MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data

A new computational tool is presented that allows the integration of high-throughput experimental results with the probabilistic modeling of previously obtained information about cellular systems. The tool (MetaReg) is demonstrated on the leucine biosynthesis system in S.cerevisiae

Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization

Understanding the mechanisms of gene transcriptional regulation through analysis of high-throughput postgenomic data is one of the central problems of computational systems biology. Various approaches have been proposed, but most of them fail to address at least one of the following objectives: (1) allow for the fact that transcription factors are potentially subject to posttranscriptional regulation; (2) allow for the fact that transcription factors cooperate as a functional complex in regulating gene expression, and (3) provide a model and a learning algorithm with manageable computational complexity. The objective of the present study is to propose and test a method that addresses these three issues. The model we employ is a mixture of factor analyzers, in which the latent variables correspond to different transcription factors, grouped into complexes or modules. We pursue inference in a Bayesian framework, using the Variational Bayesian Expectation Maximization (VBEM) algorithm for approximate inference of the posterior distributions of the model parameters, and estimation of a lower bound on the marginal likelihood for model selection. We have evaluated the performance of the proposed method on three criteria: activity profile reconstruction, gene clustering, and network inference

Reverse engieering gene regulatory networks: Elucidation of trancriptome organization, gene function and gene regulation in mammalian systems

The main aim of this thesis was to infer mammalian gene regulatory networks from tens of thousands gene expression profiles via a new "reverse-engineering" approach. Human arid Mouse gene regulatory networks were both inferred and the results collected in a database that represents part of the non-written material that will support. the thesis (http: //rretview. tigem. it). Each gene regulatory network consists of a set of gene pairs (connections) and a score based on Mutual Information, which states their statistical dependence. The inferred connections are organized into a network that allows exploration of the global features of gene regulation in a mammalian cell. We collected a massive and heterogeneous dataset of 22,255 gene expression profiles from a variety of human samples and experimental conditions. We developed a new mutual-information (MI) reverse-engineering approach able to quantify the extent to which the mRNA levels of two genes are related to each other across such a complex dataset. The resulting network consists of 4,817,629 connections among 22,255 transcripts. The inferred connections were compared against known protein-protein and other regulatory interactions to assess their biological significance. We experimentally identified a subset of predicted protein-protein interactions not previously described in the literature. We discovered regulatory modules within the network, consisting of genes strongly connected to each other, which carry out specific biological functions. We found that these connected genes tend to be in physical proximity at the chromatin level in the nucleus. We show that the network can be used to predict the biological function and subcellular localization of a protein, and to elucidate the function of a disease-gene. Specifically we discovered that the gene granulin precursor GRN), whose mutations cause frontotemporal lobar degeneration, is involved in lysosome function. We have developed an online tool (http://netview.tigem.it) for querying and exploring the gene regulatory network

Data sources and computational approaches for generating models of gene regulatory networks

[No abstract available

Altered developmental programming of the mouse mammary gland in female offspring following perinatal dietary exposures : a systems-biology perspective.

Mishaps in prenatal development can influence mammary gland development and, ultimately, affect susceptibility to factors that cause breast cancer. This research was based on the underlying hypothesis that maternal dietary composition during pregnancy can alter developmental (fetal) programming of the mammary gland. We used a computational systems-biology approach and Bayesian-based stochastic search variable selection algorithm (SSVS) to identify differentially expressed genes and biological themes and pathways. Postnatal growth trajectories and gene expression in the mammary gland at 10-weeks of age in female mice were investigated following different maternal diet exposures during prenatal-lactational-early-juvenile development. This correlated a decrease in expression of energy pathways with a reciprocal increase in cytokine and inflammatory-signaling pathways. These findings suggest maternal dietary fat exposure significantly influences postnatal growth trajectories, metabolic programming, and signaling networks in the mammary gland of female offspring. In addition, the adipocytokine pathway may be a sensitive trigger to dietary changes and may influence or enhance activation of an immune response, a key event in cancer development

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Inference of gene regulation from expression datasets

The development of high throughput techniques and the accumulation of large scale gene expression data provide researchers great opportunities to more efficiently solve important but complex biological problems, such as reconstruction of gene regulatory networks and identification of miRNA-target interactions. In the past decade, many algorithms have been developed to address these problems. However, prediction and simulation of gene expression data have not yet received as much attention. In this study, we present a model based on stepwise multiple linear regression (SMLR) that can be applied for prediction and simulation of gene expression, as well as reconstruction of gene regulatory networks by analysis of time-series gene expression data, and we present its application in analysis of paired miRNA-mRNA expression data.Ph.D., Biomedical Engineering -- Drexel University, 201