Reconstruction Of Gene Regulatory Networks Research Articles

ScPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data.

Disease development and cell differentiation both involve dynamic changes; therefore, the reconstruction of dynamic gene regulatory networks (DGRNs) is an important but difficult problem in systems biology. With recent technical advances in single-cell RNA sequencing (scRNA-seq), large volumes of scRNA-seq data are being obtained for various processes. However, most current methods of inferring DGRNs from bulk samples may not be suitable for scRNA-seq data. In this work, we present scPADGRN, a novel DGRN inference method using “time-series” scRNA-seq data. scPADGRN combines the preconditioned alternating direction method of multipliers with cell clustering for DGRN reconstruction. It exhibits advantages in accuracy, robustness and fast convergence. Moreover, a quantitative index called Differentiation Genes’ Interaction Enrichment (DGIE) is presented to quantify the interaction enrichment of genes related to differentiation. From the DGIE scores of relevant subnetworks, we infer that the functions of embryonic stem (ES) cells are most active initially and may gradually fade over time. The communication strength of known contributing genes that facilitate cell differentiation increases from ES cells to terminally differentiated cells. We also identify several genes responsible for the changes in the DGIE scores occurring during cell differentiation based on three real single-cell datasets. Our results demonstrate that single-cell analyses based on network inference coupled with quantitative computations can reveal key transcriptional regulators involved in cell differentiation and disease development.

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

BackgroundGene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure.ResultsHere, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the previous approaches, the LogicNet does not require an a priori known network structure to infer the logics. The proposed probabilistic logic is superior to the existing fuzzy logics and is more relevant to the biological contexts than the fuzzy logics. The performance of the LogicNet is superior to that of several Mutual Information-based and regression-based tools for reconstructing GRNs.ConclusionsThe LogicNet reconstructs GRNs and logic functions without requiring prior knowledge of the network structure. Moreover, in another application, the LogicNet can be applied for logic function detection from the known regulatory genes-target interactions. We also conclude that computational modeling of the logical interactions among the regulatory genes significantly improves the GRN reconstruction accuracy.

Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data

Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data from Escherichia coli and Saccharomyces cerevisiae as well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs from E. coli and S. cerevisiae to validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein–protein and protein–metabolite interactions.

Transcriptional network inference and master regulator analysis of the response to ribosome-inactivating proteins in leukemia cells

Gene-regulatory networks reconstruction has become a very popular approach in applied biology to infer and dissect functional interactions of Transcription Factors (TFs) driving a defined phenotypic state, termed as Master Regulators (MRs). In the present work, cutting-edge bioinformatic methods were applied to re-analyze experimental data on leukemia cells (human myelogenous leukemia cell line THP-1 and acute myeloid leukemia MOLM-13 cells) treated for 6 h with two different Ribosome-Inactivating Proteins (RIPs), namely Shiga toxin type 1 (400 ng/mL) produced by Escherichia coli strains and the plant toxin stenodactylin (60 ng/mL), purified from the caudex of Adenia stenodactyla Harms. This analysis allowed us to identify the common early transcriptional response to 28S rRNA damage based on gene-regulatory network inference and Master Regulator Analysis (MRA). Both toxins induce a common response at 6 h which involves inflammatory mediators triggered by AP-1 family transcriptional factors and ATF3 in leukemia cells. We describe for the first time the involvement of MAFF, KLF2 and KLF6 in regulating RIP-induced apoptotic cell death, while receptor-mediated downstream signaling through ANXA1 and TLR4 is suggested for both toxins.

Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference.

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulatory network (GRNs) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology. In this work, we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual cells, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data. The MATLAB code of GRISLI is available at: https://github.com/PCAubin/GRISLI. Supplementary data are available at Bioinformatics online.

Enhancer evolution in chordates: Lessons from functional analyses of cephalochordate cis-regulatory modules.

Chordates comprise three major groups, cephalochordates (amphioxus), tunicates (urochordates), and vertebrates. Since cephalochordates were the early branching group, comparisons between amphioxus and other chordates help us to speculate about ancestral chordates. Here, I summarize accumulating data from functional studies analyzing amphioxus cis-regulatory modules (CRMs) in model systems of other chordate groups, such as mice, chickens, clawed frogs, fish, and ascidians. Conservatism and variability of CRM functions illustrate how gene regulatory networks have evolved in chordates. Amphioxus CRMs, which correspond to CRMs deeply conserved among animal phyla, govern reporter gene expression in conserved expression domains of the putative target gene in host animals. In addition, some CRMs located in similar genomic regions (intron, upstream, or downstream) also possess conserved activity, even though their sequences are divergent. These conservative CRM functions imply ancestral genomic structures and gene regulatory networks in chordates. However, interestingly, if expression patterns of amphioxus genes do not correspond to those of orthologs of experimental models, some amphioxus CRMs recapitulate expression patterns of amphioxus genes, but not those of endogenous genes, suggesting that these amphioxus CRMs are close to the ancestral states of chordate CRMs, while vertebrates/tunicates innovated new CRMs to reconstruct gene regulatory networks subsequent to the divergence of the cephalochordates. Alternatively, amphioxus CRMs may have secondarily lost ancestral CRM activity and evolved independently. These data help to solve fundamental questions of chordate evolution, such as neural crest cells, placodes, a forebrain/midbrain, and genome duplication. Experimental validation is crucial to verify CRM functions and evolution.

