Gene Regulatory Network Modeling: A Data Driven Approach

In this paper, we first study the stability and convergence property of fuzzy logic networks (FLN). A random approach is adopted to simulate the convergence speed and steady-state properties based on four fuzzy logical functions. The simulation results show that MV logical function causes the system to be on the edge of chaos when the number of nodes increases. Thus this logical function is more useful to infer real complex networks, such as gene regulatory networks. Then, a novel gene regulatory network inference algorithm based on the FLN theory is proposed and tested. The algorithm uses fuzzy logical functions to model gene regulatory relationships, and the degrees of regulations are represented as the length of accumulated distances during a period of time intervals. Based on the assumption that the distribution of connectivity in yeast protein-protein networks follows the Zipf's law, the criteria for algorithm parameter quantifications are deduced. One unique feature of this algorithm is that it makes limited a priori assumptions concerning the modeling; hence the algorithm is categorized as a data-driven algorithm. The algorithm was applied to the S. pombe time-series dataset and 407 cell cycle regulated genes were used. The algorithm inferred 59 functionally verified regulations, 47 regulations involving genes with unknown functions, and 19 dubious regulations. The 125 regulatory pairs involve 108 genes, and the average connectivity of the inferred network, 1.157, confirms the Zipf's law.

In this chapter, a novel gene regulatory network inference algorithm based on the fuzzy logic network theory is proposed and tested. The key motivation for this algorithm is that genes with regulatory relationships may be modeled via fuzzy logic, and the strength of regulations may be represented as the length of accumulated distance during a period of time intervals. One unique feature of this algorithm is that it makes very limited a priori assumptions concerning the modeling. Hence the algorithm is categorized as a data-driven algorithm. With the theoretical guidelines to quantify the upper limits of parameters, the algorithm is implemented to infer gene regulatory networks for Saccharomyces cerevisiae and Saccharomyces pombe. The computation results not only prove the validity of the data-driven algorithm, but also offer a possible explanation concerning the difference of network stabilities between the budding yeast and the fission yeast.

Omics sciences are widely used to analyze diseases at a molecular level. Usually, results of omics experiments are sets of candidate genes potentially involved in different diseases. The interpretation of results and the filtering of candidate genes or proteins selected in an experiment is a challenge in some scenarios. This problem is particularly evident in clinical environments in which researchers are interested in the behavior of few molecules related to some specific disease while results may contains thousands of data and have very relevant dimensions. The filtering requires the use of domain-specific knowledge that is usually encoded into ontologies. Consequently, to filter out false positive genes, different approaches for selecting genes have been introduced. Such approaches are often referred to as Gene prioritization methods. They aim to identify the most related genes to a disease among a larger set of candidates genes, through the use of computational methods. We implemented GoD (Gene ranking based On Diseases), an algorithm that ranks a given set of genes based on ontology annotations. The algorithm orders genes by the semantic similarity computed with respect to a disease among the annotations of each gene and those describing the selected disease.The current version of GoD enables the prioritization of a list of input genes for a selected disease. It uses HPO (Human Phenotype Ontology), GO (Gene Ontology), and DO (Disease Ontology) ontologies for the calculation of the ranking. It takes as input a list of genes or gene products annotated with GO Terms, HPO Terms, DO Terms and a selected disease described regarding annotation of GO, HPO or DO (user may also provide novel annotations). It produces as output the ranking of those genes with respect of the input disease. Package consists of three main functions: hpoGoD (for HPO based prioritization), goGoD (for GO based prioritization), and doGoD (for DO based prioritization). We tested GoD on Gene Regulatory Networks (GRNs). Biological network inference aims to reconstruct network of interactions (or associations) among biological genes starting from experimental observations. We selected three expression datasets: Dataset 1 (GDS3285) , related to breast cancer disease; Dataset 2 (GDS5072), related to prostate cancer disease; and Dataset 3 (GDS5093), related to Dengue virus (DENV) infection. Initially, experimental data are given as input to five GRN inference algorithms, i.e. ARACNE, CLR, MRNET, GENIE3 and GGM, to produce 5 inferred GRN networks. For each inferred GRN, GoD receives as input the list of top genes and produces for each gene a semantic similarity value on a selected disease considering one of the previous ontologies (e.g. Disease Ontology). For each GRN, the genes are ranked and reordered on the basis of the computed semantic similarity and are compared allowing to rank each GRN inference method with respect to the initially selected disease.

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.

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.

