Bayesian variable selection and data integration for biological regulatory networks

A substantial focus of research in molecular biology are gene regulatory networks: the set of transcription factors and target genes which control the involvement of different biological processes in living cells. Previous statistical approaches for identifying gene regulatory networks have used gene expression data, ChIP binding data or promoter sequence data, but each of these resources provides only partial information. We present a Bayesian hierarchical model that integrates all three data types in a principled variable selection framework. The gene expression data are modeled as a function of the unknown gene regulatory network which has an informed prior distribution based upon both ChIP binding and promoter sequence data. We also present a variable weighting methodology for the principled balancing of multiple sources of prior information. We apply our procedure to the discovery of gene regulatory relationships in Saccharomyces cerevisiae (Yeast) for which we can use several external sources of information to validate our results. Our inferred relationships show greater biological relevance on the external validation measures than previous data integration methods. Our model also estimates synergistic and antagonistic interactions between transcription factors, many of which are validated by previous studies. We also evaluate the results from our procedure for the weighting for multiple sources of prior information. Finally, we discuss our methodology in the context of previous approaches to data integration and Bayesian variable selection.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS130 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Wisdom of crowds for robust gene network inference

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.National Institutes of Health (U.S.)National Centers for Biomedical Computing (U.S.) (Roadmap Initiative (U54CA121852))Howard Hughes Medical InstituteNational Institutes of Health (U.S.) (Director's Pioneer Award DPI OD003644)Swiss National Science Foundation (Fellowship

How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

Characterizing regulatory path motifs in integrated networks using perturbational data

Pathicular – a Cytoscape plugin for analysing cellular responses to transcription factor perturbations is presente

Clustering of genes into regulons using integrated modeling-COGRIM

We present a Bayesian hierarchical model and Gibbs Sampling implementation that integrates gene expression, ChIP binding, and transcription factor motif data in a principled and robust fashion. COGRIM was applied to both unicellular and mammalian organisms under different scenarios of available data. In these applications, we demonstrate the ability to predict gene-transcription factor interactions with reduced numbers of false-positive findings and to make predictions beyond what is obtained when single types of data are considered

Practical Approaches to Biological Network Discovery

This dissertation addresses a current outstanding problem in the field of systems biology, which is to identify the structure of a transcriptional network from high-throughput experimental data. Understanding of the connectivity of a transcriptional network is an important piece of the puzzle, which relates the genotype of an organism to its phenotypes. An overwhelming number of computational approaches have been proposed to perform integrative analyses on large collections of high-throughput gene expression datasets to infer the structure of transcriptional networks. I put forth a methodology by which these tools can be evaluated and compared against one another to better understand their strengths and weaknesses. Next I undertake the task of utilizing high-throughput datasets to learn new and interesting network biology in the pathogenic fungus Cryptococcus neoformans. Finally I propose a novel computational method for mapping out transcriptional networks that unifies two orthogonal strategies for network inference. I apply this method to map out the transcriptional network of Saccharomyces cerevisiae and demonstrate how network inference results can complement chromatin immunoprecipitation: ChIP) experiments, which directly probe the binding events of transcriptional regulators. Collectively, my contributions improve both the accessibility and practicality of network inference methods

Robustness of transcriptional regulatory program influences gene expression variability

<p>Abstract</p> <p>Background</p> <p>Most genes are not affected when any transcription factor (TF) is knocked out, indicating that they have robust transcriptional regulatory program. Yet the mechanism underlying robust transcriptional regulatory program is less clear.</p> <p>Results</p> <p>Here, we studied the cause and effect of robust transcriptional regulatory program. We found that cooperative TFs in the robust transcriptional regulatory program regulate their common target genes in an activity-redundant fashion, and they are able to compensate for each other's loss. As a result, their target genes are insensitive to their single perturbation. We next revealed that the degree of robustness of transcriptional regulatory program influences gene expression variability. Genes with fragile (unrobust) transcriptional regulatory program under normal growth condition could be readily reprogrammed to significantly modulate gene expression upon changing conditions. They also have high evolutionary rates of gene expression. We further showed that the fragile transcriptional regulatory program is a major source of expression variability.</p> <p>Conclusion</p> <p>We showed that activity-redundant TFs guarantee the robustness of transcriptional regulatory programs, and the fragility of transcriptional regulatory program plays a major role in gene expression variability. These findings reveal the mechanisms underlying robust transcription and expression variability.</p