Reconstruction of Gene Regulatory Network using Modified Ant based Algorithm

Healthcare is a major area of research since few years. Ample amount of biological data getting accumulated daily due to advancement in technologies. Microarray is such technology which captures expressions of thousands of genes at a time. Interactions occur among genes are represented in terms of special networkeknown as Gene Regulatory Network (GRN). It is constructed from Differentially Expressing Genes(DEFs). GRN is a graphical representation containing genes as nodes and regulatory interactions among them as edges. It helps in tracking pathways where usual gene interaction changes leading to malfunctioning of cells and results in illness. Also, now a day’s people are diagnosed with new diseases like dengue, swine flu, Nipah, Corona virus infection for which exact molecular pathways are yet to be invented through GRN. Therefore, in this paper, a nature inspired algorithm is used for reconstruction of GRN using differentially expressing genes.

Online Fuzzy Cognitive Map Learning

The fuzzy cognitive map (FCM) is an effective tool for modeling and simulating complex dynamic systems. The research on the problem of learning FCM from the available time series is outstanding. Many batch FCM learning methods have been proposed to address this issue and the performance of these methods is satisfactory. However, these batch-learning methods are difficult to cope with large-scale data sets (for example, the memory in computers is not enough to store all instances) and real-time streaming data, leading to the failure of real-time and online analysis of complex systems. In this article, unlike the existing batch learning methods, such as evolutionary and regression-based methods, we first extend the FCM learning to an online setting, and then develop an effective algorithm based on a follow-the-regularized-leader (FTRL)-proximal style learning algorithm to address the online FCM learning problem, termed as OFCM. The performance of OFCM is validated on constructed benchmark data sets, including synthetic data sets, and gene regulatory network reconstruction data sets. The experimental results demonstrate the merits of OFCM, which can effectively solve online FCM learning problems.

Integrated multi-omics framework of the plant response to jasmonic acid.

Understanding the systems-level actions of transcriptional responses to hormones provides insight into how the genome is reprogrammed in response to environmental stimuli. Here, we investigate the signaling pathway of the hormone jasmonic acid (JA), which controls a plethora of critically important processes in plants and is orchestrated by the transcription factor (TF) MYC2 and its closest relatives in Arabidopsis thaliana. We generated an integrated framework of the response to JA that spans from the activity of master and secondary-regulatory TFs, through gene expression outputs and alternative splicing to protein abundance changes, protein phosphorylation and chromatin remodeling. We integrated time series transcriptome analysis with (phospho)proteomic data to reconstruct gene regulatory network models. These enable us to predict previously unknown points of crosstalk from JA to other signaling pathways and to identify new components of the JA regulatory mechanism, which we validated through targeted mutant analysis. These results provide a comprehensive understanding of how a plant hormone remodels cellular functions and plant behavior, the general principles of which provide a framework for analysis of cross-regulation between other hormone and stress signaling pathways.

A Preference-Based Evolutionary Biobjective Approach for Learning Large-Scale Fuzzy Cognitive Maps: An Application to Gene Regulatory Network Reconstruction

Learning large-scale fuzzy cognitive maps (FCMs) with the sparse attribute automatically from time series without prior knowledge remains a challenging problem. Most existing automated learning methods were applied to learn small-scale FCMs, and the learned FCMs are much denser than the maps constructed by human experts. Learning FCMs is the procedure of judging whether there are connecting edges and determining the weights of connecting edges. Thus, we transform the problem of learning FCMs into a biobjective optimization problem with two objects of minimizing the measure error and the number of nonzero entries, respectively. To solve this optimization problem, a preference-based iterative thresholding evolutionary biobjective optimization algorithm for learning FCMs is proposed. The strategy focuses on the knee area of the Pareto front (PF) with preference on the solutions near the true sparsity. Moreover, an initialization operator based on random forest is proposed to increase the speed of convergence toward the PF. The experiments on large-scale synthetic data with varying sizes and densities and the application to the gene regulatory network reconstruction problem have been conducted to demonstrate that our proposal matches or exceeds the existing state-of-the-art FCM learning approaches in most cases in terms of four measures, namely, Data_Error , Out_of_Sample_Error , Model Error, and SS_Mean . The Data_Error obtained by the proposed method can achieve 3.07E-06 even when the number of nodes reaches 200. We also demonstrate the effectiveness of the proposed initialization operator and the preference-based strategy, which can result in a fast convergence speed and higher accuracy.

