Large-scale Gene Regulatory Networks Research Articles

Construction of single-cell cross-species chromatin accessibility landscapes with combinatorial-hybridization-based ATAC-seq

Despite recent advances in single-cell genomics, the lack of maps for single-cell candidate cis-regulatory elements (cCREs) in non-mammal species has limited our exploration of conserved regulatory programs across vertebrates and invertebrates. Here, we developed a combinatorial-hybridization-based method for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) named CH-ATAC-seq, enabling the construction of single-cell accessible chromatin landscapes for zebrafish, Drosophila, and earthworms (Eisenia andrei). By integrating scATAC censuses of humans, monkeys, and mice, we systematically identified 152 distinct main cell types and around 0.8 million cell-type-specific cCREs. Our analysis provided insights into the conservation of neural, muscle, and immune lineages across species, while epithelial cells exhibited a higher organ-origin heterogeneity. Additionally, a large-scale gene regulatory network (GRN) was constructed in four vertebrates by integrating scRNA-seq censuses. Overall, our study provides a valuable resource for comparative epigenomics, identifying the evolutionary conservation and divergence of gene regulation across different species.

ILSGRN: inference of large-scale gene regulatory networks based on multi-model fusion.

Gene regulatory networks (GRNs) are a way of describing the interaction between genes, which contribute to revealing the different biological mechanisms in the cell. Reconstructing GRNs based on gene expression data has been a central computational problem in systems biology. However, due to the high dimensionality and non-linearity of large-scale GRNs, accurately and efficiently inferring GRNs is still a challenging task. In this article, we propose a new approach, iLSGRN, to reconstruct large-scale GRNs from steady-state and time-series gene expression data based on non-linear ordinary differential equations. Firstly, the regulatory gene recognition algorithm calculates the Maximal Information Coefficient between genes and excludes redundant regulatory relationships to achieve dimensionality reduction. Then, the feature fusion algorithm constructs a model leveraging the feature importance derived from XGBoost (eXtreme Gradient Boosting) and RF (Random Forest) models, which can effectively train the non-linear ordinary differential equations model of GRNs and improve the accuracy and stability of the inference algorithm. The extensive experiments on different scale datasets show that our method makes sensible improvement compared with the state-of-the-art methods. Furthermore, we perform cross-validation experiments on the real gene datasets to validate the robustness and effectiveness of the proposed method. The proposed method is written in the Python language, and is available at: https://github.com/lab319/iLSGRN.

Inferring Gene Regulatory Networks via Ensemble Path Consistency Algorithm Based on Conditional Mutual Information.

Utilizing gene expression data to infer gene regulatory networks has received great attention because gene regulation networks can reveal complex life phenomena by studying the interaction mechanism among nodes. However, the reconstruction of large-scale gene regulatory networks is often not ideal due to the curse of dimensionality and the impact of external noise. In order to solve this problem, we introduce a novel algorithms called ensemble path consistency algorithm based on conditional mutual information (EPCACMI), whose threshold of mutual information is dynamically self-adjusted. We first use principal component analysis to decompose a large-scale network into several subnetworks. Then, according to the absolute value of coefficient of each principal component, we could remove a large number of unrelated nodes in every subnetwork and infer the relationships among these selected nodes. Finally, all inferred subnetworks are integrated to form the structure of the complete network. Rather than inferring the whole network directly, the influence of a mass of redundant noise could be weakened. Compared with other related algorithms like MRNET, ARACNE, PCAPMI and PCACMI, the results show that EPCACMI is more effective and more robust when inferring gene regulatory networks with more nodes.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer’s disease network

The inference of large-scale gene regulatory networks is essential for understanding comprehensive interactions among genes. Most existing methods are limited to reconstructing networks with a few hundred nodes. Therefore, parallel computing paradigms must be leveraged to construct large networks. We propose a generic parallel framework that enables any existing method, without re-engineering, to infer large networks in parallel, guaranteeing quality output. The framework is tested on 15 inference methods (not limited to) employing in silico benchmarks and real-world large expression matrices, followed by qualitative and speedup assessment. The framework does not compromise the quality of the base serial inference method. We rank the candidate methods and use the top-performing method to infer an Alzheimer's Disease (AD) affected network from large expression profiles of a triple transgenic mouse model consisting of 45,101 genes. The resultant network is further explored to obtain hub genes that emerge functionally related to the disease. We partition the network into 41 modules and conduct pathway enrichment analysis, revealing that a good number of participating genes are collectively responsible for several brain disorders, including AD. Finally, we extract the interactions of a few known AD genes and observe that they are periphery genes connected to the network's hub genes. Availability: The R implementation of the framework is downloadable from https://github.com/Netralab/GenericParallelFramework.

Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons

BackgroundBuilding biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regulatory relationships are complex and often nonlinear or non-monotonic, which makes inference using linear models challenging.ResultsIn this paper, we propose a multi-layer perceptron-based differential equation method, which operates by training a fully connected neural network (NN) to simulate the transcription rate of genes in traditional differential equations. We verify whether the regulatory network constructed by the NN method can continue to achieve the expected biological function by verifying the degree of overlap between the regulatory network discovered by NN and the regulatory network constructed by the Hill function. And we validate our approach by adapting to noise signals, regulator knockout, and constructing large-scale gene regulatory networks using link-knockout techniques. We apply a real dataset (the mesoderm inducer Xenopus Brachyury expression) to construct the core topology of the gene regulatory network and find that Xbra is only strongly expressed at moderate levels of activin signaling.ConclusionWe have demonstrated from the results that this method has the ability to identify the underlying network topology and functional mechanisms, and can also be applied to larger and more complex gene network topologies.

EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks.

Reconstruction of genome-scale networks from gene expression data is an actively studied problem. A wide range of methods that differ between the types of interactions they uncover with varying trade-offs between sensitivity and specificity have been proposed. To leverage benefits of multiple such methods, ensemble network methods that combine predictions from resulting networks have been developed, promising results better than or as good as the individual networks. Perhaps owing to the difficulty in obtaining accurate training examples, these ensemble methods hitherto are unsupervised. In this article, we introduce EnGRaiN, the first supervised ensemble learning method to construct gene networks. The supervision for training is provided by small training datasets of true edge connections (positives) and edges known to be absent (negatives) among gene pairs. We demonstrate the effectiveness of EnGRaiN using simulated datasets as well as a curated collection of Arabidopsis thaliana datasets we created from microarray datasets available from public repositories. EnGRaiN shows better results not only in terms of receiver operating characteristic and PR characteristics for both real and simulated datasets compared with unsupervised methods for ensemble network construction, but also generates networks that can be mined for elucidating complex biological interactions. EnGRaiN software and the datasets used in the study are publicly available at the github repository: https://github.com/AluruLab/EnGRaiN. Supplementary data are available at Bioinformatics online.

MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks.

High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost involved when learning from very large datasets in a sequential execution environment. To overcome this issue, we present a multiple kernel learning (MKL) based gene regulatory network (GRN) inference approach wherein multiple heterogeneous datasets are fused using MKL paradigm. We formulate the GRN learning problem as a supervised classification problem, whereby genes regulated by a specific transcription factor are separated from other non-regulated genes. A parallel execution architecture is devised to learn a large scale GRN by decomposing the initial classification problem into a number of subproblems that run as multiple processes on a multi-processor machine. We evaluate the approach in terms of increased speedup and inference potential using genomic data from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The results thus obtained demonstrate that the proposed method exhibits better classification accuracy and enhanced speedup compared to other state-of-the-art methods while learning large scale GRNs from multiple and heterogeneous datasets.

