An estimation method for inference of gene regulatory net-work using Bayesian network with uniting of partial problems

Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information

Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.

Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

Inferring gene regulatory networks from expression data is a central problem in systems biology.

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy

Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the "large p, small n" problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.

An efficient data assimilation schema for restoration and extension of gene regulatory networks using time-course observation data

Gene regulatory networks (GRNs) play a central role in sustaining complex biological systems in cells. Although we can construct GRNs by integrating biological interactions that have been recorded in literature, they can include suspicious data and a lack of information. Therefore, there has been an urgent need for an approach by which the validity of constructed networks can be evaluated; simulation-based methods have been applied in which biological observational data are assimilated. However, these methods apply nonlinear models that require high computational power to evaluate even one network consisting of only several genes. Therefore, to explore candidate networks whose simulation models can better predict the data by modifying and extending literature-based GRNs, an efficient and versatile method is urgently required. We applied a combinatorial transcription model, which can represent combinatorial regulatory effects of genes, as a biological simulation model, to reproduce the dynamic behavior of gene expressions within a state space model. Under the model, we applied the unscented Kalman filter to obtain the approximate posterior probability distribution of the hidden state to efficiently estimate parameter values maximizing prediction ability for observational data by the EM-algorithm. Utilizing the method, we propose a novel algorithm to modify GRNs reported in the literature so that their simulation models become consistent with observed data. The effectiveness of our approach was validated through comparison analysis to the previous methods using synthetic networks. Finally, as an application example, a Kyoto Encyclopedia of Genes and Genomes (KEGG)-based yeast cell cycle network was extended with additional candidate genes to better predict the real mRNA expressions data using the proposed method.

Inference of TFRNs (2)

This chapter introduces to a study aiming at comprehensively understanding the transcription factor regulatory networks (TFRNs) that govern the process of a cell differentiation. Here we focus on the adipocyte differentiation. For the cell differentiation, we inferred its TFRN using the Bayesian network (BN) method. BNs have been widely used to estimate TFRNs. Many BN methods have been developed to estimate networks from TF expression data. However, BN-based methods require huge computational time to estimate large-scale networks. This chapter introduces to a BN-based deterministic method with reduced computational time. This approach generates all the combinational subnetworks of three TFs, estimates networks of the subnetworks by BN, and unites the networks into a single large network. This method decreases the search space of predicting TFRNs without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. This chapter also presents a massively parallel implementation for the BN-based inference of TFRNs. The system enables us to estimate large-scale (>10,000 transcripts) multiple TFRNs from expression profiles of various tissues and conditions. The comparison among estimated TFRNs of adipose tissues with stimulus induction is conducted. The various regulations to Ucp1 (uncoupled protein 1) in those networks may reflect different responses of the tissues under the stimulus induction.

LegumeGRN: a gene regulatory network prediction server for functional and comparative studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.