Overview of Gene Regulatory Network Inference Based on Differential Equation Models.

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 Novel Model Integration Network Inference Algorithm with Clustering and Hub Genes Finding.

Gene regulatory network Inference with high accuracy based on gene expression data sets is one of the most challenging problems in computational biology. To improve the accuracy of gene regulatory network inference and find hub genes, we proposed a novel model integration network inference method with clustering and hub genes finding called MINICHG. The method is divided into three main steps: (1) using single models inference results based on three machine learning algorithms to construct feature matrix; (2) using k-means to cluster gene pairs according to feature matrix; (3) hub genes finding. MINICHG integrates RF(Random Forest), GBDT (Gradient Boosting Decision Tree) and Pearson Correlation results with a novel weighted strategy in a semi-unsupervised way. The designed optimization scheme in MINICHG considering sparse gold standard data characteristics is suitable for most gene regulatory network reconstruction. We evaluated the proposed method on simulated data sets from five Dream4 multifactorial data sets and Dream5 in silico data set and real data set from E.coli. The performance was better than other network inference methods with high accuracy and robustness.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments.

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.

Reconstructing gene regulatory networks in single-cell transcriptomic data analysis.

Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level. Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples. Here, we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.

Computational methods for single-cell omics across modalities.

Single-cell omics approaches provide high-resolution data on cellular phenotypes, developmental dynamics and communication networks in diverse tissues and conditions. Emerging technologies now measure different modalities of individual cells, such as genomes, epigenomes, transcriptomes and proteomes, in addition to spatial profiling. Combined with analytical approaches, these data open new avenues for accurate reconstruction of gene-regulatory and signaling networks driving cellular identity and function. Here we summarize computational methods for analysis and integration of single-cell omics data across different modalities and discuss their applications, challenges and future directions.

Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer.

Inferring dynamic regulatory networks that rewire at different stages is a reasonable way to understand the mechanisms underlying cancer development. In this study, we reconstruct the stage-specific gene regulatory networks (GRNs) for colorectal cancer to understand dynamic changes of gene regulations along different disease stages. We combined multiple sets of clinical transcriptomic data of colorectal cancer patients and employed a supervised approach to select initial gene set for network construction. We then developed a dynamical system-based optimization method to infer dynamic GRNs by incorporating mutual information-based network sparsification and a dynamic cascade technique into an ordinary differential equations model. Dynamic GRNs at four different stages of colorectal cancer were reconstructed and analyzed. Several important genes were revealed based on the rewiring of the reconstructed GRNs. Our study demonstrated that reconstructing dynamic GRNs based on clinical transcriptomic profiling allows us to detect the dynamic trend of gene regulation as well as reveal critical genes for cancer development which may be important candidates of master regulators for further experimental test.

High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering.

Studying 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 overlap 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.

Reverse engineering gene networks using global-local shrinkage rules.

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.

Analysis of transcription by ChIP‐seq in the eye: Progress and challenges

AbstractPurposeChIP‐seq experiments are designed to map in vivo localization of DNA‐binding transcription factors, their associated chromatin remodeling enzymes, RNA polymerase, and histone posttranslational modifications, at the genome scale. The results, coupled with analysis of gene loss‐of‐function studies by RNA‐seq, are used to reconstruct gene regulatory networks (GRNs) that govern cellular development and differentiation. In the eye, the majority of available data include lens and retina. Although some of the chromatin‐based assays can be conducted at single cell level (e.g. ATAC‐seq), mapping of transcription factors and modified histones require large numbers of cells.MethodsStandard ChIP‐seq experiments require millions of cells. In contrast, recent modifications allow the use of <100 000 cells. Complementary ATAC‐seq experiments employ 1–50 000 individual cells. RNA‐seq experiments are included in the analysis to distinguish directly and indirectly regulated specific targets of these transcription factors.ResultsIn lens, standard ChIP‐seq data exist for Pax6, H3K4me1, H3K4me3, H3K27ac, H3K27me3, and RNA polymerase II. Using ATAC‐seq, we found that Pax6 was bound both in “open” and “closed” lens chromatin domains in agreement with its dual roles as transcriptional activator and repressor.ConclusionsReconstructions of GRNs that govern lens and retinal morphogenesis are in progress and require joint efforts of multiple research teams. Ongoing studies to use smaller numbers of cells will be reported. Together, these data open the field for mechanistic studies of lens‐ and retinal‐specific promoters and enhancers.