Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers.

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling.

A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets.

Unlabelled: We propose a likelihood ratio test (LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrap-based approach. LRT is shown to be significantly faster and statistically powerful even within non-Normal distributions.

Collocation-based sparse estimation for constructing dynamic gene networks.

One of the open problems in systems biology is to infer dynamic gene networks describing the underlying biological process with mathematical, statistical and computational methods. The first-order difference equation-based models such as dynamic Bayesian networks and vector autoregressive models were used to infer time-lagged relationships between genes from time-series microarray data. However, two primary problems greatly reduce the effectiveness of current approaches.

Gene regulatory network clustering for graph layout based on microarray gene expression data.

We propose a statistical model realizing simultaneous estimation of gene regulatory network and gene module identification from time series gene expression data from microarray experiments. Under the assumption that genes in the same module are densely connected, the proposed method detects gene modules based on the variational Bayesian technique. The model can also incorporate existing biological prior knowledge such as protein subcellular localization.

Comparison of gene expression profiles produced by CAGE, illumina microarray and real time RT-PCR.

Several technologies are currently used for gene expression profiling, such as Real Time RT-PCR, microarray and CAGE (Cap Analysis of Gene Expression). CAGE is a recently developed method for constructing transcriptome maps and it has been successfully applied to analyzing gene expressions in diverse biological studies. The principle of CAGE has been developed to address specific issues such as determination of transcriptional starting sites, the study of promoter regions and identification of new transcripts.

An efficient biological pathway layout algorithm combining grid-layout and spring embedder for complicated cellular location information.

Background: Graph drawing is one of the important techniques for understanding biological regulations in a cell or among cells at the pathway level. Among many available layout algorithms, the spring embedder algorithm is widely used not only for pathway drawing but also for circuit placement and www visualization and so on because of the harmonized appearance of its results. For pathway drawing, location information is essential for its comprehension.

Gene set-based module discovery decodes cis-regulatory codes governing diverse gene expression across human multiple tissues.

Decoding transcriptional programs governing transcriptomic diversity across human multiple tissues is a major challenge in bioinformatics. To address this problem, a number of computational methods have focused on cis-regulatory codes driving overexpression or underexpression in a single tissue as compared to others. On the other hand, we recently proposed a different approach to mine cis-regulatory codes: starting from gene sets sharing common cis-regulatory motifs, the method screens for expression modules based on expression coherence.

Rationale of pelvic autonomic nerve preservation in rectal cancer surgery based on immunohistochemical study.

Purpose: Previous studies revealed that the incidence of cancer cell involvement along the pelvic autonomic nerves ranged from 4 to 14%. However, patients' profiles and methodologies differed among the studies. This study was conducted to clarify the incidence of cancer cell involvement in and around the pelvic autonomic nerves immunohistochemically.

DA 1.0: parameter estimation of biological pathways using data assimilation approach.

Summary: Data assimilation (DA) is a computational approach that estimates unknown parameters in a pathway model using time-course information. Particle filtering, the underlying method used, is a well-established statistical method that approximates the joint posterior distributions of parameters by using sequentially generated Monte Carlo samples. In this article, we report the release of Java-based software (DA 1.

Inferring contagion in regulatory networks.

Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However, only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.

International network of cancer genome projects.

The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the genomic, epigenomic and transcriptomic levels will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define clinically relevant subtypes for prognosis and therapeutic management, and enable the development of new cancer therapies.

Time-dependent structural transformation analysis to high-level Petri net model with active state transition diagram.

Background: With an accumulation of in silico data obtained by simulating large-scale biological networks, a new interest of research is emerging for elucidating how living organism functions over time in cells. Investigating the dynamic features of current computational models promises a deeper understanding of complex cellular processes. This leads us to develop a method that utilizes structural properties of the model over all simulation time steps.

The systems approach to the prespore-specific activation of sigma factor SigF in Bacillus subtilis.

The prespore-specific activation of sigma factor SigF (sigma(F)) in Bacillus subtilis has been explained mainly by two factors, i.e., the transient genetic asymmetry and the volume difference between the mother cell and the prespore.

A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.

Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks.

A state space representation of VAR models with sparse learning for dynamic gene networks.

We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm.

Inferring dynamic gene networks under varying conditions for transcriptomic network comparison.

Motivation: Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse engineer a cellular system, called gene network, from time series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time series datasets under varying conditions to find critical differences between these networks.

Quality control and reproducibility in DNA microarray experiments.

Biological experiments are usually set up in technical replicates (duplicates or triplicates) in order to ensure reproducibility and, to assess any significant error introduced during the experimental process. The first step in biological data analysis is to check the technical replicates and to confirm that the error of measure is small enough to be of no concern. However, little attention has been paid to this part of analysis.

Brachial-brachial autogenous arteriovenous fistula in a dialysis patient with Staphylococcus aureus bacteremia.

As the number of patients on hemodialysis increases, there will also be an increase in the number of patients with inadequate superficial veins for the creation of an autogenous arteriovenous fistula (AVF). In those patients, medical devices such as vascular prostheses or tunneled-cuffed catheters are necessary to maintain dialysis access. However, these devices are frequently associated with bacterial infection.

When should we intervene to control the 2009 influenza A(H1N1) pandemic?

We simulated the early phase of the 2009 influenza A(H1N1) pandemic and assessed the effectiveness of public health interventions in Japan. We show that the detection rate of border quarantine was low and the timing of the intervention was the most important factor involved in the control of the pandemic, with the maximum reduction in daily cases obtained after interventions started on day 6 or 11. Early interventions were not always effective.

On determining firing delay time of transitions for Petri net based signaling pathways by introducing stochastic decision rules.

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases.

Cell Illustrator 4.0: a computational platform for systems biology.

Cell Illustrator is a software platform for Systems Biology that uses the concept of Petri net for modeling and simulating biopathways. It is intended for biological scientists working at bench. The latest version of Cell Illustrator 4.

The impact of measurement errors in the identification of regulatory networks.

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.

Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks.

Atmospheric chemistry of BrO radicals: kinetics of the reaction with C2H5O2 radicals at 233-333 K.

Cavity ring-down spectroscopy was used to study the title reaction in 50-200 Torr of O2 diluent at 233-333 K. There was no discernible effect of total pressure, and a rate constant of k(BrO + C2H5O2) = (3.8 +/- 1.

Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis.

DNA microarrays have become a powerful tool to describe gene expression profiles associated with different cellular states, various phenotypes and responses to drugs and other extra- or intra-cellular perturbations. In order to cluster co-expressed genes and/or to construct regulatory networks, definition of distance or similarity between measured gene expression data is usually required, the most common choices being Pearson's and Spearman's correlations. Here, we evaluate these two methods and also compare them with a third one, namely Hoeffding's D measure, which is used to infer nonlinear and non-monotonic associations, i.