Stochastic S-system modeling of gene regulatory network

Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomycescerevisiae yeast, and (2) the SOS DNA repair network in Escherichiacoli.     

How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?

Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on pieces of experimental genetic perturbation evidence from manually reading primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.     

Pattern identification and classification in gene expression data using an autoassociative neural network model

The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.     

A Markov-blanket-based model for gene regulatory network inference

An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.     

Web Tools for Rice Transcriptome Analyses

Gene expression databases provide profiling data for the expression of thousands of genes to researchers worldwide. Oligonucleotide microarray technology is a useful tool that has been employed to produce gene expression profiles in most species. In rice, there are five genome-wide DNA microarray platforms: NSF 45K, BGI/Yale 60K, Affymetrix, Agilent Rice 44K, and NimbleGen 390K. Presently, more than 1,700 hybridizations of microarray gene expression data are available from public microarray depositing databases such as NCBI gene expression omnibus and Arrayexpress at EBI. More processing or reformatting of public gene expression data is required for further applications or analyses. Web-based databases for expression meta-analyses are useful for guiding researchers in designing relevant research schemes. In this review, we summarize various databases for expression meta-analyses of rice genes and web tools for further applications, such as the development of co-expression network or functional gene network.     

最短路径法在水稻ABA 和环境胁迫条件下基因应答网络研究中的应用

目前微阵列数据分析方法都基于具有相似表达模式的基因可能具有相近的生物学功能这一假设, 而实际上参与同一生物学功能的基因, 在表达时间和空间上是有关联的, 而并非表现为相似模式。利用水稻cDNA微阵列, 对水稻在ABA及干旱、寒冷和高盐胁迫条件下的基因表达进行了研究。选取环境胁迫和ABA应答的相关基因, 采用最短路径法(shortest path), 利用自行编制的计算软件, 在表达模式不直接相关的基因之间构建最短路径。研究表明, 通过分析这些基因的表达数据, 可以发现它们在功能上的关联性, 并对未知基因的功能预测进行了探索, 为构建水稻在ABA和环境胁迫条件下的分子应答网络奠定了基础。     

Analysis of gap gene regulation in a 3D organism-scale model of the Drosophila melanogaster embryo

The axial bodyplan of Drosophila melanogaster is determined during a process called morphogenesis. Shortly after fertilization, maternal bicoid mRNA is translated into Bicoid (Bcd). This protein establishes a spatially graded morphogen distribution along the anterior-posterior (AP) axis of the embryo. Bcd initiates AP axis determination by triggering expression of gap genes that subsequently regulate each other's expression to form a precisely controlled spatial distribution of gene products. Reaction-diffusion models of gap gene expression on a 1D domain have previously been used to infer complex genetic regulatory network (GRN) interactions by optimizing model parameters with respect to 1D gap gene expression data. Here we construct a finite element reaction-diffusion model with a realistic 3D geometry fit to full 3D gap gene expression data. Though gap gene products exhibit dorsal-ventral asymmetries, we discover that previously inferred gap GRNs yield qualitatively correct AP distributions on the 3D domain only when DV-symmetric initial conditions are employed. Model patterning loses qualitative agreement with experimental data when we incorporate a realistic DV-asymmetric distribution of Bcd. Further, we find that geometry alone is insufficient to account for DV-asymmetries in the final gap gene distribution. Additional GRN optimization confirms that the 3D model remains sensitive to GRN parameter perturbations. Finally, we find that incorporation of 3D data in simulation and optimization does not constrain the search space or improve optimization results.     

Upregulation of DNA repair genes in active cirrhosis associated with hepatocellular carcinoma

Phenotypic changes in injured livers involve complex network of genes whose interplays may lead to fibrosis and cirrhosis, a major risk of hepatocellular carcinoma. Gene expression profiles in fibrotic livers were analyzed by using cDNA microarray, hierarchical clustering and gene ontology. Analyses of a major cluster of upregulated genes in cirrhosis identified a new set of genes involved in DNA repair and damage. The upregulation of DNA repair genes was confirmed by real-time quantitative polymerase chain reaction and associated with necroinflammatory activity (P<0.001). Increased DNA repair activity in cirrhosis with inflammatory activity may reflect increased DNA damages as a consequence of chronic liver injury.     

Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling

MOTIVATION: Recent advances in DNA microarray technologies have made it possible to measure the expression levels of thousands of genes simultaneously under different conditions. The data obtained by microarray analyses are called expression profile data. One type of important information underlying the expression profile data is the 'genetic network,' that is, the regulatory network among genes. Graphical Gaussian Modeling (GGM) is a widely utilized method to infer or test relationships among a plural of variables. RESULTS: In this study, we developed a method combining the cluster analysis with GGM for the inference of the genetic network from the expression profile data. The expression profile data of 2467 Saccharomyces cerevisiae genes measured under 79 different conditions (Eisen et al., PROC: Natl Acad. Sci. USA, 95, 14683-14868, 1998) were used for this study. At first, the 2467 genes were classified into 34 clusters by a cluster analysis, as a preprocessing for GGM. Then, the expression levels of the genes in each cluster were averaged for each condition. The averaged expression profile data of 34 clusters were subjected to GGM, and a partial correlation coefficient matrix was obtained as a model of the genetic network of S. cerevisiae. The accuracy of the inferred network was examined by the agreement of our results with the cumulative results of experimental studies.     

AtCAST, a tool for exploring gene expression similarities among DNA microarray experiments using networks

The comparison of gene expression profiles among DNA microarray experiments enables the identification of unknown relationships among experiments to uncover the underlying biological relationships. Despite the ongoing accumulation of data in public databases, detecting biological correlations among gene expression profiles from multiple laboratories on a large scale remains difficult. Here, we applied a module (sets of genes working in the same biological action)-based correlation analysis in combination with a network analysis to Arabidopsis data and developed a 'module-based correlation network' (MCN) which represents relationships among DNA microarray experiments on a large scale. We developed a Web-based data analysis tool, 'AtCAST' (Arabidopsis thaliana: DNA Microarray Correlation Analysis Tool), which enables browsing of an MCN or mining of users' microarray data by mapping the data into an MCN. AtCAST can help researchers to find novel connections among DNA microarray experiments, which in turn will help to build new hypotheses to uncover physiological mechanisms or gene functions in Arabidopsis.     

Spearman Correlation Identifies Statistically Significant Gene Expression Clusters in Spinal Cord Development and Injury

An important problem in the analysis of large-scale gene expression data is the validation of gene expression clusters. By examining the temporal expression patterns of 74 genes expressed in rat spinal cord under three different experimental conditions, we have found evidence that some genes cluster together under multiple conditions. Using RT-PCR data from spinal cord development and two sets of microarray data from spinal injury, we applied Spearman correlation to identify clusters and to assign P values to pairs of genes with highly similar temporal expression patterns. We found that 15% of genes occurred in statistically significant pairs in all three experimental conditions, providing both statistical and experimental support for the idea that genes that cluster together are co-regulated. In addition, we demonstrated that DNA microarray and RT-PCR data are comparable, and can be combined to confirm gene expression relationships.     

Bayesian hierarchical modeling for time course microarray experiments

Time course microarray experiments designed to characterize the dynamic regulation of gene expression in biological systems are becoming increasingly important. One critical issue that arises when examining time course microarray data is the identification of genes that show different temporal expression patterns among biological conditions. Here we propose a Bayesian hierarchical model to incorporate important experimental factors and to account for correlated gene expression measurements over time and over different genes. A new gene selection algorithm is also presented with the model to simultaneously identify genes that show changes in expression among biological conditions, in response to time and other experimental factors of interest. The algorithm performs well in terms of the false positive and false negative rates in simulation studies. The methodology is applied to a mouse model time course experiment to correlate temporal changes in azoxymethane-induced gene expression profiles with colorectal cancer susceptibility.     

Time ordering of gene coexpression

Temporal microarray gene expression profiles allow characterization of gene function through time dynamics of gene coexpression within the same genetic pathway. In this paper, we define and estimate a global time shift characteristic for each gene via least squares, inferred from pairwise curve alignments. These time shift characteristics of individual genes reflect a time ordering that is derived from ob- served temporal gene expression profiles. Once these time shift characteristics are obtained for each gene, they can be entered into further analyses, such as clustering. We illustrate the proposed methodology using Drosophila embryonic development and yeast cell-cycle gene expression profiles, as well as simulations. Feasibility is demonstrated through the successful recovery of time ordering. Estimated time shifts for Drosophila maternal and zygotic genes provide excellent discrimination between these two categories and confirm known genetic pathways through the time order of gene expression. The application to yeast cell-cycle data establishes a natural time order of genes that is in line with cell-cycle phases. The method does not require periodicity of gene expression profiles. Asymptotic justifications are also provided.