Multilayer Perceptron based Model of Large-Scale Gene Regulatory Network

Multilayer Perceptron based Model of Large-Scale Gene Regulatory Network

Reconstructing gene regulatory networks via memetic algorithm and LASSO based on recurrent neural networks

Reconstructing gene regulatory networks (GRNs) from gene expression data is an important and challenging problem in system biology. In general, the problem of reconstructing GRNs can be modeled as an optimization problem. Recurrent neural network (RNN) has been widely used for GRNs. However, in a real GRN, the number of genes is very large and the relationships between genes are usually very sparse. In this paper, we design a memetic algorithm to learn partial parameters of RNN, and develop a framework based on the least absolute shrinkage and selection operator (LASSO) to reconstruct GRNs based on RNN, which is termed as MALASSORNN-GRN. In the LASSO, the task of reconstructing GRNs is decomposed into a sparse signal reconstructing problem. In the experiments, MALASSORNN-GRN is applied on synthetic data and well-known benchmark datasets DREAM3 and DREAM4. The effect of parameters on MALASSORNN-GRN is discussed, and MALASSORNN-GRN is compared with three other algorithms which are all state-of-the-art RNN learning algorithms. The results show that MALASSORNN-GRN performs best and is capable of reconstructing large-scale GRNs.

A Stable, Unified Density Controlled Memetic Algorithm for Gene Regulatory Network Reconstruction Based on Sparse Fuzzy Cognitive Maps

Gene regulatory networks (GRNs) denote the interrelation among genes in the genomic level. GRNs have a sparse network structures, and as a simulation of GRNs, the density of The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge is less than 5%. So using sparse models to represent GRNs is a meaningful task. Fuzzy cognitive maps (FCMs) have been used to reconstruct GRNs. However, the networks learned by automated derivate-free methods is much denser than those in practical applications. Moreover, the performance of current sparse FCM learning algorithms is worse than what we expect. Therefore, proposing a fast, simple and sparse FCM learning algorithm is a realistic demand. Here, we propose a new unified algorithm: Density Controlled Memetic Algorithm (DC-MA) for learning sparse FCMs. As a simple and good-performance algorithm, memetic algorithm (MA) is chosen as the framework of DC-MA. In DC-MA, a new crossover operator and a mutation operator are designed to optimize the target, control the density and ensure the stability; the local search is used to improve the accuracy and a special self-learning operator is proposed to adjust density. To test the effectiveness of our algorithm, DC-MA is performed on synthetic data with varying sizes and densities. The results show that DC-MA obtains good performance in learning sparse FCMs from time series. On the benchmark datasets DREAM3, DREAM4 and large-scale GRN reconstruction DREAM5 dataset, DC-MA shows high accuracy. The good performance in learning sparse FCMs shows the effectiveness of DC-MA, and the simplicity and scalability of the framework ensure that DC-MA can be adapted to a wide range of needs.

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot topic of system biology in recent years. In order to reduce the computation load for large-scale GRN identification, a parallel algorithm based on restricted gene expression programming (RGEP), namely MPRGEP, is proposed to infer instantaneous and time-delayed regulatory relationships between transcription factors and target genes. In MPRGEP, the structure and parameters of time-delayed S-system (TDSS) model are encoded into one chromosome. An original hybrid optimization approach based on genetic algorithm (GA) and gene expression programming (GEP) is proposed to optimize TDSS model with MapReduce framework. Time-delayed GRNs (TDGRN) with hundreds of genes are utilized to test the performance of MPRGEP. The experiment results reveal that MPRGEP could infer more accurately gene regulatory network than other state-of-art methods, and obtain the convincing speedup.

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review.

: System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

Inferring gene regulatory networks with hybrid of multi-agent genetic algorithm and random forests based on fuzzy cognitive maps

Inferring gene regulatory networks (GRNs) from expression data is an important and challenging problem in the field of computational biology. With the growth of high-throughput gene expression data, GRN inference has attracted much interest from researchers. In this paper, we focus on inferring large-scale GRNs using a fast and accurate algorithm. We first use fuzzy cognitive maps (FCMs) to model GRNs. Then, multi-agent genetic algorithm (MAGA) is used to determine regulatory links, and random forests (RF) are used as the feature selection algorithm to initialize the agents, which can reduce the search space of MAGA according to the gene ranking. We improve the genetic operators of MAGA to cope with GRN inference. The proposed algorithm is termed as MAGARFFCM-GRN. In the experiments, the performance of MAGARFFCM-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that MAGARFFCM-GRN can infer directed GRNs with high accuracy and efficiency.

Inferring Large-Scale Gene Regulatory Networks Using a Randomized Algorithm Based on Singular Value Decomposition.

