Inferring Time-Delayed Causal Gene Network Using Time-Series Expression Data

Gene Regulatory Identification Based on the Novel Hybrid Time-Delayed Method

Gene regulatory network (GRN) inference with biology data is a difficult and serious issue in the field of system biology. In order to detect the direct associations of GRN more accurately, a novel two-step GRN inference technique based on the time-delayed correlation coefficient (TDCC) and time-delayed complex-valued S-system model (TDCVSS) is proposed. First, a TDCC algorithm is utilized to construct an initial network. Second, a TDCVSS model is utilized to prune the network topology in order to delete false-positive regulatory relationships for each target gene. The complex-valued restricted additive tree and complex-valued differential evolution are proposed to approximate the optimal TDCVSS model. Finally, the overall network could be inferred by integrating the regulations of all target genes. Two real gene expression datasets from E. coli and S. cerevisiae gene networks are utilized to evaluate the performances of our proposed two-step GRN inference algorithm. The results demonstrated that the proposed algorithm could infer GRN more correct than classical methods and time-delayed methods.

NIMCE: A Gene Regulatory Network Inference Approach Based on Multi Time Delays Causal Entropy

Gene regulatory networks (GRNs)are involved in various biological processes, such as cell cycle, differentiation and apoptosis. The existing large amount of expression data, especially the time-series expression data, provide a chance to infer GRNs by computational methods. These data can reveal the dynamics of gene expression and imply the regulatory relationships among genes. However, identify the indirect regulatory links is still a big challenge as most studies treat time points as independent observations, while ignoring the influences of time delays. In this study, we propose a GRN inference method based on information-theory measure, called NIMCE. NIMCE incorporates the transfer entropy to measure the regulatory links between each pair of genes, then applies the causation entropy to filter indirect relationships. In addition, NIMCE applies multi time delays to identify indirect regulatory relationships from candidate genes. Experiments on simulated and colorectal cancer data show NIMCE outperforms than other competing methods. All data and codes used in this study are publicly available at https://github.com/CSUBioGroup/NIMCE.

Enhancing gene regulatory networks inference through hub-based data integration

One of the main research topics in computational biology is Gene Regulatory Network (GRN) reconstruction that refers to inferring the relationships between genes involved in regulating cell conditions in response to internal or external stimuli. To this end, most computational methods use only transcriptional gene expression data to reconstruct gene regulatory networks, but recent studies suggest that gene expression data must be integrated with other types of data to obtain more accurate models predicting real relationships between genes. In this study, a diffusion-based method is enhanced to integrate biological data of network types besides structural prior knowledge. The Random Walk with Restart algorithm (RWR) with an emphasis on hub nodes is executed separately on each network, and then jointly optimizes low-dimensional feature vectors for network nodes by diffusion component analysis. Next, these feature vectors are used to infer gene regulatory networks. Fourteen centrality measures are studied for the detection of hub nodes to be used in the RWR algorithm, and the best centrality measure having the greatest effect on the improvement of gene network inference is selected. A case study for the Saccharomyces cerevisiae and E. coli networks shows that using the proposed features in comparison with gene expression data alone results in 0.02-0.08 units improvement in Area Under Receiver Characteristic Operator (AUROC) criteria across different gene regulatory network inference methods. Furthermore, the proposed method was applied to the esophageal cancer data to infer its gene regulatory network. The proposed framework substantially improves accuracy and scalability of GRN inference. The fused features and the best centrality measure detected can be used to provide functional insights about genes or proteins in various biological applications. Moreover, it can be served as a general framework for network data and structural data integration and analysis problems in various scientific disciplines including biology.

A Composite Mode Differential Gene Regulatory Architecture based on Temporal Expression Profiles

Exploring the complex interactive mechanism in a Gene Regulatory Network (GRN) developed using transcriptome data obtained from standard microarray and/or RNA-seq experiments helps us to understand the triggering factors in cancer research. The Transcription Factor (TF) genes generate protein complexes which affect the transcription of various target genes. However, considering the mode of regulation in a time frame such transcriptional activities are dependent on some specific activation time points only. It is also crucial to check whether the regulating capabilities are uniform across varied stages, especially when periodicity is a big issue. In this context, we propose an algorithm called RIFT which helps to monitor the temporal differential regulatory pattern of a Differentially Expressed (DE) target gene either by a TF gene or a group of TF genes from a large time series (TS) data. We have tested our algorithm on HeLa cell cycle data and compared the result with its most advanced state of the art counterpart proposed so far. As our algorithm yields up stringent mode and target specific significant valid TF genes for a DE gene, we can expect to have new forms of genetic interactions.

Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network

BACKGROUND

The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications.

METHODS

We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN.

RESULTS

Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network sizes and levels of stochastic noise. We found our HRNN method to be superior in terms of accuracy for nonlinear data sets with higher amounts of noise.

CONCLUSIONS

The proposed method identifies time-delayed gene-gene interactions of GRNs. The topology-based advancement of our HRNN worked as expected by more effectively modeling nonlinear data sets. As a non-fully connected network, an added benefit to HRNN was how it helped to find the few genes which regulated the target gene over different time delays.

High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network

BACKGROUND

Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes.

RESULTS

We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain.

CONCLUSIONS

We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed.

Time Delayed Causal Gene Regulatory Network Inference with Hidden Common Causes

Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient but unrealistic assumption of causal sufficiency, i.e. all the relevant factors in the causal network have been observed and there are no unobserved common cause. In principle, in the real world, it is impossible to be certain that all relevant factors or common causes have been observed, because some factors may not have been conceived of, and therefore are impossible to measure. In view of this, we have developed a novel algorithm named HCC-CLINDE to infer an GRN from time series data allowing the presence of hidden common cause(s). We assume there is a sparse causal graph (possibly with cycles) of interest, where the variables are continuous and each causal link has a delay (possibly more than one time step). A small but unknown number of variables are not observed. Each unobserved variable has only observed variables as children and parents, with at least two children, and the children are not linked to each other. Since it is difficult to obtain very long time series, our algorithm is also capable of utilizing multiple short time series, which is more realistic. To our knowledge, our algorithm is far less restrictive than previous works. We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. The results show that our algorithm can adequately recover the true causal GRN and is robust to slight deviation from Gaussian distribution in the error terms. We have also demonstrated the potential of our algorithm on small YEASTRACT subnetworks using limited real data.