Yeast Regulatory Network Research Articles

Network Benchmarking: A Happy Marriage between Systems and Synthetic Biology

In their new Cell paper, Cantone et al. (2009) present exciting results on constructing and utilizing a small synthetic gene regulatory network in yeast that draws from two rapidly developing fields of systems and synthetic biology.

Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture

Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.

Single and multiple input modules in regulatory networks

Interactions between transcription factors and target genes form regulatory networks that control target gene expression. Regulatory networks contain canonical motifs, including the feed forward loop (FFL), single input module (SIM), and multiple input module (MIM) (Fig. 1). A challenge for network analysis is to identify and enumerate the motifs, required to illuminate their biological significance. Although there is consensus about the definition of the FFL, published definitions of the SIM and MIM are unclear and often used inconsistently. Here, we provide, for the first time, a complete and consistent definition of SIM and MIM, and algorithms for enumerating SIMs and MIMs in any network. From the algorithmic point of view, enumeration of SIMs and MIMs is substantially harder than enumerating FFLs. We compare the distributions of motifs in the Yeast regulatory network under different physiological conditions, reported earlier by the landmark paper of Luscombe et al. (Nature 2004, 431: 308-312). Our reanalysis shows major differences in the number of motifs compared with the results of those authors, requiring significant revision of some of their conclusions.

Current approaches to gene regulatory network modelling

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

Evolutionary conservation and over-representation of functionally enriched network patterns in the yeast regulatory network

BackgroundLocalized network patterns are assumed to represent an optimal design principle in different biological networks. A widely used method for identifying functional components in biological networks is looking for network motifs – over-represented network patterns. A number of recent studies have undermined the claim that these over-represented patterns are indicative of optimal design principles and question whether localized network patterns are indeed of functional significance. This paper examines the functional significance of regulatory network patterns via their biological annotation and evolutionary conservation.ResultsWe enumerate all 3-node network patterns in the regulatory network of the yeast S. cerevisiae and examine the biological GO annotation and evolutionary conservation of their constituent genes. Specific 3-node patterns are found to be functionally enriched in different exogenous cellular conditions and thus may represent significant functional components. These functionally enriched patterns are composed mainly of recently evolved genes suggesting that there is no evolutionary pressure acting to preserve such functionally enriched patterns. No correlation is found between over-representation of network patterns and functional enrichment.ConclusionThe findings of functional enrichment support the view that network patterns constitute an important design principle in regulatory networks. However, the wildly used method of over-representation for detecting motifs is not suitable for identifying functionally enriched patterns.

MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals

In recent years, there has been a growing interest in applying Bayesian networks and their extensions to reconstruct regulatory networks from gene expression data. Since the gene expression domain ...

Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification

Sequence polymorphisms affect gene expression by perturbing the complex network of regulatory interactions. We propose a probabilistic method, called Geronemo, which directly aims to identify the mechanism by which genetic changes perturb the regulatory network. Geronemo automatically constructs a set of coregulated genes (modules), whose regulation can involve both sequence variations and expression of regulators. By exploiting the modularity of genetic regulatory systems, Geronemo reveals regulatory relationships that are indiscernible when genes are considered in isolation, allowing the recovery of intricate combinatorial regulation. By incorporating both expression and genotype of regulators, Geronemo captures cases where the effect of sequence variation on its targets is indirect. We applied Geronemo to a data set from the progeny generated by a cross between laboratory BY4716 (BY) and wild RM11-1a (RM) isolates of Saccharomyces cerevisiae. Geronemo produced previously undescribed hypotheses regarding genetic perturbations in the yeast regulatory network, including transcriptional regulation, signal transduction, and chromatin modification. In particular, we find a large number of modules that have both chromosomal characteristics and are regulated by chromatin modification proteins. Indeed, a large fraction of the variance in the expression can be explained by a small number of markers associated with chromatin modifiers. Additional analysis reveals positive selection for sequence evolution of elements in the Swi/Snf chromatin remodeling complex. Overall, our results suggest that a significant part of individual expression variation in yeast arises from evolution of a small number of chromatin structure modifiers.

Computational architecture of the yeast regulatory network

The topology of regulatory networks contains clues to their overall design principles and evolutionary history. We find that while in- and out-degrees of a given protein in the regulatory network are not correlated with each other, there exists a strong negative correlation between the out-degree of a regulatory protein and in-degrees of its targets. Such correlation positions large regulatory modules on the periphery of the network and makes them rather well separated from each other. We also address the question of relative importance of different classes of proteins quantified by the lethality of null-mutants lacking one of them as well as by the level of their evolutionary conservation. It was found that in the yeast regulatory network highly connected proteins are in fact less important than their low-connected counterparts.

Rapid evolution of expression and regulatory divergences after yeast gene duplication.

Although gene duplication is widely believed to be the major source of genetic novelty, how the expression or regulatory network of duplicate genes evolves remains poorly understood. In this article, we propose an additive expression distance between duplicate genes, so that the evolutionary rate of expression divergence after gene duplication can be estimated through phylogenomic analysis. We have analyzed yeast genome sequences, microarrays, and transcriptional regulatory networks, showing a >10-fold increase in the initial rate for both expression and regulatory network evolution after gene duplication but only an approximately 20% rate increase in the early stage for protein sequences. Based on the estimated age distribution of yeast duplicate genes, we roughly estimate that the initial rate of expression divergence shortly after gene duplication is 2.9 x 10(-9) per year, whereas the baseline rate for very ancient gene duplication is 0.14 x 10(-9) per year. Relative expression rate tests suggest that the expression of duplicate genes tends to evolve asymmetrically, that is, the expression of one copy evolves rapidly, whereas the other one largely maintains the ancestral expression profile. Our study highlights the crucial role of early rapid evolution after gene/genome duplication for continuously increasing the complexity of the yeast regulatory network.

Validation and refinement of gene-regulatory pathways on a network of physical interactions

A new automated procedure for prioritizing genetic perturbations was used to evaluate 38 candidate regulatory networks in yeast. Further analysis of four high-priority gene knockout experiments provided new insights into two regulatory pathways

Reconstructing genetic networks in yeast

By combining data from gene expression and DNA-binding experiments, a computational algorithm identifies the genetic regulatory network in yeast.