BackgroundIdentifying gene regulatory networks is an important task for understanding biological systems. Time-course measurement data became a valuable resource for inferring gene regulatory networks. Various methods have been presented for reconstructing the networks from time-course measurement data. However, existing methods have been validated on only a limited number of benchmark datasets, and rarely verified on real biological systems.ResultsWe first integrated benchmark time-course gene expression datasets from previous studies and reassessed the baseline methods. We observed that GENIE3-time, a tree-based ensemble method, achieved the best performance among the baselines. In this study, we introduce BTNET, a boosted tree based gene regulatory network inference algorithm which improves the state-of-the-art. We quantitatively validated BTNET on the integrated benchmark dataset. The AUROC and AUPR scores of BTNET were higher than those of the baselines. We also qualitatively validated the results of BTNET through an experiment on neuroblastoma cells treated with an antidepressant. The inferred regulatory network from BTNET showed that brachyury, a transcription factor, was regulated by fluoxetine, an antidepressant, which was verified by the expression of its downstream genes.ConclusionsWe present BTENT that infers a GRN from time-course measurement data using boosting algorithms. Our model achieved the highest AUROC and AUPR scores on the integrated benchmark dataset. We further validated BTNET qualitatively through a wet-lab experiment and showed that BTNET can produce biologically meaningful results.

Gene regulatory networks (GRNs) are crucial for understanding gene regulation and cellular processes. Inferring GRNs helps uncover regulatory pathways, shedding light on the regulation and development of cellular processes. With the rise of high-throughput sequencing and advancements in computational technology, computational models have emerged as cost-effective alternatives to traditional experimental studies. Moreover, the surge in ChIP-seq data for TF-DNA binding has catalyzed the development of graph neural network (GNN)-based methods, greatly advancing GRN inference capabilities. However, most existing GNN-based methods suffer from the inability to capture long-distance structural semantic correlations due to transitive interactions. In this paper, we introduce a novel GNN-based model named Hierarchical Graph Transformer with Contrastive Learning for GRN (HGTCGRN) inference. HGTCGRN excels at capturing structural semantics using a hierarchical graph Transformer, which introduces a series of gene family nodes representing gene functions as virtual nodes to interact with nodes in the GRNS. These semantic-aware virtual-node embeddings are aggregated to produce node representations with varying emphasis. Additionally, we leverage gene ontology information to construct gene interaction networks for contrastive learning optimization of GRNs. Experimental results demonstrate that HGTCGRN achieves superior performance in GRN inference.

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

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.

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.

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up a gene regulatory network. The understanding and unraveling of gene regulatory networks have been proven very useful in disease diagnosis and genomic drug design. Due to the complexity of gene regulatory networks, the completely understanding of their dynamics is difficult to achieve only through biological experiments without any computational aids. As a consequence, computational models for gene regulatory networks are indispensable. Recently a wide variety of different computational models have been proposed for inferring gene regulatory networks. This paper surveys some of computational models for inferring large gene regulatory networks, in particular, Boolean network model, differential/ difference equation models, and state-space models. Some advantages and disadvantages of these models are commented on. Some criteria for validating the inferred gene regulatory networks are also discussed from the bioinformatics perspective. Finally, several directions of the future work for modeling gene regulatory networks are proposed.

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

Objective: Breast cancer is a common tumor and has a high mortality rate. Gene regulatory networks(GRNs) can genetically facilitate targeted therapies for this disease. Impact Statement: This study proposes a new method to infer GRNs. This new method combining genetic modules and convolutional neural networks is presented to infer GRNs from the RNA sequencing data of breast cancer. Introduction: GRNs play an essential role in many disease treatments. Previous studies showed that GRNs will accelerate tumor therapy. However, most of the existing network inference methods are based on large-scale gene collections, which ignore the characteristics of different tumors. Methods: In this work, weighted gene coexpression network analysis was deployed to screen key genes and gene modules. The gene regulatory associations in gene modules were then transformed into 2-dimensional histogram types. A convolutional neural network was chosen as the main framework to fit the gene regulatory types and infer the GRN. Results: The method integrates genetic data analysis and deep learning perspectives to screen key genes and predict GRNs among key genes. The key genes screened were validated by multiple methods, and the inferred gene regulatory associations were widely validated in real datasets. Conclusion: The method can be used as an auxiliary tool with the potential to predict key genes and the GRNs of key genes. It has the potential to facilitate the therapeutic process and targeted therapy for breast cancer.

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.

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.