Inferring Gene Regulatory Networks from Gene Expression Data by a Dynamic Bayesian Network-Based Model

We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.

Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of Mus musculus dendritic cells incubated with Candida albicans and proofed our method by predicting previously verified PHIs as robust interactions.

Gene Regulatory Networks (GRNs) inference from gene expression data is a hard task and a widely addressed challenge. GRNs can be represented as Boolean models similarly to digital circuits. Cartesian Genetic Programming (CGP), often used for designing circuits, can thus be adopted in the inference of GRNs. The main CGP operator for generating candidate designs is mutation, making its choice important for obtaining good results. Although there are many mutation operators for CGP, to the best of our knowledge, there is no analysis of them in the GRN inference problem. An evaluation of the Single ActiveMutation (SAM) and the Semantically-Oriented Mutation Operator (SOMO)is performed here for GRNs inference. Also, a combination of both operators is proposed. We use a benchmark single-cell RNA-Sequencingtime series data and its evaluation pipeline to measure the performance of the approaches. The experiments indicate that (i) combining SOMOand SAM provides the best results, and (ii) the results obtained by the proposal are competitive with those from state-of-the-art methods.

In this study, in order to deal with the noise and uncertainty in gene expression data, learning networks, especially Bayesian networks, that have the ability to use prior knowledge, were used to infer gene regulatory network. Learning networks are methods that have the structure of the network and a learning process to obtain relationships. One of the methods which have been used for measuring the relationship between genes is the correlation metrics, but the high correlated genes not necessarily mean that they have causal effect on each other. Studies on common methods in inference of gene regulatory networks are yet to pay attention to their biological importance and as such, predictions by these methods are less accurate in terms of biological significance. Hence, in the proposed method, genes with high correlation were identified in one cluster using clustering, and the existence of edge between the genes in the cluster was prevented. Finally, after the Bayesian network modeling, based on knowledge gained from clustering, the refining phase and improving regulatory interactions using biological correlation were done. In order to show the efficiency, the proposed method has been compared with several common methods in this area including GENIE3 and BMALR. The results of the evaluation indicate that the proposed method recognized regulatory relations in Bayesian modeling process well, due to using of biological knowledge which is hidden in the data collection, and is able to recognize gene regulatory networks align with important methods in this field.

The inference of Gene Regulatory Networks (GRNs) from gene expression data is a hard and widely addressed scientific challenge with potential industrial and health-care use. Discrete and continuous models of GRNs are often used (i) to understand the process, and (ii) to predict the values of the relevant variables. Here, we propose a procedure to infer models of GRNs from data where (i) the data is binarized, (ii) a Boolean model is created using a Cartesian Genetic Programming technique, (iii) the obtained Boolean model is converted to a system of ordinary differential equations, and (iv) an Evolution Strategy defines the parameters of the continuous model. As a result, we expect to reduce the effect of noise and to improve biological interpretability. The proposed method is applied to two ODE systems that describe the circadian rhythm network dynamic, with 5 and 10 state variables. The models created by the proposed procedure are able to reproduce the behavior observed in the original data.

Microarray experiments generate data sets containing valuable information on the gene expression levels of millions of genes in the form of a set of biological samples. Inference of gene regulatory networks from microarray data has become an important research area in bioinformatics. Several computational methods have been proposed to infer important relationships between transcription factors with target genes from gene expression and transcription factor data sets. The inferences from these methods are consistent with the biological literature and can help researchers design reasonable and efficient drugs to resist different diseases. This paper is a survey of different methods used for preprocessing and inferring the gene regulatory networks from a data set of gene expression.

Inference of gene regulatory network from microarray data is one of the most important issues in bioinformatics. Several algorithms have been introduced for this problem, but they cannot give accurate results in case of time series gene expression data. Here, we propose an algorithm that predicts the gene regulatory network more accurately than the previous methods. A new method finds the relationship between a pair of genes by time-shifting the time series data of one gene against another when we compare the patterns of the gene pair. In addition, we increase the accuracy of the prediction method by filtering out the interactions that cannot exist in real biological network. We tested the algorithm to several simulated data which are based on realistic enzyme kinetics system, and evaluated the effectiveness of our algorithm. Results show that the present algorithm significantly improves the accuracy of the inference of gene regulatory network.

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

To address one of the most challenging ecosystems issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence on the life and the development of living organisms, and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. The ecosystems impacted by GRN inference span various levels from cell to society -- globally.

A survey of the methodological process of modeling, inference, and evaluation of gene regulatory networks using scRNA-Seq data.

A comparative study of covariance selection models for the inference of gene regulatory networks

Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As measurement techniques evolve, there has been an increasing need for data analysis methodologies, especially those focused on cell-type identification and inference of gene regulatory networks (GRNs). We have developed a new method named BootCellNet, which employs smoothing and resampling to infer GRNs. Using the inferred GRNs, BootCellNet further infers the minimum dominating set (MDS), a set of genes that determines the dynamics of the entire network. We have demonstrated that BootCellNet robustly infers GRNs and their MDSs from scRNA-seq data and facilitates unsupervised identification of cell clusters using scRNA-seq datasets of peripheral blood mononuclear cells and hematopoiesis. It has also identified COVID-19 patient-specific cells and their potential regulatory transcription factors. BootCellNet not only identifies cell types in an unsupervised and explainable way but also provides insights into the characteristics of identified cell types through the inference of GRNs and MDS.

An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

The inference of gene regulatory networks from expression data is an important area of research that provides insight to the inner workings of a biological system. In this paper we present a new reverse-engineering framework for gene regulatory network reconstruction. It exploits some interesting characteristics of genetic programming, like the ability of solving complex regression problems with little or no information about the underlying data and the ability of performing an automatic selection of features at learning time. Our framework not only reconstructs the topology of the network, but it is also able to simulate its dynamic behaviour, forecasting the gene expression values in the timepoints subsequent to the initial one. In this paper, we test the proposed framework on the well-known IRMA gene regulatory network, and we show that its performances are promising.

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.