Embryo development is a complex process orchestrated by hundreds of genes and influenced by multiple environmental factors. We demonstrate the application of simple and effective meta-study and gene network analyses strategies to characterize the co-regulation of the embryo transcriptome in a systems biology framework. A meta-analysis of nine microarray experiments aimed at characterizing the e...

MOTIVATION In a gene regulatory network, genes are typically regulated by transcription factors (TFs). Transcription factor activity (TFA) is more difficult to measure than gene expression levels are. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. Th...

We analyze the structure of the yeast transcriptional regulation network, as revealed by chromatin immunoprecipitation experiments, and characterize the molecular evolution of both its transcriptional regulators and their target (regulated) genes. We test the hypothesis that highly connected genes are more important to the function of gene networks. Three lines of evidence-the rate of molecular...

Cardiac development is anchored on an intricate program of gene regulation and coordination, associated with critical timing and cell–cell interactions. Rather than a single master regulatory process, as originally envisioned to reside in a transcriptional complex or protein signaling cascade, cardiac development is likely regulated by a network of coordinated gene expressions, critically timed...

OBJECTIVE A combination of comparative analysis of gene expression profiles between normal tissue samples and small cell lung cancer (SCLC) samples and network analysis was performed to identify key genes in SCLC. MATERIALS AND METHODS Microarray data set GSE43346 was downloaded from Gene Expression Omnibus (GEO), including 43 normal tissue samples and 23 clinical SCLC samples. Differentially...

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up fo...

Non-steroidal anti-inflammatory drugs (NSAIDs) identified effective in many diseases. One of which is neurodegenerative diseases including Alzheimer disease (AD). In this study gross alteration of gene expression in AD mice by ibuprofen treatment is investigated via Protein-protein interaction network (PPI) analysis. Expression profiling of microarray dataset GSE67306 was retrieved from GEO dat...

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parame...

Cancer cells have abnormal gene expression profiles; however, to what degree these are chaotic or driven by structured gene regulatory networks is often not known. Here we studied a model of Ras-driven invasive tumorigenesis in Drosophila epithelial tissues and combined in vivo genetics with next-generation sequencing and computational modeling to decipher the regulatory logic of tumor cells. S...