Reconstructing large-scale gene regulatory networks (GRNs) is a challenging problem in the field of computational biology. Various methods for inferring GRNs have been developed, but they fail to accurately infer GRNs with a large number of genes. Additionally, the existing evaluation indexes for evaluating the constructed networks have obvious disadvantages because GRNs in most biological systems are sparse. In this paper, we develop a new method for inferring GRNs based on randomized singular value decomposition (RSVD) and ordinary differential equation (ODE)-based optimization, denoted as IGRSVD, from large-scale time series data with noise. The three major contributions of this paper are as follows. First, the IGRSVD algorithm uses the RSVD to handle the noise and reduce the original large-scale data into small-scale problems. Second, we propose two new evaluated indexes, the expected value accuracy (EVA) and the expected value error (EVE), to evaluate the performance of inferred networks by considering the sparse features in the network. Finally, the proposed IGRSVD algorithm is compared with the existing SVD algorithm and PCA_CMI algorithm using four subsets from E. coli and datasets from DREAM challenge. The experimental results demonstrate that the IGRSVD algorithm is effective and more suitable for reconstructing large-scale networks.

P_UNSAT approach of attractor calculation for Boolean gene regulatory networks

Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which combined the predecessor approach and the logic unsatisfiability approach to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks even the networks with a relatively large average degree.

Assessing regulatory information in developmental gene regulatory networks

Gene regulatory networks (GRNs) provide a transformation function between the static genomic sequence and the primary spatial specification processes operating development. The regulatory information encompassed in developmental GRNs thus goes far beyond the control of individual genes. We here address regulatory information at different levels of network organization, from single node to subcircuit to large-scale GRNs and discuss how regulatory design features such as network architecture, hierarchical organization, and cis-regulatory logic contribute to the developmental function of network circuits. Using specific subcircuits from the sea urchin endomesoderm GRN, for which both circuit design and biological function have been described, we evaluate by Boolean modeling and in silico perturbations the import of given circuit features on developmental function. The examples include subcircuits encoding positive feedback, mutual repression, and coherent feedforward, as well as signaling interaction circuitry. Within the hierarchy of the endomesoderm GRN, these subcircuits are organized in an intertwined and overlapping manner. Thus, we begin to see how regulatory information encoded at individual nodes is integrated at all levels of network organization to control developmental process.

A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps

BackgroundReconstructing gene regulatory networks (GRNs) from expression data plays an important role in understanding the fundamental cellular processes and revealing the underlying relations among genes. Although many algorithms have been proposed to reconstruct GRNs, more rapid and efficient methods which can handle large-scale problems still need to be developed. The process of reconstructing GRNs can be formulated as an optimization problem, which is actually reconstructing GRNs from time series data, and the reconstructed GRNs have good ability to simulate the observed time series. This is a typical big optimization problem, since the number of variables needs to be optimized increases quadratically with the scale of GRNs, resulting an exponential increase in the number of candidate solutions. Thus, there is a legitimate need to devise methods capable of automatically reconstructing large-scale GRNs.ResultsIn this paper, we use fuzzy cognitive maps (FCMs) to model GRNs, in which each node of FCMs represent a single gene. However, most of the current training algorithms for FCMs are only able to train FCMs with dozens of nodes. Here, a new evolutionary algorithm is proposed to train FCMs, which combines a dynamical multi-agent genetic algorithm (dMAGA) with the decomposition-based model, and termed as dMAGA-FCMD, which is able to deal with large-scale FCMs with up to 500 nodes. Both large-scale synthetic FCMs and the benchmark DREAM4 for reconstructing biological GRNs are used in the experiments to validate the performance of dMAGA-FCMD.ConclusionsThe dMAGA-FCMD is compared with the other four algorithms which are all state-of-the-art FCM training algorithms, and the results show that the dMAGA-FCMD performs the best. In addition, the experimental results on FCMs with 500 nodes and DREAM4 project demonstrate that dMAGA-FCMD is capable of effectively and computationally efficiently training large-scale FCMs and GRNs.

An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks

Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.

Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens.

In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator–target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics ‘first-hand’ data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models.