Research on the Gene Regulation Network Construction Algorithm

Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd," is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g. correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA, and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold-standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step toward incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction. Data and code are available from https://github.com/iiiime/SPREd.

Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary data are available at Bioinformatics online.

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.

Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)-guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l1-norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual. Especially, the proposed method provides a clear interpretation of combinatorial regulations of gene expression by optimally extracting regulation coordination for a given target gene. Experimental results on both simulation data and real-world microarray data demonstrate the competent performance of discovering regulatory relationships in GRN reconstruction.

Reconstruction of gene regulatory networks (GRNs), which explicitly represent the causality of developmental or regulatory process, is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but seldom are taken into account simultaneously in one computational method. In this work, we present a novel method for inferring GRNs from gene expression data considering the non-linear dependence and topological structure of GRNs by employing path consistency algorithm (PCA) based on conditional mutual information (CMI). In this algorithm, the conditional dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles. The method is validated on the benchmark GRNs from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The cross-validation results confirmed the effectiveness of our method (PCA-CMI), which outperforms significantly other previous methods. Besides its high accuracy, our method is able to distinguish direct (or causal) interactions from indirect associations. All the source data and code are available at: http://csb.shu.edu.cn/subweb/grn.htm. lnchen@sibs.ac.cn; zpliu@sibs.ac.cn Supplementary data are available at Bioinformatics online.

The increasing amount of data produced by current high-throughput technologies allows for a better understanding of disease-related phenotypic traits and yet the molecular mechanisms stabilizing them are predominantly unknown. The inference of gene regulatory networks (GRNs) has potential to significantly enhance the discovery of disease-related and context-specific interactomes [1]. However, GRN inference remains a major challenge due to the combinatorial explosion of solutions and the underdetermined nature of the problem, which can be overcome by adding more context-specific information, such as gene expression, chromatin conformation or covalent modifications. Even though integrating multiple layers of information has been attempted before, all methods are tailored to specific needs [2,3]. In this work an Integer Linear Programming approach is proposed for inferring context-specific Boolean GRNs. In order to guide the optimization to a more reliable GRN, both context-independent and context-specific information is encoded into constraints. In contrast to previous methods that explore the search space using heuristics, the proposed methodology guarantees global optimality, significantly reduced runtime, and scalability. The optimization-based approach was validated on various human cell lines and outperformed previous methods in all cases when comparing the amount of retained cell-line specific ChIP-Seq interactions. We believe that our modular framework will significantly enhance the discovery of disease motifs and drug target prediction by providing more reliable context-specific GRNs.

The complex interactions between transcription factors (TFs) and their target genes in a spatially and temporally specific manner are crucial to all cellular processes. Reconstruction of gene regulatory networks (GRNs) from gene expression profiles can help to decipher TF-gene regulations in a variety of contexts; however, the inevitable prediction errors of GRNs hinder optimal data mining of RNA-Seq transcriptome profiles. Here we perform an integrative study of Zea mays (maize) seed development in order to identify key genes in a complex developmental process. First, we reverse engineered a GRN from 78 maize seed transcriptome profiles. Then, we studied collective gene interaction patterns and uncovered highly interwoven network communities as the building blocks of the GRN. One community, composed of mostly unknown genes interacting with opaque2, brittle endosperm1 and shrunken2, contributes to seed phenotypes. Another community, composed mostly of genes expressed in the basal endosperm transfer layer, is responsible for nutrient transport. We further integrated our inferred GRN with gene expression patterns in different seed compartments and at various developmental stages and pathways. The integration facilitated a biological interpretation of the GRN. Our yeast one-hybrid assays verified six out of eight TF-promoter bindings in the reconstructed GRN. This study identified topologically important genes in interwoven network communities that may be crucial to maize seed development.

Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to fully understand disease ontology and to reduce the cost of drug development, gene regulatory networks (GRN) have to be constructed. During the last decade, many GRN inference algorithms like ‘Bayesian network’ that are based on genome-wide data have been developed to unravel the complexity of gene regulation. Recently, many of structure learning algorithms were used to learn Bayesian network that have shown promise in gene regulatory network reconstruction. In this paper we apply different structure learning algorithms on actual microarray data to obtain a better understanding of their relative strengths and weaknesses on the system biology community and we evaluate their outputs from different perspectives.

Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R

BackgroundInferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.ResultsHere we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.ConclusionsOur study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.

Regulation of gene expression is a fundamental mechanism allowing the existence of complex living systems. This regulation takes shape in the form of hierarchical gene regulatory networks (GRNs), culminating in the binding of transcription factors (TFs) to regulatory elements (REs). Uncovering the structures of these GRNs has shed light on the mechanistic basis of complex spatiotemporal processes like embryonic development. The reconstruction of such GRN, for any biological process, is therefore the ultimate goal for its understanding. The planarian Schmidtea mediterranea is an important model organism for the study of adult stem cell systems, cell differentiation and regeneration. However, very little is known about the structure of GRNs in this organism due to the historical lack of adequate tools to study regulatory elements. At the beginning of my thesis, I developed a robust Start-seq protocol to study RE activity in S. mediterranea by probing for transcription initiation events. Following this, I characterized the identified putative REs on the basis of their distribution, chromatin context as well as motif content. Putative REs identified using Start-seq possess characteristic epigenomic signatures as they are situated within regions of open chromatin and are enriched for active epigenetic marks such as H3K27Ac, H3K4me3 and H3k4me1. I next showed that both planarian putative enhancers and promoter are enriched for core promoter motifs that are mostly situated at the expected position. Interestingly, the DPE motif was enriched at the transcription initiation site in both types of REs instead of its described position at ± +30 nt. Following this, I showed that most of the identified REs showed sign of bidirectional transcription initiation which is considered as a characteristic feature of REs. After characterizing the REs, I leveraged the existence of two naturally occurring biotypes within S. mediterranea (sexual and asexual) to investigate gene regulatory networks (GRNs) responsible for the development and maintenance of the planarian reproductive system. These biotypes are genetically similar, but one fails to develop sexual organs. Comparing their transcription initiation landscapes allowed me to identify multiple transcription factors potentially part of in these GRNs. Notably, the majority of the selected TF candidates exhibited expression in sexual organs, with half of the candidates being specifically expressed in these tissues. Knockdown of three candidates (tead 1, thap and cebp 4 ) showed dysregulation of many reproductive genes. In situ hybridizations on sexual markers after RNAi of the three candidates confirmed the abrogation of sexual tissues in these conditions. The results indicated that tead 1 produced a severe phenotype, where most sexual organs were absent, except for the ovaries. The thap candidate exhibited a defect in shell gland formation and yolk tissue patterning, while the cebp 4 candidate displayed an impairment in sperm differentiation. Overall, this study was not able to recreate a GRN governing the development and maintenance of the reproductive tissue in S. mediterranea. However, it lays a foundation for further research on GRNs in planaria as it shows that identification of differentially active REs using the established protocol was successful in obtaining important regulators the planarian reproductive system.

Recurrent neural network based hybrid model for reconstructing gene regulatory network

This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.

This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations

Elucidating gene regulatory networks (GRNs) is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer or reconstruct gene regulatory networks from expression data. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we present a new algorithm for reverse engineering (inferring) gene regulatory networks on a computer cluster in a cloud environment. The algorithm, implemented in Apache Spark, employs an information-theoretic approach to infer GRNs from time-series gene expression data. Experimental results show that our Spark program is much faster than an existing tool while achieving the same prediction accuracy.