Transcriptional regulatory networks and the yeast cell cycle (original) (raw)
Related papers
Nature Genetics, 2008
A major goal of biology is the construction of networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. A network reconstructed by integrating genotypic, TFBS and PPI data is shown to be the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. The network is also shown to elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. Predictions are prospectively validated to provide direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.
Inferring transcriptional regulatory networks from high-throughput data
Bioinformatics, 2007
Motivation: Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various posttranslational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes. Results: In this article, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming (LP) problem which has a globally optimal solution in terms of L 1 norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge, but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes. Availability: The software TRNinfer is available from
Gene networks as a tool to understand transcriptional regulation
Genetics and molecular research : GMR, 2006
Gene regulatory networks, or simply gene networks (GNs), have shown to be a promising approach that the bioinformatics community has been developing for studying regulatory mechanisms in biological systems. GNs are built from the genome-wide high-throughput gene expression data that are often available from DNA microarray experiments. Conceptually, GNs are (un)directed graphs, where the nodes correspond to the genes and a link between a pair of genes denotes a regulatory interaction that occurs at transcriptional level. In the present study, we had two objectives: 1) to develop a framework for GN reconstruction based on a Bayesian network model that captures direct interactions between genes through nonparametric regression with B-splines, and 2) to demonstrate the potential of GNs in the analysis of expression data of a real biological system, the yeast pheromone response pathway. Our framework also included a number of search schemes to learn the network. We present an intuitive n...
Identifying regulatory networks by combinatorial analysis of promoter elements
Nature Genetics, 2001
Several computational methods based on microarray data are currently used to study genome-wide transcriptional regulation. Few studies, however, address the combinatorial nature of transcription, a well-established phenomenon in eukaryotes. Here we describe a new approach using microarray data to uncover novel functional motif combinations in the promoters of Saccharomyces cerevisiae. In addition to identifying novel motif combinations that affect expression patterns during the cell cycle, sporulation and various stress responses, we observed regulatory cross-talk among several of these processes. We have also generated motif-association maps that provide a global view of transcription networks. The maps are highly connected, suggesting that a small number of transcription factors are responsible for a complex set of expression patterns in diverse conditions. This approach may be useful for modeling transcriptional regulatory networks in more complex eukaryotes.
Inferring Gene Regulatory Networks from a Population of Yeast Segregants
Scientific Reports, 2019
Constructing gene regulatory networks is crucial to unraveling the genetic architecture of complex traits and to understanding the mechanisms of diseases. On the basis of gene expression and single nucleotide polymorphism data in the yeast, Saccharomyces cerevisiae, we constructed gene regulatory networks using a two-stage penalized least squares method. A large system of structural equations via optimal prediction of a set of surrogate variables was established at the first stage, followed by consistent selection of regulatory effects at the second stage. Using this approach, we identified subnetworks that were enriched in gene ontology categories, revealing directional regulatory mechanisms controlling these biological pathways. Our mapping and analysis of expression-based quantitative trait loci uncovered a known alteration of gene expression within a biological pathway that results in regulatory effects on companion pathway genes in the phosphocholine network. In addition, we id...
Bioinformatics, 2008
Unraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge. Method: Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks. The algorithm is based on a two-stage constrained matrix decomposition model. The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs. The gene expression profile is modeled as a linear weighted combination of the activity profiles of a set of TFs. The TF activity profiles are deduced from the expression levels of TF target genes, instead directly from TFs themselves. The TF-target gene relationships are derived from ChIP-chip and other TF binding data. The proposed algorithm can not only identify transcriptional modules, but also reveal regulatory programs of which TFs control which target genes in which specific ways (either activating or inhibiting). Results: In comparison with other methods, our algorithm identifies biologically more meaningful transcriptional modules relating to specific TFs. We applied the new algorithm on yeast cell cycle and stress response data. While known transcriptional regulations were confirmed, novel TF-gene interactions were predicted and provide new insights into the regulatory mechanisms of the cell. Contact:
Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data
Current Genomics, 2015
Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.
Briefings in bioinformatics, 2012
The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such 'reverse-engineering' methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)^target gene expression levels and estimating TFs' activity, and further explore cooperative models. Finally we confirm that the detailed transcription mechanism is overly-complex for expression data alone to reveal, nevertheless, future network reconstruction studies could benefit from the incorporation of context-specific information, the modeling of multiple layers of regulation (e.g. micro-RNA), or the development of approaches for context-dependent analysis, to uncover the mechanisms of gene regulation.
Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae
BMC Bioinformatics, 2006
Background: Gene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network.