. Disruption of nuclear factor I transcription factors, which maintain and restore quiescence, induces Müller glia to proliferate and generate neurons in adult mice after injury. These findings may aid in designing therapies to restore retinal neurons lost to degenerative diseases.

INFERENCE OF LARGE-SCALE GENE REGULATORY NETWORKS USING REGRESSION-BASED NETWORK APPROACH

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720009004278 ◽

2009 ◽

Vol 07 (04) ◽

pp. 717-735 ◽

Cited By ~ 4

Author(s):

HASEONG KIM ◽

JAE K. LEE ◽

TAESUNG PARK

Keyword(s):

Gene Networks ◽

Regulatory Networks ◽

Large Scale ◽

Simple Procedure ◽

Simulated Data ◽

Computation Time ◽

Network Approach ◽

Global Regulators ◽

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crecentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.